Reduced YTHDF2 inhibits PD-L1 expression by stabilizing m6A-containing SPOP mRNA in colorectal cancer

Reduced YTHDF2 inhibits PD-L1 expression by stabilizing m6A-containing SPOP mRNA in colorectal cancer | 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 Reduced YTHDF2 inhibits PD-L1 expression by stabilizing m 6 A-containing SPOP mRNA in colorectal cancer Jiaying Shen, Xian Xu, Hao Chen, Rongjie Zhao, Jiansheng Xie, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7735800/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Cell Death & Disease → Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) is one of the most frequently diagnosed malignant tumors. However, clear evidence explaining the regulatory mechanisms of programmed death ligand 1 (PD-L1) in CRC has been limited. To illustrate the function of YTH N 6 -methyladenosine (m 6 A) RNA binding protein F2 (YTHDF2), we conducted a comprehensive evaluation of the expression profiling datasets from online databases and clinical samples. A subcutaneous immunodeficient mouse model was utilized to investigate the impact of YTHDF2 on CRC. Western blots, flow cytometry, PD-1/PD-L1 binding and cell killing assays were employed to detect the relationship between YTHDF2 and PD-L1. We utilized RNA sequencing, along with methylated RNA immunoprecipitation (MeRIP) and RNA binding protein immunoprecipitation (RIP) sequencing to analyze mRNA expression, m 6 A methylation level, and target transcripts of YTHDF2. The m 6 A methylation locations of mRNAs were verified using sequence-based RNA adenosine methylation site predictor (SRAMP), MeRIP-qRT-PCR, RIP-qRT-PCR, and a dual-luciferase reporter system. It was found that YTHDF2 was upregulated in CRC tissues, and patients with higher YTHDF2 expression had a worse prognosis. The in vivo model illustrated that YTHDF2 promoted CRC growth, while in vitro experiments showed that the inhibition of YTHDF2 expression did not affect cell proliferation, migration or invasion. Mechanistically, interference with YTHDF2 reduced PD-L1 expression and the binding ability between PD-1 and PD-L1. The use of RNA-seq, MeRIP-seq, RIP-seq, and bioinformatics tools confirmed speckle type BTB/POZ protein (SPOP) mRNA as a YTHDF2 target and validated its m 6 A methylation sites. After YTHDF2 knockdown, SPOP mRNA stability increased, causing an increase in SPOP expression and a decrease in PD-L1 expression. This study demonstrated that YTHDF2 might upregulate PD-L1 expression by destabilizing m 6 A-containing SPOP mRNA and promote CRC development. The biological effect of the YTHDF2-SPOP-PD-L1 axis presented a promising target for CRC treatment and provided an approach to enhance the efficacy of anti-PD-1/PD-L1 therapy. Biological sciences/Cancer/Cancer microenvironment Biological sciences/Immunology/Immune evasion colorectal cancer m6A YTHDF2 PD-1 PD-L1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Worldwide, CRC is a common malignancy of the digestive tract, with an incidence and mortality ranking third among all malignant tumors 1 , and similar burden can be seen in China 2 . For patients who receive surgery, 30 ~ 40% experience recurrence and metastasis within 5 years 3 . Although chemotherapy, molecular targeted therapy and immunotherapy have shown clinical success, advanced patients are still confronted with poor prognoses and low survival rates 4 . Research progress has revealed some of the mechanisms underlying the development and occurrence of CRC, such as the roles of tumor microenvironment 5 . PD-L1, coded by CD274 , is a crucial immune regulator that plays an essential biological role in subverting the anticancer activity of T cells 6 . Anti-PD-1/PD-L1 agents act on T cells and relieve immune suppression, becoming one of the most promising checkpoint inhibitors. Regarding CRC, anti-PD-1 agents elicit significant clinical responses in microsatellite instability-high (MSI-H)/deficient mismatch repair (dMMR) patients 7 . However, anti-PD-1 agents are unable to overcome primary drug resistance in approximately 30% of CRC patients, and they are largely ineffective in microsatellite stability (MSS) and mismatch repair-proficient (pMMR) CRC patients 8 . Thus, it is especially meaningful to explore the mechanism and effect of PD-1/PD-L1 response in CRC. With advancements in technology, various forms of RNA modifications have been discovered. As the most prevalent RNA modification, m 6 A has been extensively studied for its vital role in various biological processes, such as RNA processing, translation and degradation 9 . Moreover, m 6 A regulators have been reported to participate in immune responses 10 . Various studies have highlighted the promising potential of m 6 A regulators in regulating macrophages 11 , dendritic cells 12 , myeloid-derived suppressor cells 13 , natural killer cells 14 and T cells 15 . m 6 A regulators exhibiting dynamic regulatory effects are classified as writers, erasers and readers. YTHDF2 is a reader that primarily recognizes and binds to m 6 A-modified sites on mRNAs 16 . Recent studies have shown that YTHDF2 primarily participates in mRNA decay 17 , but can also enhance mRNA translation in response to heat shock stress conditions 18 . Moreover, YTHDF2 has been found involved in immune responses and therapeutic efficacy improvements 19 , 20 . However, the impact of YTHDF2 on CRC remains unclear. In our study, we proposed a regulatory relation between YTHDF2 and PD-L1 expression and explored the role of YTHDF2 in CRC progression, providing potential molecular markers and therapeutic targets for CRC patients. 2. Materials and Methods 2.1. Bioinformatics Analysis The RNA-seq data were obtained from The Cancer Genome Atlas (TCGA, 620 CRC tissues and 51 normal tissues) data portal ( https://portal.gdc.cancer.gov/ ) and Genotype-Tissue Expression (GTEx, 779 normal tissues) database ( https://www.gtexportal.org/home/index.html ). Prognosis and expression information were provided by GSE17537 ( n = 55), GSE31595 ( n = 37), and GSE38832 ( n = 122). We extracted pathological and molecular characteristics as well as expression data from GSE39582 ( n = 585), and immunotherapy response information from IMvigor210 ( n = 348) 21 . We collected SPOP mutation data from Pan-Cancer Atlas project 22 . Analyses were performed in R software (version 4.2.2). ggplot2 and pheatmap were utilized to depict the expression pattern of 19 m 6 A regulators 23 , 24 , 25 and multi-gene correlation 26 , 27 . The univariate COX regression model was adopted to calculate the hazard ratios (HR) for m 6 A regulators. Log-rank test was performed for survival analysis. Differentially expressed genes were identified using limma package. The gene set of “c5.all.v7.1.symbols” was downloaded from gene-set enrichment analysis (GSEA) database ( https://www.gsea-msigdb.org ) for gene ontology (GO) and GSEA enrichment analysis using fgsea package. Consensus molecular subtypes (CMS) classification was defined using CMSclassifier package 28 . The statistical difference of two groups was compared through Wilcox test, and the significance difference of three groups was tested with Kruskal-Wallis test. Spearman’s correlation analysis was used to describe the correlation between quantitative variables without a normal distribution. 2.2. Human sample collection 73 adjacent normal and 98 CRC tissues were collected from National Human Genetic Resources Sharing Service Platform (Shanghai, P. R. China). Patients were not subjected to any preoperative anti-cancer treatment. The diagnoses of CRC were all histologically confirmed and all participants were provided informed consent for obtaining the study specimens. Ethical approval was obtained from the Ethics Committee of Shanghai Outdo Biotech Company (YB M-05-02). 2.3. Histology and immunohistochemical staining Formalin-fixed (G1101, Servicebio, Hubei, P. R. China) and paraffin-embedded samples were sectioned at 5 µm and stained with hematoxylin and eosin (G1005, Servicebio, Hubei, P. R. China). Immunohistochemistry (IHC) was performed on samples using YTHDF2 antibody (ab246514, Abcam, Cambridge, MA, UK), PD-L1 antibody (13684, CST, BOS, USA) or CD8 (ab235951, Abcam, Cambridge, MA, UK). IHC score was generated by assigning sub-scores for the distribution (0 ~ 4) and intensity (0 ~ 3), which were multiplied together to yield the immunoreactivity score. The percentage positivity was scored as 0 (no staining), 1 (1 ~ 25%), 2 (26 ~ 50%), 3 (51 ~ 75%), or 4 (76 ~ 100%). The staining intensity was scored as 0 (no staining), 1 (weakly stained), 2 (moderately stained), or 3 (strongly stained). The total IHC score was the product of the above factors, which ranged from 0 to 12. The CD8 + T cell infiltration in tumor tissues was evaluated by staining with CD8, and quantification was performed using ImageJ software. 2.4. Cell culture Human CRC cell lines (RKO, LoVo and HCT-116) and HEK-293T were utilized in this study. Cell lines were maintained in RPMI-1640 (MA0215, Meilunbio, Liaoning, P. R. China) or DMEM medium (MA0212, Meilunbio, Liaoning, P. R. China). All mediums were supplemented with 10% fetal bovine serum (FBS, NFBS-2500, Noverse™, SN, Germany) and 1% penicillin/streptomycin (MA0110, Meilunbio, Liaoning, P. R. China). All cells were cultured at 37°C in a humidified atmosphere containing 5% CO 2 . Cells in the logarithmic growth phase were used in the subsequent experiments. 2.5. Lentiviral production and transduction All short hairpin RNA (shRNA) targeting sequences were cloned into hU6-MCS-CBh-gcGFP-IRES-puromycin vector (Genechem, P R. China). To produce lentivirus, HEK-293T cells were transfected with the aforementioned shRNA vectors and packaging plasmids pLP1, pLP2 and pLP/VSVG at a 4:3:3:2 ratio. Transfection was performed using polyetherimide (PEI) reagent (#24765, Polysciences, PA, USA). The viral supernatant was collected 48 h after transfection, filtered with a 0.45 µm filter and used to infect RKO cells with polybrene (H8761, Solarbio, Beijing, P. R. China). 1 ng/µL puromycin (MB2005, Meilunbio, Liaoning, P. R. China) was applied for following selection. shRNA targeting sequences were listed in Table S1 . 2.6. Primary cell culture Peripheral blood mononuclear cells (PBMCs) were isolated through density gradient centrifugation of fresh blood from healthy volunteers, using Human peripheral lymphocyte separation medium (MB0911, Meilunbio, Liaoning, P. R. China). Written informed consent was obtained from each volunteer. PBMC single-cell suspensions were cultured in RPMI-1640 medium supplemented with ImmunoCult™ Human CD3/CD28 T Cell Activator (10971, Stemcell, VAN, Canada) and 10 ng/ml rIL-2 (589102, Biolegend, CA, USA). This PBMCs culturing was used to induce the activation and proliferation of T lymphocytes. All experimental procedures were approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (SRRSH 2022-499-01). 2.7. Animal experiments A humanized mouse model was used to explore the interaction between immune and tumor cells of human origin. Briefly, 1×10 6 RKO cells with stable knockdown of YTHDF2 (sh-NC and sh-YTHDF2) were resuspended in 100 µL phosphate-buffered saline (PBS, CR20012, Cienry, Zhejiang, P. R. China) and injected subcutaneously into the dorsal right flank of 5-week-old male non-obese oiabetic, severe combined immunodeficiency gamma (NSG) immunodeficient mice purchased from Shanghai Model Organisms Center (Shanghai, P. R. China). NSG mice are short of mature T cells, B cells and nature killer cells and deficient in cytokine signaling pathways. Seven days after inoculation, mice received intravenous 3×10 6 activated PBMCs. For treatment model, anti-PD-L1 (A2004, Selleck, TX, USA) or control immune globulin G (IgG, A2051, Selleck, TX, USA) were given intraperitoneally beginning on day 7 (200 µg/mouse). Tumor diameter was measured every 3 days for 3 weeks. Tumor volume (mm 3 ) was estimated by measuring the longest and shortest diameter of the tumor. Tumor volume = 1/2 length × width 2 . When tumors reached the size limit (2 cm), mice were sacrificed and tumors were isolated and weighed. All experimental procedures were approved by the Ethics Committee of Zhejiang University (ZJU20230100). 2.8. Flow cytometry assay Cultured cells were harvested using trypsin (CR25200, Cienry, Zhejiang, P. R. China), washed twice with ice-cold PBS and resuspended. Tumors were dissociated into single-cell suspensions using RPMI-1640 medium containing 2% FBS and 2 mg/ml type Ⅳ collagenase (C5138, Sigma, MO, USA) in a shaker at 200 revolutions per minute and 37°C for 2 h. Afterwards, 100 µL cell suspension (1×10 6 cells/ml) was stained with the indicated antibodies according to the manufacturer’s instructions. Tumor cells were stained with anti-PD-L1 antibody (329705, Biolegend, CA, USA). Lymphocytes were stained with anti-CD45 (304016, Biolegend, CA, USA), anti-CD3 (300305, 317335, Biolegend, CA, USA), anti-CD4 (317415, Biolegend CA, USA) and anti-CD8 (344705, Biolegend, CA, USA) antibodies. All live/dead discrimination was performed with Auqa stain kit (L34966, Invitrogen, CA, USA). After incubation for 30 min at room temperature in the dark, the stained cells were washed with PBS and analyzed using a flow cytometer (Beckman Coulter, CA, USA). 2.9. Small interfering RNA (siRNA) transfection SiRNAs against target genes were designed and synthesized by Tsingke Biotechnology (Beijing, P. R. China) and listed in Table S1 . Oligonucleotides were transfected into cells using Lipofectamine® RNAiMAX Reagent (13778, Invitrogen, CA, USA) according to the manufacturer’s recommendations. Subsequent experiments were conducted 48 h after transfection. 2.10. Cell counting kit 8 Cell viability was assessed using CCK-8 reagent (MA0218, Meilunbio, Liaoning, P. R. China) according to the manufacturer’s recommendations. Cells were seeded into 96-well plates at a density of 2×10 3 cells/well 24 h post transfection. At 0, 24, 48 and 72 h after seeding, CCK-8 solution was supplemented into each well in amount equaling 10% of the volume of the culture medium and incubated at 37°C for 1 hr. The absorbance at 450 nm was measured using a microplate absorbance reader (Biotek, VT, USA). 2.11. Trans-well migration and invasion assays Cell migration and invasion assays were performed using 24-well trans-well chambers (3422, Corning, NY, USA). Cells were harvested and suspended in serum-free medium at a density of 2.5×10 5 /ml. Subsequently, 100 µL cell suspension was added into the upper chamber while the lower chamber was filled with 600 µL medium containing 10% FBS. In the invasion assay, the upper chamber was pre-coated with Matrigel Matrix (354230, Corning, NY, USA) and incubated at 37°C for 1 hr. After incubation for 18 hr, cells that did not invade through the membrane were mechanically removed with a cotton swab. Next, 4% paraformaldehyde (G1102, Servicebio, Hubei, P. R. China) was used to fix the cells on the bottom surface of the membrane. Then, cells were stained using a crystal violet solution (MA0148, Meilunbio, Liaoning, P. R. China) and imaged using a digital microscopy (Carl Zeiss Jena, Germany). The number of cells was counted in 5 randomly selected fields. 2.12. Western blots Cells were collected and lysed using NP-40 lysis buffer (MA0156, Meilunbio, Liaoning, P. R. China). Samples of the lysates were separated on 10% gels and then transferred to polyvinylidene fluoride membranes. After blocked with 5% nonfat dry milk in tris buffered saline with Tween-20 (TBST), membranes were then incubated with primary antibodies at 4°C overnight. Antibodies for YTHDF2 (ab220163, Abcam Cambridge, MA, UK), PD-L1 (13684, CST, BOS, USA), SPOP (16750, Proteintech, Hubei, P. R. China), c-MYC (D84C12, CST, BOS, USA), BRD2 (sc-130707, Santa cruz, CA, USA) and GAPDH (AF0343, Elabscience, Hubei, P. R. China) were utilized. The following day, membranes were washed in TBST to remove non-specific binding antibodies and incubated with horseradish peroxidase-conjugated secondary antibodies (SY0115 and SY0119, Elabscience, Hubei, P. R. China) at room temperature for 1 hr. By exposing the membranes to the chemiluminescence substrate (MA0186, Meilunbio, Liaoning, P. R. China), protein bands were visualized by ChemiScope 3300 Mini (Clinx, P. R. China) and Amersham Imager 600 (GE, BOS, USA). GAPDH was used as the loading control, and the quantification of indicated protein bands was assessed using ImageJ software. 2.13. Reverse transcription PCR and quantitative real-time PCR Total RNA was extracted from cells using Trizol reagent (R401-01, Vazyme, Jiangsu, P. R. China) according to the manufacturer’s instructions. Subsequently, 1 µg RNA was reverse transcribed into cDNA using 1st Strand cDNA Synthesis Kit (R312, Vazyme, Jiangsu, P. R. China). The target genes and the reference gene HPRT1 were quantified using UltraSYBR Mixture (CW0957, CWBIO, Jiangsu, P. R. China) in the Roche LightCycler (Roche, USA). The primers used in qRT-PCR were listed in Table S2 . Quantification was performed using the 2 −△△Ct formula and the fold change (FC) of target genes was normalized by the internal control. 2.14. PD-1/PD-L1 binding assay Cells were cultured on coverslips, transfected with corresponding siRNAs and fixed with 4% paraformaldehyde (G1102, Servicebio, Hubei, P. R. China) for 10 min. After blocked with 5% bovine serum albumin (A8010, Solarbio, Beijing, P. R. China) in PBST for 30 min, coverslips were incubated with recombinant human PD-1 FC chimera protein (1086-PD, R&D systems, MN, USA) at 4°C overnight and then incubated with fluorescent conjugated secondary antibody (A-11013, Invitrogen, CA, USA) for 1 hr at room temperature. Slides were imaged on a confocal microscope (Nikon, Tokyo, Japan) and captured with 5 randomly selected fields. 2.15. Cells killing assay CRC cells were plated in a 96-well plate (RKO 5×10 3 /well, LoVo 2×10 3 /well). Activated PBMCs were added in a 1:10 ratio (CRC cells/PBMCs ratio) and incubated at 37°C for 12 hr. Caspase 3/7 substrate (22796, AAT Bioquest, CA, USA) was added to plate and incubated for 1 hr at room temperature according to the manufacturer’s instructions. Green-fluorescent cells were counted as dead cells. The fluorescence intensity (Ex/Em = 490/525 nm) was measured and normalized to CRC cells that were incubated in absence of PBMCs to give the percentage of dead cells. 2.16. RNA-seq, MeRIP-seq and RIP-seq The immune-precipitation (IP) protocols of MeRIP and RIP, and RNA sequencing protocols in sh-NC and sh-YTHDF2 RKO cells were described previously 29 with the help of Lc-Bio Technologies (Hangzhou, P. R. China). Visualizations of m 6 A modification and YTHDF2-binding sites on the transcripts were produced by using integrative genomics viewer (IGV). Two replicates were used. The possible m 6 A methylation locations of mRNAs were verified using SRAMP 30 . 2.17. MeRIP-qPCR and RIP-qPCR MeRIP-qPCR was performed using a Magna MeRIP™ m 6 A kit (17-10499, Millipore, MA, USA) and a miRNeasy Mini Kit (217004, Qiagen, Hilden, Germany) following the manufacturer’s protocol. And RIP was performed using a Magna RIP™ kit (17–700, Millipore, MA, USA). An antibody targeting YTHDF2 (ab246514, Abcam, Cambridge, MA, UK) was applied to immune-precipitate the mRNA-YTHDF2 complex. qRT-PCR was carried out following immunoprecipitation to quantify the changes in the m 6 A methylation level and YTHDF2-binding ability of target RNAs. The primers used were as followed in Table S2 . 2.18. Dot blot assay Total RNA was isolated with Trizol and denatured by heating at 95°C for 3 min. Then RNA was cooled on ice immediately. Then, 2 µL RNA was spotted on Nylon Transfer Membranes (YA1760, Solarbio, Beijing, P. R. China) and cross-linked by UVP (BD, USA) at a total energy of 0.24J/CM 2 . After incubation at 4°C overnight with an anti-m 6 A antibody (ab284130, Abcam, Cambridge, MA, UK), the membranes were washed in TBST next day and incubated with horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 hr. By exposing the membranes to the chemiluminescence substrate (MA0186, Meilunbio, Liaoning, P. R. China), m 6 A-modified dots were visualized by Amersham Imager 600 (GE, BOS, USA). Methylene blue was used as loading control. 2.19. Measurement of RNA lifespan Actinomycin D (ActD, HY17559, MCE, NJ, USA) was added to CRC cells to inhibit mRNA transcription. Samples were harvested at 0, 3, and 6 hr after treatment with ActD. Total RNA was isolated and tested via qRT-PCR analysis. 2.20. Dual-luciferase reporter assay The m 6 A sites were inserted into the pmirGLO vector (E1330, Promega, WI, USA), which was designed to study their effect on transcript stability. SPOP 3’ untranslated region (3’UTR) was cloned into the XhoI site of pmriGLO vector using ClonExpress® II One Step Cloning Kit (C112, Vazyme, Jiangsu, P. R. China). Both wild-type (WT) and mutant-type (Mut) m 6 A sequences in SPOP 3’UTR was listed in Table S3 . Primers used for SPOP 3’UTR amplification and mutation were listed in Table S4. All constructs were confirmed by DNA sequencing. Transfection of vector was performed with Lipofectamine 3000 reagent (L3000015, Invitrogen, CA, USA) according to the manufacturer’s instructions. Dual-Luciferase Assay kit (RG027, Beyotime, Shanghai, P. R. China) was employed to test mRNA production in cells 48 hr after transfection. 2.21. Statistical analysis All in vitro data were presented as mean ± standard error of mean (SEM). The majority of experiments were repeated two or three times. The hypothesis test for significance between two groups utilized Student’s t test. For three or more groups, results were analyzed with one-way analysis of variance (ANOVA). The Chi-square test was used to analyze IHC results and clinical characteristics of CRC patients. All statistical analyses were conducted using GraphPad Prism or SPSS software, and the statistical significance was inferred at P ≤ 0.050. 3. Results 3.1. YTHDF2 was a pro-tumorigenic factor in CRC and was closely correlated with immune-related pathways. To uncover the relationship between m 6 A regulators and CRC development, we systematically evaluated the expression patterns of 19 m 6 A regulators 31 in human CRC and normal tissues (Fig. 1 A). The “writers” included methyltransferase like 3 ( METTL3 ), METTL14 , WT1-associated protein ( WTAP ), RNA-binding motif protein 15 ( RBM15 ), RBM15B , and zinc finger CCCH-type containing 13 ( ZC3H13 ). The “readers” comprised YTH domain-containing 1 ( YTHDC1 ), YTHDC2 , YTHDF1 , YTHDF2 , YTHDF3 , RNA binding motif protein X-linked ( RBMX ), heterogeneous nuclear ribonucleoprotein C ( HNRNPC ), HNRNPA2B1 , insulin like growth factor 2 mRNA binding protein 1 ( IGF2BP1 ), IGF2BP2 and IGF2BP3 . The “erasers” included alpha-ketoglutarate dependent dioxygenase alkB homolog 5 ( ALKBH5 ) and fat mass- and obesity-associated protein ( FTO ). The expression levels of 15 m 6 A regulators ( P < 0.050), including the “reader” YTHDF2 ( P < 0.001), were clearly higher in CRC tissues than in normal tissues. In contrast, the expression levels of 4 m 6 A regulators were suppressed in CRC tissues ( P < 0.001), including ALKBH5 , HNRNPA2B1 , METTL3 and YTHDC2 . Apart from that, close connections were revealed among the expression of 18 m 6 A regulators and 14 immune-related genes ( P < 0.050, Fig. 1 B), which might explain the mechanisms underlying the above expression patterns. To assess the effects of m 6 A regulators on prognosis, we then stratified patients into groups with high or low expression levels of m 6 A regulators and plotted survival curves (GSE17537, n = 55; Fig. 1 C and Figure S1 ). The overall survival (OS) and disease-free survival (DFS) probability for patients with lower YTHDF2 expression levels were higher than for those with overexpressed YTHDF2 ( P 0.050). To obtain further information about the clinicopathological features of YTHDF2, we collected 98 CRC patient tissues, 73 normal tissues and the corresponding clinical information. It was evident that the expression of YTHDF2 in CRC tissues was higher than that in normal tissues (Fig. 1 D-E, Table 1 , P < 0.001). Higher YTHDF2 expression might be associated with older age ( P = 0.016), a higher histologic grade ( P = 0.001), and the T stage (Table 2 , P = 0.019) and, a worse prognosis (Fig. 1 F, P = 0.014). We also conducted GO enrichment analysis and found that the differentially expressed genes were enriched in biological processes closely related to immunity (Fig. 1 G, P < 0.001). Collectively, these results enabled us to understand how m 6 A regulators, especially YTHDF2, might affect CRC development. Table 1 Differential expression of YTHDF2 in colorectal cancer and normal tissues. Tissue types Total cases YTHDF2 expression, cases (%) χ2 P value High Low Colorectal cancer 98 43 (43.88) 55 (56.12) 39.56 < 0.001 Adjacent tissues 73 1 (1.37) 72 (98.63) Table 2 Correlation between YTHDF2 expression and clinicopathological characteristics. Variables Total YTHDF2 expression, cases (%) χ2 P value Low High Age (year) 5.797 0.016 < 70 42 29 (69.05) 13 (30.95) ≥ 70 50 22 (44.00) 28 (56.00) Gender 0.240 0.624 Female 42 25 (59.52) 17 (40.48) Male 55 30 (54.55) 25 (45.45) Grade 10.622 0.001 1–2 71 47 (66.20) 24 (33.80) 3 27 8 (29.63) 19 (70.37) T stage 5.509 0.019 T 1 -T 3 82 50 (60.98) 32 (39.02) T 4 12 3 (25.00) 9 (75.00) N stage 0.031 0.861 N 0 61 35 (57.38) 26 (42.62) N 1 -N 2 36 20 (55.56) 16 (44.44) TNM stage 0.031 0.861 I-II 61 35 (57.38) 26 (42.62) III-IV 36 20 (55.56) 16 (44.44) 3.2. YTHDF2-induced CRC tumor progression was independent of the proliferation, migration or invasion of cells. To investigate the role of YTHDF2 in CRC progression, we successfully constructed YTHDF2-knockdown RKO cells, which were labeled sh-NC and sh-YTHDF2 (Figure S2 A, P < 0.001). Through RNA-seq and GO analysis, these affected genes were found to be related to immune-related pathways (Fig. 2 D, P < 0.005). GSEA analysis (Fig. 2 E) also shown that lymphocyte migration (Enrichment score = -0.639, P = 0.011), T-cell migration (Enrichment score = -0.677, P = 0.013), T-cell mediated immunity (Enrichment score = -0.527, P = 0.032) and T-cell cytokine production (Enrichment score = -0.658, P = 0.048) were correlated with YTHDF2 expression. Based on these results, an immunodeficient mouse sub-skin model was applied (Fig. 2 A). Human PBMCs was activated and amplified into T lymphocytes (Figure S2 B). The growth and weight of the subcutaneously transplanted tumors in the NSG mice decreased with YTHDF2 knockdown (Fig. 2 B, P < 0.001; Fig. 2 C, P 0.050). Notably, we also established a mouse sub-skin model without PBMCs (Figure S2 C-D), in which the suppression of tumor growth caused by YTHDF2 knockdown disappeared ( P > 0.050). We investigated the tumor-infiltrating lymphocytes of subcutaneous tumors through flow cytometry and IHC. A higher count of CD4 + and CD8 + T cells (Fig. 2 F, P < 0.010; Figure S2 E, P 0.050), migration or invasion (Fig. 2 G, P > 0.050; Figure S2 H-I, P > 0.050). Hence, these results further indicated that the pro-tumorigenic effect of YTHDF2 was associated with immune interactions rather than cell proliferation, migration or invasion. Considering the importance of PD-L1 in T-cell function, we employed western blots to detect the relationship between YTHDF2 and PD-L1 in subcutaneous tumors (Fig. 2 G). When YTHDF2 expression decreased, PD-L1 expression declined ( P < 0.050; Figure S2 A, P < 0.050; Figure S2 J, P < 0.001). Besides, we explored the expression pattern of YTHDF2 in patients after atezolizumab treatment (Figure S3 A). The results showed that patients with higher expression level of YTHDF2 ( P < 0.050) and CD274 ( P = 0.099) were sensitive to atezolizumab treatment. Our data also showed that YTHDF2 and CD274 expression levels were significantly elevated in dMMR (Figure S3 B, P < 0.001) and CMS1 patients 28 , 32 (Figure S3 C, P < 0.001). In addition, we also observed that the expression level of YTHDF2 significantly increased in patients with kirsten rat sarcoma viral oncogene ( KRAS ) and B-Raf proto-oncogene ( BRAF ) mutation (Figure S3 D, P < 0.010). Based on the above findings, we further investigated whether depleting YTHDF2 enhances the response to immunotherapy with mouse models. NSG mice bearing tumors were treated with control IgG or anti-PD-L1 antibody. YTHDF2 knockdown synergized with anti-PD-L1 treatment, resulting in the inhibition of tumor growth and decrease in tumor weight (Figure S4A-D, P < 0.050). These effects were accompanied by increased CD4 + and CD8 + T cell infiltration (Figure S4E, P < 0.050). Taking these results together, we could ascertain that YTHDF2 was a key contributor to CRC progression, enabling tumor cells to produce higher expression of PD-L1 and exhibit stronger resistance to T-cell-mediated cytotoxicity. 3.3. Depletion of YTHDF2 decreased PD-L1 expression. Based on above results, it was reasonable to hypothesize that YTHDF2 could promote PD-L1-induced immunosuppression. After YTHDF2 knockdown, the protein expression level of PD-L1 decreased notably (Fig. 3 A, P < 0.010; Figure S5A, P 0.050; Figure S5A, P < 0.050) in RKO, LoVo and HCT-116 cells. Accordingly, flow cytometry demonstrated that reduced YTHDF2 suppressed PD-L1 that resided on the surface membrane of CRC cells (Fig. 3 B, P < 0.050; Figure S5B, P < 0.010). Additionally, 88 CRC tissues were analyzed and divided into groups with high or low PD-L1 expression depending on the median IHC scores (Fig. 3 C). Higher YTHDF2 expression levels were clearly more common in the group with higher PD-L1 expression levels ( P < 0.050). Similar results were obtained in 620 CRC tissues from the TCGA database (Fig. 3 D, P < 0.001). The decreased levels of PD-L1 mediated by reduced YTHDF2 could attenuate binding ability between PD-1 and PD-L1 (Fig. 3 E-F, P < 0.001). This decrease in binding activity resulted in an increase in T-cell cytotoxicity (Fig. 3 G, P < 0.050). Considering that m 6 A regulators share common target transcripts, we investigated the impact of other regulators on PD-L1 expression (Figure S5C-D). The experimental results showed that knockdown of METTL3, METTL14, ALKBH5, YTHDF1 or YTHDF3 had no influence on PD-L1 expression ( P > 0.050). At the same time, we found that knockdown of YTHDF2 did not impact the expression levels of METTL3, METTL14, ALKBH5, FTO, YTHDF1 or YTHDF3, suggesting that YTHDF2 regulated its mRNA targets independently (Figure S6A-B, P > 0.050). 3.4. Identification of potential target mRNAs of YTHDF2. Recent studies that systematically investigated YTHDF2 have indicated its role as a m 6 A reader that induces mRNA decay 17 . This finding seemed to contradict our findings regarding the positive relationship between YTHDF2 and PD-L1, we then subjected sh-NC and sh-YTHDF2 RKO cells to RNA, MeRIP and RIP sequencing. After performing MeRIP, we found m 6 A peaks mainly clustered at the 3’UTR around the stop codon (Fig. 4 A-B), coinciding with the distribution characteristics of m 6 A sites on mRNAs in previous studies (Fig. 4 C, fold enrichment > 2) 33 . We observed 525 transcripts with increased levels and 534 transcripts with decreased levels (Figure S7A-B). Among them, 306 transcripts were defined as significantly upregulated targets (Fig. 4 C, logFC > 1.2, P 0.58, P < 0.050). A “GGACU” motif was also identified in sh-NC and sh-YTHDF2 cells (Figure S7C, P < 0.001). It was noteworthy that the previously identified m 6 A consensus motif “DRACH” (where D represents A, G, or U, R represents A or G, and H represents A, C, or U) was validated in our study 34 . Furthermore, YTHDF2-binding sites followed the same pattern, with a “GAACU” motif in sh-NC cells and an “AGACU” motif in sh-YTHDF2 cells (Figure S7C, P < 0.001), which were consistent with the m 6 A consensus motif. To be considered the target mRNAs of YTHDF2, three screening requirements needed to be met. First, the expression of the target transcripts needed to be upregulated. Second, the target transcripts needed to contain m 6 A modification sites. Third, YTHDF2 needed to bind the target transcripts directly (Fig. 4 C). As a result, 77 transcripts were identified as candidates. Next, we eliminated transcripts with low expression, leaving 45 candidates for further analysis (Fig. 4 D). To identify connections between 45 candidates and the PD-L1 regulatory network, we did extensive research. SPOP and SHOC2 leucine rich repeat scaffold protein ( SHOC2 ) emerged as the most promising candidates. Using IGV, we produced visualizations of m 6 A modification and YTHDF2-binding sites on transcripts. Compared to the input group, SPOP mRNA had enriched m 6 A modification and YTHDF2-binding sites at the 3’UTR (Fig. 4 E), which suggested that YTHDF2 bound at the exact sites where m 6 A sites coexisted. In addition, SRAMP shown that m 6 A resided on the 1 593, 1 611 and 1 633 adenine residues (Fig. 4 F). Therefore, it was possible that YTHDF2 recognized and bound to these m 6 A sites on SPOP mRNA. In contrast, although MeRIP-seq data revealed that SHOC2 possessed both m 6 A modification and YTHDF2-binding sites mainly at the coding sequence (CDS, Figure S7D), SRAMP generated a slightly different prediction (CDS, Figure S7F). According to the SRAMP prediction, the m 6 A modification sites occurred around the 5’untranslated region (5’UTR), which weakened the plausibility of SHOC2 being the target mRNA of YTHDF2. While YTHDF2 was also found to accelerate the translation of mRNA, it was noteworthy that the ability of YTHDF2 to bind to CD274 mRNA cannot be completely excluded. The m 6 A methylation sites on CD274 mRNA occurred at the CDS and 3’UTR (Figure S7E and S7G), but the binding of YTHDF2 and CD274 mRNA had no difference between the input and RIP groups (Figure S7E). Upon YTHDF2 knockdown, the SPOP mRNA expression level increased (Fig. 4 G, P 0.050). Therefore, it was possible that YTHDF2 recognized and bound to SPOP mRNA. This observation showed no direct binding between YTHDF2 and CD274 mRNA. We presented the preliminary concept of YTHDF2 recognizing m 6 A sites on SPOP mRNA, binding to them, and accelerating the degradation of SPOP mRNA. 3.5. SPOP mRNA was a crucial target by which YTHDF2 regulated immune responses. As reported in previous studies, PD-L1 was regulated by the Cullin 3-SPOP E3 ligase via proteasome-mediated degradation 35 , 36 . Based on these findings, we aimed to demonstrate the existence of the YTHDF2-SPOP-PD-L1 axis in CRC. After inhibition of YTHDF2, the expression of SPOP was obviously upregulated (Fig. 5 A, P < 0.010; Figure S7H, P < 0.010). Given the expression pattern and clinical significance of SPOP in CRC, it was unsurprising to discover that SPOP had a lower expression level in tumor tissue (Fig. 5 B, P < 0.001) in contrast to YTHDF2 . Additionally, patients with elevated SPOP expression levels had better prognoses in both GSE31595 (Fig. 5 C, n = 37, P = 0.040) and GSE38832 ( n = 122, P = 0.085), indicating the tumor suppressor role of SPOP . To further uncover the effects of SPOP on CRC progression, cell proliferation, migration and invasion assays were also utilized. Our findings suggested that, consistent with YTHDF2, SPOP did not significantly affect any of these processes (Fig. 5 D-E, P > 0.050). To determine the possible function of SPOP, we classified patients into high or low SPOP expression groups. Through GO analysis, we discovered a correlation between SPOP and immune-related pathways (Fig. 5 F, P < 0.001). With the help of GSEA (Fig. 5 G), we also found significant relationships between SPOP expression and cytokine secretion (Enrichment score = 0.531, P < 0.001), T-cell activation (Enrichment score = 0.383, P = 0.040), T-cell differentiation (Enrichment score = 0.430, P = 0.032), and T-cell proliferation (Enrichment score = 0.455, P = 0.034). Furthermore, knockdown of SPOP resulted in an elevated PD-L1 protein expression level (Figure S8A, P 0.050). SPOP mutation was widely studied 37 and previous studies notified the somatic mutation of SPOP in CRC 38 , 39 . Mutant SPOP lost its interactions with substrates, causing the up-regulation of themselves 35 . Apart from PD-L1, it was reported that the substrates of SPOP also included c-MYC 40 , BRD2 41 , myeloid differentiation primary response gene 88 (MYD88) 42 , interleukin enhancer binding factor 3 (ILF3) 43 , GLI family zinc finger 2 (GLI2) 44 , tumor protein p53 binding protein 1 (TP53BP1) 45 and SET domain containing 2 (SETD2) 46 . We obtained SPOP mutation and expression data of CRC patients from Pan-Cancer Atlas project (Figure S8B, n = 592). It was revealed that the mRNA expression level of CD274 was significantly increased in SPOP mutant CRC tissues ( P 0.050). After YTHDF2 knockdown, protein expression of c-MYC and BRD2 did not show significant changes (Figure S8C, P > 0.050). We would infer that YTHDF2-mediated SPOP decay did not affect the stability of other substrates in CRC. Moreover, we conducted a further study examining the relationship between PD-L1 and another candidate, SHOC2. PD-L1 exhibited no significant changes upon SHOC2 knockdown (Figure S8D, P > 0.050). These findings supported the hypothesized YTHDF2-SPOP-PD-L1 axis in CRC. 3.6. YTHDF2 modulated SPOP mRNA in a m 6 A dependent manner. Based on above evidence, YTHDF2 and SPOP had different effects on PD-L1 expression. The combination of siRNAs resulted in unchanged PD-L1 expression, indicating that SPOP knockdown could reverse the decline in PD-L1 induced by si-YTHDF2 (Fig. 6 A, P > 0.050). Given that SPOP mRNA was identified as a target mRNA of YTHDF2, the next question was whether this impact relied on m 6 A methylation. A dot blot assay did not show an overall reduction in m 6 A modification after YTHDF2 knockdown in CRC cells (Fig. 6 B). However, knockdown of YTHDF2 led to a significant increase in the lifespan of SPOP mRNA in RKO and LoVo cells (Fig. 6 C, P < 0.050). Hence, YTHDF2 did not alter the overall methylation levels but affected the lifespan of its target mRNAs. To further confirm the observed results, we performed MeRIP-qPCR and RIP-qPCR. Compared to the IgG group, the anti-m 6 A antibody cross-reacted with m 6 A-containing SPOP mRNA fragments, and the anti-YTHDF2 antibody successfully isolated the YTHDF2-mRNA complex (Fig. 6 D, P < 0.001). As detected by m 6 A site-specific qPCR primers, SPOP mRNA levels were noticeably decreased after YTHDF2 knockdown in RIP-qPCR ( P 0.050). This result confirmed the role of YTHDF2 as a reader in destabilizing mRNA and directing transcripts to the decay machinery. The decrease in SPOP mRNA expression could be attributed to the binding of YTHDF2 to m 6 A sites located in the 3’UTR. To prove that YTHDF2 functioned by binding directly to target mRNAs, we introduced mutations into the reporter transcript (Fig. 6 E) and assessed the impact on mRNA abundance. The results showed a significant decrease in luciferase activity when m 6 A sites were introduced into the luciferase mRNA (Fig. 6 F, P < 0.001). The overall luciferase activity increased following YTHDF2 knockdown, indicating a major role of YTHDF2 in promoting mRNA degradation ( P < 0.010). Conversely, mutations in these sites compromised the ability of YTHDF2 to bind to m 6 A sites. These results confirmed that YTHDF2 hindered SPOP expression in a m 6 A-dependent manner. 4. Discussion Previous studies have conducted systematic analyses of YTHDF2, focusing on tumor proliferation 47 , metastasis 48 , apoptosis 49 , stem cells 50 and metabolism 51 . YTHDF2 has been found to be either upregulated 50 in acute myeloid leukemia or downregulated in melanoma 19 and CRC 52 . Additionally, YTHDF2 has been found to be both upregulated and downregulated in lung cancer 53 , gastric cancer 54 and liver cancer 55 . The contradictory expression pattern of YTHDF2 highlights the functional complexity of this protein. In this study, we confirmed that YTHDF2 was upregulated in CRC tissues, correlated with a poor prognosis and closely related to immune-related pathways. YTHDF2 promoted CRC growth in vivo but did not affect CRC cell proliferation, migration or invasion ability in vitro . A higher count of CD4 + and CD8 + T cells was observed in sh-YTHDF2 tumor obtained from NSG mice. High-throughput sequencing data and clinical sample data further supported a positive correlation between YTHDF2 and PD-L1. After YTHDF2 knockdown, the expression level and the subsequent binding ability of PD-L1 was disrupted, potentially playing a role in relieving T-cell repression in the tumor microenvironment. According to the results of our integrated analysis of transcriptome data and in vitro experiments, YTHDF2 recognized the m 6 A-modified 3’UTR of SPOP mRNA in CRC cells, subsequently inducing the degradation of SPOP mRNA. Reduced SPOP helped foster the PD-L1/PD-1 response. Thus, we developed the concept and presented the function of the YTHDF2-SPOP-PD-L1 axis in CRC (Fig. 6 G), in order to provide a basis for medical practice. The relationship between m 6 A regulators and tumor immunotherapy has been widely reported. It was found that YTHDF2 promoted lymphocyte (PD-1 + , CD8 + , Foxp3 + and CD45 RO + cells) infiltration in non-small-cell lung cancer 56 . A similar study also reported that the use of Toll-like receptor 9 agonist-conjugated siRNAs specifically targeting YTHDF2 in tumor-associated macrophages enhanced the efficacy of anti-PD-L1 therapy 20 . It made targeting m 6 A regulators a pillar of tumor therapy. Highly effective inhibitors of YTHDF2 showed promising application prospects in tumor treatment. Consistently, we also explored the connection between YTHDF2 knockdown and anti-PD-L1 treatment in the supplementary materials. The results showed that reduced YTHDF2 help improve treatment effect (Figure S4). Furthermore, the inhibition of METTL3 and METTL14 has also been shown to improve response to anti-PD-1 treatment by boosting CD8 + tumor-infiltrating lymphocytes in pMMR/microsatellite instability-low (MSI-L) CRC and melanoma 57 . In brief, researchers have exploited distinct inhibitors of m 6 A regulators as a form of combination therapy 58 . In addition, we also found the connections among YTHDF2 expression, CMS status, KRAS and BRAF mutation, indicating the involvement of YTHDF2 in different pathological and molecular characteristics (Figure S3 ). Studies have suggested that m 6 A methylation could provide more precise and effective control of mRNA stability during cancer progression. Our study also revealed the m 6 A-dependent SPOP mRNA recognition and consequent degradation functions of YTHDF2 (Fig. 6 C-F). SPOP is a nuclear speckle-type pox virus and zinc finger protein that plays an extensive role in tumorigenesis. As an adaptor protein for Cullin 3-based E3 ubiquitin ligases, SPOP has been shown to participate in the regulation of PD-L1 abundance by ubiquitination-mediated degradation in prostate cancer 35 and esophageal adenocarcinoma 59 . This interaction between SPOP and PD-L1 has also been observed in CRC 36 , consistent with our findings in this study (Figure S8A). In addition, there is a high expectation that m 6 A-circular RNAs will serve as prognostic biomarkers in CRC 60 , and YTHDF2 has been found to be an important recognition receptor for these circular RNAs 61 . This suggests that YTHDF2 impacts CRC progression in multiple ways. Despite the discovery of the YTHDF2-SPOP-PD-L1 axis in this study, further research is needed to investigate other mechanisms linking YTHDF2 and PD-L1 expression. While PD-L1 is regulated at the transcription, posttranscription, translation, and post-translation levels 62 , increasing evidence has implied that m 6 A modification plays a vital role in the regulation of PD-L1 63 . The m 6 A writer METTL3, eraser FTO, and reader YTHDF1 have been shown to bind directly to PD-L1 mRNA in breast cancer 64 and colon cancer 65 , and METTL14 has been shown to indirectly affect PD-L1 in cholangiocarcinoma 66 . Previous research has shown that YTHDF1 binds to mRNAs before YTHDF2 67 . This study demonstrated that YTHDF2 impacted PD-L1 expression in CRC, with no effect of YTHDF1 on PD-L1 (Figure S5C-D). Although the expression level of PD-L1 in CRC tissues failed to predict the effectiveness of treatment, studies have revealed the expression level of PD-L1 is closely related to tumor stage and prognosis 68 , 69 . And higher PD-L1 expression was more commonly seen in MSI-H tumors than in MSS tumors 70 . We also found higher YTHDF2 and CD274 expression in dMMR CRC (Figure S3 B). In addition, preliminary results have suggested that CRC patients with higher CD8 + T-cell infiltration would be more likely to benefit from immunotherapy 71 . Therefore, there might be underlying connections among PD-L1 expression, immune cell infiltration and immunotherapy efficacy in CRC. The sensitivity of antibodies used in IHC, differences in evaluation criteria, and CRC tissue heterogeneity are possible barriers to obtain accurate measurements of PD-L1. More studies are needed to elucidate whether changing the expression of PD-L1 profoundly affects CRC progression and treatment. However, we could not deny the existence of weaknesses in the study. Given that microRNAs 72 , 73 , 74 and HIF-2α 75 have been found to regulate YTHDF2 expression, the cause of aberrant YTHDF2 expression in CRC remains unknown and deserves further investigation. Additionally, the understanding of interactions between immune cells is just the tip of the iceberg. Although we tried to imitate the humanized immune environment with a humanized mouse model, the PBMC populations used were mainly composed of CD3 + T cells. Regarding the complex and dynamic nature of the immune response, it is important to rule out the impact of other immune cells. Apart from that, we failed to explore the function of somatic mutations in SPOP in CRC because of the low mutation frequency (< 5%) 22, 38 , and more samples are needed for further analysis. 5. Conclusions The integration of the aforementioned results led us to propose a mechanism involving m 6 A-dependent SPOP mRNA degradation boosted by YTHDF2. The SPOP-mediated PD-L1 proteasome-dependent degradation is weakened, promoting PD-L1 expression. Collectively, our results underscore the importance of YTHDF2 and suggest that the combination of targeting m 6 A regulators and anti-PD-1/PD-L1 therapy may be effective in treating CRC. Abbreviations 3’UTR 3’ untranslated region 5’UTR 5’ untranslated region ActD actinomycin D ALKBH5 alpha-ketoglutarate dependent dioxygenase alkB homolog 5 ANOVA one-way analysis of variance BRAF B-Raf proto-oncogene BRD2 bromodomain containing 2 c-MYC myelocytomatosis viral oncogene homolog CCK-8 cell counting kit-8 CDS coding sequence CMS consensus molecular subtypes CR complete response CRC colorectal cancer CTLA4 cytotoxic T-lymphocyte associated protein 4 CXCL9 C-X-C motif chemokine ligand 9 CXCL10 C-X-C motif chemokine ligand 10 DAPI 4’,6-diamidino-2-phenylindole DFS disease-free survival dMMR deficient mismatch repair EV empty vector F-Luc firefly/renilla luciferase FBS fetal bovine serum FC fold change FPKM fragments per kilo base per million mapped reads FTO fat mass- and obesity-associated protein GAPDH glyceraldehyde-3-phosphate dehydrogenase GEO Gene Expression Omnibus GLI2 GLI family zinc finger 2 GO gene ontology GSEA gene-set enrichment analysis GTEx Genotype-Tissue Expression GZMA granzyme A GZMB granzyme B HAVCR2 hepatitis A virus cellular receptor 2 HNRNPA2B1 heterogeneous nuclear ribonucleoprotein A2/B1 HNRNPC heterogeneous nuclear ribonucleoprotein C HPRT1 hypoxanthine phosphoribosyl transferase 1 HR hazard ratios IDO1 indoleamine 2,3-dioxygenase 1 IGF2BP1 insulin like growth factor 2 mRNA binding protein 1 IGF2BP2 insulin like growth factor 2 mRNA binding protein 2 IGF2BP3 insulin like growth factor 2 mRNA binding protein 3 IgG immune globulin G IGV integrative genomics viewer IHC immunohistochemistry ILF3 interleukin enhancer binding factor 3 IP immune-precipitation i.v intravenous injection KRAS kirsten rat sarcoma viral oncogene LAG3 lymphocyte activating 3 m 1 A N 1 -methyladenosine m 5 C 5- methylcytosine m 6 A N 6 -methyladenosine MeRIP methylated RNA immunoprecipitation METTL3 methyltransferase like 3 METTL14 methyltransferase like 14 MFI mean fluorescence intensity MSI-H microsatellite instability-high MSI-L microsatellite instability-low MSS microsatellite stability Mut mutated-type MYD88 myeloid differentiation primary response gene 88 N. S. no significance NC negative control NSG non-obese oiabetic, severe combined immunodeficiency gamma OS overall survival PBMCs peripheral blood mononuclear cells PBS phosphate-buffered saline PD progressive disease PD-1 programmed death-1 PD-L1 programmed death ligand 1 PDCD1 programmed cell death 1 PEI polyetherimide pMMR mismatch repair-proficient PR partial response PRF1 perforin 1 qRT-PCR quantitative real-time transcription-polymerase chain reaction RBM15 RNA binding motif protein 15 RBM15B RNA binding motif protein 15B RBMX RNA binding motif protein X-linked RIP RNA binding protein immunoprecipitation RFU relative fluorescence unit s.c subcutaneous injection SD stable disease SEM standard error of mean SETD2 SET domain containing 2 SHOC2 SHOC2 leucine rich repeat scaffold protein shRNA short hairpin RNA siRNA small interfering RNA SPOP speckle type BTB/POZ protein SRAMP sequence-based RNA adenosine methylation site predictor TBST tris buffered saline with Tween-20 TBX2 T-box transcription factor 2 TCGA The Cancer Genome Atlas TNF tumor necrosis factor TP53BP1 tumor protein p53 binding protein 1; WT wild-type WTAP WT1 associated protein YTHDC1 YTH domain-containing 1 YTHDC2 YTH domain-containing 2 YTHDF1 YTH N 6 -methyladenosine RNA binding protein F1 YTHDF2 YTH N 6 -methyladenosine RNA binding protein F2 YTHDF3 YTH N 6 -methyladenosine RNA binding protein F3 ZC3H13 zinc finger CCCH-type containing 13 Declarations Ethics approval and consent to participate The isolation of human peripheral blood mononuclear cells was approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (SRRSH 2022-499-01). Ethical approval for human sample collection was obtained from the Ethics Committee of Shanghai Outdo Biotech Company (YB M-05-02). Written informed consents were received from all patients. All experimental procedures for animals were approved by the Ethics Committee of Zhejiang University (ZJU20230100). Consent for publication Not applicable. Data availability statement All data relevant to the study are included in the article or supplemental materials. Data are available upon reasonable request. Conflict of interest statement The authors declare that they have no conflicts of interests. Funding information This work was supported by Natural Science Foundation of Zhejiang Province (LY23H160018) and National Natural Science Foundation of China (81903160). Authors’ contributions Xian Xu performed the experiments, analyzed the data and wrote the paper; Hao Chen helped with the experiments; Rongjie Zhao conducted and improved the process of bioinformatics analysis, and helped with supplementary experiment; Jiaying Shen, Hongming Pan and Weidong Han designed and supervised the entire project; the remaining study authors participated in the discussion and data interpretation. All authors read and approved the final manuscript. Acknowledgements We thank Dr Qiyin Zhou (Institute of Translational Medicine; Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. China) for providing guidance on SPOP study. And we thank Yunbin Yao (Animal Center; Sir Run Run Shaw Hospital; Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. China) for his assistance in animal experiments. We would also like to thank the staff members from Core facilities (Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. 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YTHDF2 reduction fuels inflammation and vascular abnormalization in hepatocellular carcinoma. Mol Cancer 2019, 18 (1) : 163. Additional Declarations There is no duality of interest Supplementary Files OriginalWesternBlots.docx Original Western Blots Originalm6AandRIPpeaks.xlsx Original m6A and RIP peaks Supplementarymaterials.docx Supplementary materials Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Cell Death & Disease → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Heatmap showed expression pattern of 19 m\u003csup\u003e6\u003c/sup\u003eA regulators in 620 CRC tissues from TCGA database, and 830 normal tissues from TCGA database (\u003cem\u003en\u003c/em\u003e = 51) and GTEx database (\u003cem\u003en \u003c/em\u003e= 779). The abscissa with different colors represented different groups, and the ordinate represented m\u003csup\u003e6\u003c/sup\u003eA regulators. Ⅰ-Ⅳ was related to tumor stage.\u003c/p\u003e\n\u003cp\u003eB. Heatmap of the correlation among m\u003csup\u003e6\u003c/sup\u003eA regulators and immune-related genes in 620 CRC tissues from TCGA database. The abscissa and ordinate represented genes, different colors represented different correlation coefficients (red represented positive correlation whereas blue represented negative correlation). The darker the color was, the stronger the relation was.\u003c/p\u003e\n\u003cp\u003eC. OS and DFS analysis for patients with high or low \u003cem\u003eYTHDF2\u003c/em\u003e expression levels from GSE17537 (\u003cem\u003en \u003c/em\u003e= 55). The top half of figure showed the overall survival curves and the bottom half of figure represented the number of patients at different points of time. Red represented high expression level whereas blue represented low expression level.\u003c/p\u003e\n\u003cp\u003eD-E. Tissue sections from CRC patients were immunohistochemically stained for YTHDF2. Representative adjacent (\u003cem\u003en\u003c/em\u003e = 73) and tumor (\u003cem\u003en\u003c/em\u003e = 98) tissues were shown. Scale bars, 200 μm and 100 μm. The quantification was shown (E).\u003c/p\u003e\n\u003cp\u003eF. Survival analysis for patients with high (= 12) or low (\u0026lt; 12) YTHDF2 expression levels from CRC patients’ samples.\u003c/p\u003e\n\u003cp\u003eG. GO analysis of 89 rectal tissues from TCGA database showed significant enrichment in immune-related pathway with high or low \u003cem\u003eYTHDF2\u003c/em\u003e expression levels. The abscissa represented enrichment score, and the ordinate represented pathways. The size of the circles represented the number of genes enriched, and the color depth of the circles represented the significance levels. Red arrows indicated immune-related pathways.\u003c/p\u003e\n\u003cp\u003eThe statistical difference of two groups was compared through the Wilcox test, significance difference of three groups was tested with Kruskal-Wallis test. Spearman’s correlation analysis was used to describe the correlation between quantitative variables without a normal distribution. Asterisks (*) stands for significance levels, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CRC: colorectal cancer; DFS: disease-free survival; IHC: immunohistochemistry; OS: overall survival.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/d3c27a768633bb2a28cd0612.png"},{"id":94546694,"identity":"03545c90-eb10-4220-b503-cf5458b8d3ff","added_by":"auto","created_at":"2025-10-28 17:40:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2515387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYTHDF2-induced tumor progression was independent of proliferation, migration or invasion of cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. Experimental design for the humanized mouse model (A). NSG mice were injected subcutaneously with 1×10\u003csup\u003e6\u003c/sup\u003e sh-NC or sh-YTHDF2 RKO cells. Seven days after inoculation, mice received intravenous 3×10\u003csup\u003e6\u003c/sup\u003e activated PBMCs. The tumor volumes and mice weights were monitored after cell implanting every 3 days (\u003cem\u003en\u003c/em\u003e = 5) (B). When tumors reached the size limit (2 cm), mice were sacrificed.\u003c/p\u003e\n\u003cp\u003eC. Representative images of subcutaneous tumors formed by sh-NC or sh-YTHDF2 RKO cells (left). Tumor masses were weighed after harvesting (right).\u003c/p\u003e\n\u003cp\u003eD. GO analysis of significantly upregulated and down-regulated genes identified by RNA-seq data in sh-NC and sh-YTHDF2 RKO cells. The abscissa represented enrichment score, and the ordinate represented pathways. The size of the circles represented the number of genes enriched, and the color depth of the circles represented the significance levels. Red arrows indicated immune-related pathways.\u003c/p\u003e\n\u003cp\u003eE. GSEA showed the pathways of differentially expressed genes altered by YTHDF2. The abscissa represented genes arranged in order of fold change, and the ordinate represented enrichment score.\u003c/p\u003e\n\u003cp\u003eF. Flow cytometric analysis of the percentage of CD4\u003csup\u003e+\u003c/sup\u003e or CD8\u003csup\u003e+\u003c/sup\u003e tumor infiltrating-lymphocytes.\u003c/p\u003e\n\u003cp\u003eG. Western blots analysis of YTHDF2 and PD-L1 protein expression level in paired subcutaneous tumors (left). GAPDH was included as loading control. The quantification was shown (right).\u003c/p\u003e\n\u003cp\u003eThe hypothesis test for significance between two groups utilized Student’s t test. Results were analyzed with ANOVA for three or more groups. N.S., no statistical significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Values are mean ± SEM.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ANOVA: one-way analysis of variance; \u003cem\u003ei.v\u003c/em\u003e: intravenous injection; NC: negative control; NSG: non-obese oiabetic, severe combined immunodeficiency gamma; PBMCs: peripheral blood mononuclear cells; \u003cem\u003es.c\u003c/em\u003e: subcutaneous injection.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/1c3685ae30b196fd82df9e01.png"},{"id":94546706,"identity":"f9b8d743-b595-43b0-8983-84a0bae00cbb","added_by":"auto","created_at":"2025-10-28 17:40:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6706104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepletion of YTHDF2 decreased PD-L1 expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. qRT-PCR and western blots analysis of YTHDF2 and \u003cem\u003eCD274\u003c/em\u003e/PD-L1 in siRNAs (NC or si-YTHDF2) transfected CRC cell lines (RKO and LoVo). HPRT1 served as the internal control in qRT-PCR. GAPDH was included as loading control in western blots. The quantification of western blots was shown (right).\u003c/p\u003e\n\u003cp\u003eB. PD-L1 was visualized by flow cytometry in RKO and LoVo cells treated with siRNAs (NC or si-YTHDF2). The quantification was shown (right).\u003c/p\u003e\n\u003cp\u003eC. Tissue sections from 88 CRC patients were immunohistochemically stained for YTHDF2 and PD-L1. Representative IHC images were shown (left). Patients were divided into two groups based on median PD-L1 IHC score. Scale bars, 200 μm and 100 μm. The quantification was shown (right).\u003c/p\u003e\n\u003cp\u003eD. The correlation between \u003cem\u003eYTHDF2\u003c/em\u003e and \u003cem\u003eCD274\u003c/em\u003e in 620 CRC tissues from TCGA database. The abscissa represented the expression distribution of \u003cem\u003eCD274\u003c/em\u003e, and the ordinate represented the expression distribution of \u003cem\u003eYTHDF2\u003c/em\u003e. The density curve on the right represented the trend in distribution of \u003cem\u003eYTHDF2\u003c/em\u003e, the upper density curve represented the trend in distribution of \u003cem\u003eCD274\u003c/em\u003e. The value on the bottom represented the correlation \u003cem\u003eP\u003c/em\u003e value and correlation coefficient.\u003c/p\u003e\n\u003cp\u003eE-F. Immuno-stain of PD-1 (fused to Ig-Fc) on RKO cells treated by siRNAs (NC or si-YTHDF2) (E). The green fluorescence represented the binding between PD-1 and PD-L1. Scale bars, 20 μm (upper), 10 μm (magnified). The quantification was shown (F).\u003c/p\u003e\n\u003cp\u003eG. T cell killing assay of RKO and LoVo cells after YTHDF2 knockdown.\u003c/p\u003e\n\u003cp\u003eThe expression correlation of two genes was analyzed with Spearman. The hypothesis test for significance between two groups utilized Student’s t test. Results were analyzed with ANOVA for three or more groups. N.S., no statistical significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Values are mean ± SEM.\u003c/p\u003e\n\u003cp\u003eAbbreviations: IHC: immunohistochemistry; MFI: mean fluorescence intensity; NC: negative control; RFU: relative fluorescence unit.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/5b32be8b7d4ffeff8461bbbe.png"},{"id":94546961,"identity":"707e2f91-099d-4aa5-9c96-5589bfb952ce","added_by":"auto","created_at":"2025-10-28 17:41:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1658374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of potential target mRNAs of YTHDF2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Percentages of m\u003csup\u003e6\u003c/sup\u003eA modification sites in 5’UTR, exon and 3’UTR.\u003c/p\u003e\n\u003cp\u003eB. Distribution of the enriched m\u003csup\u003e6\u003c/sup\u003eA peaks across mRNA transcripts and nearly unchanged distribution of m\u003csup\u003e6\u003c/sup\u003eA peaks between sh-NC and sh-YTHDF2 cells.\u003c/p\u003e\n\u003cp\u003eC. Venn diagram showed the overlap of transcripts identified by RNA-seq, MeRIP-seq and RIP-seq.\u003c/p\u003e\n\u003cp\u003eD. Flow chart showed the selection for candidate YTHDF2 target transcripts.\u003c/p\u003e\n\u003cp\u003eE. IGV tracks displayed the distribution of m\u003csup\u003e6\u003c/sup\u003eA peaks and YTHDF2-binding sites along \u003cem\u003eSPOP\u003c/em\u003e mRNAs according to MeRIP-seq and RIP-seq data. The abscissa represented transcripts, and the ordinate represented peaks signals. Red represented IP group whereas blue represented Input group.\u003c/p\u003e\n\u003cp\u003eF. The potential m\u003csup\u003e6\u003c/sup\u003eA sites of \u003cem\u003eSPOP\u003c/em\u003e mRNA were predicted by SRAMP. The abscissa represented transcripts, and the ordinate represented combined scores. Different color lines indicated different confidences (red, purple, blue and green respectively represented very high, high, moderate and low confidence). High score sites were marked by arrows.\u003c/p\u003e\n\u003cp\u003eG. qRT-PCR analysis of \u003cem\u003eSPOP\u003c/em\u003e and \u003cem\u003eSHOC2\u003c/em\u003e expression levels post siRNAs (NC or si-YTHDF2) transfection in CRC cell lines (RKO and LoVo). HPRT1 served as the internal control.\u003c/p\u003e\n\u003cp\u003eThe hypothesis test for significance between two groups utilized Student’s t test. Results were analyzed with ANOVA for three or more groups. N.S., no statistical significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Values are mean ± SEM.\u003c/p\u003e\n\u003cp\u003eAbbreviations: 3’UTR: 3’ untranslated region; 5’UTR: 5’ untranslated region; CDS: coding sequence; CRC: colorectal cancer; FC: fold change; IGV: integrative genomics viewer; IP: immune-precipitation; m\u003csup\u003e6\u003c/sup\u003eA: N\u003csup\u003e6\u003c/sup\u003e-methyladenosine; MeRIP: methylated RNA immunoprecipitation; NC: negative control; RIP: RNA binding protein immunoprecipitation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/43b25e7ab1d17824d7ab40a8.png"},{"id":94546876,"identity":"7b396611-965d-4442-9ad8-f4c9f63d64a8","added_by":"auto","created_at":"2025-10-28 17:41:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3123629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSPOP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e mRNA was a crucial target by which the YTHDF2 regulated immune responses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Western blots analysis of SPOP expression levels post siRNAs (NC or si-YTHDF2) transfection in CRC cell lines (RKO and LoVo). The quantification was shown (right).\u003c/p\u003e\n\u003cp\u003eB. The expression distribution of \u003cem\u003eSPOP\u003c/em\u003e in 620 CRC tissues and 789 normal tissues from TCGA and GTEx database. The abscissa represented groups, and the ordinate represented the expression distribution of SPOP. Different colors represented different groups. The statistical difference of two groups was compared through the Wilcox test.\u003c/p\u003e\n\u003cp\u003eC. Survival analysis for patients with high or low \u003cem\u003eSPOP\u003c/em\u003e expression levels from GSE31595 (\u003cem\u003en\u003c/em\u003e = 37) and GSE38832 (\u003cem\u003en\u003c/em\u003e = 122). The top half of figure showed the overall survival curves and the bottom half of figure represented the number of patients at different points of time. Red represented high expression level whereas blue represented low expression level.\u003c/p\u003e\n\u003cp\u003eD. CCK-8 proliferation assay in siRNAs (NC or si-SPOP) transfected CRC cell lines (RKO and LoVo).\u003c/p\u003e\n\u003cp\u003eE. Representative images of trans-well migration and invasion assays in RKO cells (upper) and LoVo cells (lower) after SPOP knockdown (left). The quantification was shown (right).\u003c/p\u003e\n\u003cp\u003eF. GO analysis of 89 rectal cancer tissues from TCGA database showed significant enrichment in immune-related pathway between high and low \u003cem\u003eSPOP\u003c/em\u003eexpression (based on median expression) levels. The abscissa represented enrichment score, and the ordinate represented pathways. The size of the circles represented the number of genes enriched, and the color depth of the circles represented the significance levels. Red arrows indicated immune-related pathways.\u003c/p\u003e\n\u003cp\u003eG. GSEA showed the pathways of differentially expressed genes altered by \u003cem\u003eSPOP\u003c/em\u003e. The abscissa represented genes arranged in order of fold change, and the ordinate represented enrichment score.\u003c/p\u003e\n\u003cp\u003eThe hypothesis test for significance between two groups utilized Student’s t test. Results were analyzed with ANOVA for three or more groups. GAPDH was included as loading control in western blots. HPRT1 served as the internal control in qRT-PCR. N.S., no statistical significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Values are mean ± SEM.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CCK-8: cell counting kit-8; CRC: colorectal cancer; NC: negative control.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/b530a0d32392ff49a61a4344.png"},{"id":94547022,"identity":"88575a06-20b8-4b71-ba38-be82c0b086aa","added_by":"auto","created_at":"2025-10-28 17:41:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3586663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYTHDF2 modulated \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSPOP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e mRNA in a m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA dependent manner.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. qRT-PCR and western blots analysis of SPOP and \u003cem\u003eCD274\u003c/em\u003e/PD-L1 expression level in siRNAs (NC, si-YTHDF2, or si-SPOP) transfected CRC cell lines (RKO and LoVo). The quantification of western blots was shown.\u003c/p\u003e\n\u003cp\u003eB. RNA m\u003csup\u003e6\u003c/sup\u003eA dot blot assay of siRNAs (NC or si-YTHDF2) transfected CRC cell lines (RKO and LoVo). Methylene blue staining served as a loading control.\u003c/p\u003e\n\u003cp\u003eC. The decay rate of \u003cem\u003eSPOP\u003c/em\u003e mRNA was detected after 2 μmol/L ActD treatment at the indicated times through qRT-PCR.\u003c/p\u003e\n\u003cp\u003eD. MeRIP-qPCR (left) and RIP-qPCR (right) analysis of m\u003csup\u003e6\u003c/sup\u003eA levels and YTHDF2-binding ability in sh-NC or sh-YTHDF2 RKO cells.\u003c/p\u003e\n\u003cp\u003eE. Schematic showed generation strategy for the pmirGLO luciferase reporters containing WT and Mut (A to T) \u003cem\u003eSPOP\u003c/em\u003e 3’UTR.\u003c/p\u003e\n\u003cp\u003eF. Luciferase activities of the indicated pmirGLO vector were measured in CRC cell lines (RKO and LoVo) with or without YTHDF2 knockdown.\u003c/p\u003e\n\u003cp\u003eG. A schematic diagram depicting the function of YTHDF2-SPOP-PD-L1 axis in CRC.\u003c/p\u003e\n\u003cp\u003eThe hypothesis test for significance between two groups utilized Student’s t test. Results were analyzed with ANOVA for three or more groups. GAPDH was included as loading control in western blots. HPRT1 served as the internal control in qRT-PCR. N.S., no statistical significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.050, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.010, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Values are mean ± SEM.\u003c/p\u003e\n\u003cp\u003eAbbreviations: 3’UTR: 3’ untranslated region; ActD: actinomycin D; CRC: colorectal cancer; EV: empty vector; F-Luc: firefly/renilla luciferase; IgG: immune globulin G; IP: immune-precipitation; m\u003csup\u003e6\u003c/sup\u003eA: N\u003csup\u003e6\u003c/sup\u003e-methyladenosine; MeRIP: methylated RNA immunoprecipitation; Mut: mutated-type; NC: negative control; RIP: RNA binding protein immunoprecipitation; WT: wild-type.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/cb063366d2e13ffd3c0f22ce.png"},{"id":105888389,"identity":"7b52b2cd-4147-4f45-b02a-492e341ac048","added_by":"auto","created_at":"2026-04-01 07:44:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24704822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/3f2700ca-9e8b-4e54-8eca-1fd8ebe039a6.pdf"},{"id":94546968,"identity":"5ef3e98c-ae14-4b56-9656-04f7fc84a6e8","added_by":"auto","created_at":"2025-10-28 17:41:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2940280,"visible":true,"origin":"","legend":"Original Western Blots","description":"","filename":"OriginalWesternBlots.docx","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/fb2f5d4ded7f49003b07bfbd.docx"},{"id":94546915,"identity":"719baa7d-0baf-4805-8444-bb507687e8aa","added_by":"auto","created_at":"2025-10-28 17:41:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6802426,"visible":true,"origin":"","legend":"Original m6A and RIP peaks","description":"","filename":"Originalm6AandRIPpeaks.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/eda98823d937c9674bb85468.xlsx"},{"id":94547194,"identity":"2b8e7c12-2f39-440e-8efa-c32befa9a2a2","added_by":"auto","created_at":"2025-10-28 17:42:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2846611,"visible":true,"origin":"","legend":"Supplementary materials","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7735800/v1/f76aee1589986e5655790192.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReduced YTHDF2 inhibits PD-L1 expression by stabilizing m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-containing \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSPOP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e mRNA in colorectal cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eWorldwide, CRC is a common malignancy of the digestive tract, with an incidence and mortality ranking third among all malignant tumors \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and similar burden can be seen in China \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For patients who receive surgery, 30\u0026thinsp;~\u0026thinsp;40% experience recurrence and metastasis within 5 years \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although chemotherapy, molecular targeted therapy and immunotherapy have shown clinical success, advanced patients are still confronted with poor prognoses and low survival rates \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Research progress has revealed some of the mechanisms underlying the development and occurrence of CRC, such as the roles of tumor microenvironment \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. PD-L1, coded by \u003cem\u003eCD274\u003c/em\u003e, is a crucial immune regulator that plays an essential biological role in subverting the anticancer activity of T cells \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Anti-PD-1/PD-L1 agents act on T cells and relieve immune suppression, becoming one of the most promising checkpoint inhibitors. Regarding CRC, anti-PD-1 agents elicit significant clinical responses in microsatellite instability-high (MSI-H)/deficient mismatch repair (dMMR) patients \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, anti-PD-1 agents are unable to overcome primary drug resistance in approximately 30% of CRC patients, and they are largely ineffective in microsatellite stability (MSS) and mismatch repair-proficient (pMMR) CRC patients \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thus, it is especially meaningful to explore the mechanism and effect of PD-1/PD-L1 response in CRC.\u003c/p\u003e\u003cp\u003eWith advancements in technology, various forms of RNA modifications have been discovered. As the most prevalent RNA modification, m\u003csup\u003e6\u003c/sup\u003eA has been extensively studied for its vital role in various biological processes, such as RNA processing, translation and degradation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, m\u003csup\u003e6\u003c/sup\u003eA regulators have been reported to participate in immune responses \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Various studies have highlighted the promising potential of m\u003csup\u003e6\u003c/sup\u003eA regulators in regulating macrophages \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, dendritic cells \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, myeloid-derived suppressor cells \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, natural killer cells \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and T cells \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. m\u003csup\u003e6\u003c/sup\u003eA regulators exhibiting dynamic regulatory effects are classified as writers, erasers and readers. YTHDF2 is a reader that primarily recognizes and binds to m\u003csup\u003e6\u003c/sup\u003eA-modified sites on mRNAs \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that YTHDF2 primarily participates in mRNA decay \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, but can also enhance mRNA translation in response to heat shock stress conditions \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, YTHDF2 has been found involved in immune responses and therapeutic efficacy improvements \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the impact of YTHDF2 on CRC remains unclear.\u003c/p\u003e\u003cp\u003eIn our study, we proposed a regulatory relation between YTHDF2 and PD-L1 expression and explored the role of YTHDF2 in CRC progression, providing potential molecular markers and therapeutic targets for CRC patients.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Bioinformatics Analysis\u003c/h2\u003e\u003cp\u003eThe RNA-seq data were obtained from The Cancer Genome Atlas (TCGA, 620 CRC tissues and 51 normal tissues) data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Genotype-Tissue Expression (GTEx, 779 normal tissues) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/index.html\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Prognosis and expression information were provided by GSE17537 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55), GSE31595 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37), and GSE38832 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;122). We extracted pathological and molecular characteristics as well as expression data from GSE39582 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;585), and immunotherapy response information from IMvigor210 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;348) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We collected \u003cem\u003eSPOP\u003c/em\u003e mutation data from Pan-Cancer Atlas project \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnalyses were performed in R software (version 4.2.2). ggplot2 and pheatmap were utilized to depict the expression pattern of 19 m\u003csup\u003e6\u003c/sup\u003eA regulators \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and multi-gene correlation \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The univariate COX regression model was adopted to calculate the hazard ratios (HR) for m\u003csup\u003e6\u003c/sup\u003eA regulators. Log-rank test was performed for survival analysis. Differentially expressed genes were identified using limma package. The gene set of \u0026ldquo;c5.all.v7.1.symbols\u0026rdquo; was downloaded from gene-set enrichment analysis (GSEA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for gene ontology (GO) and GSEA enrichment analysis using fgsea package. Consensus molecular subtypes (CMS) classification was defined using CMSclassifier package \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The statistical difference of two groups was compared through Wilcox test, and the significance difference of three groups was tested with Kruskal-Wallis test. Spearman\u0026rsquo;s correlation analysis was used to describe the correlation between quantitative variables without a normal distribution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Human sample collection\u003c/h2\u003e\u003cp\u003e73 adjacent normal and 98 CRC tissues were collected from National Human Genetic Resources Sharing Service Platform (Shanghai, P. R. China). Patients were not subjected to any preoperative anti-cancer treatment. The diagnoses of CRC were all histologically confirmed and all participants were provided informed consent for obtaining the study specimens. Ethical approval was obtained from the Ethics Committee of Shanghai Outdo Biotech Company (YB M-05-02).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Histology and immunohistochemical staining\u003c/h2\u003e\u003cp\u003eFormalin-fixed (G1101, Servicebio, Hubei, P. R. China) and paraffin-embedded samples were sectioned at 5 \u0026micro;m and stained with hematoxylin and eosin (G1005, Servicebio, Hubei, P. R. China). Immunohistochemistry (IHC) was performed on samples using YTHDF2 antibody (ab246514, Abcam, Cambridge, MA, UK), PD-L1 antibody (13684, CST, BOS, USA) or CD8 (ab235951, Abcam, Cambridge, MA, UK). IHC score was generated by assigning sub-scores for the distribution (0\u0026thinsp;~\u0026thinsp;4) and intensity (0\u0026thinsp;~\u0026thinsp;3), which were multiplied together to yield the immunoreactivity score. The percentage positivity was scored as 0 (no staining), 1 (1\u0026thinsp;~\u0026thinsp;25%), 2 (26\u0026thinsp;~\u0026thinsp;50%), 3 (51\u0026thinsp;~\u0026thinsp;75%), or 4 (76\u0026thinsp;~\u0026thinsp;100%). The staining intensity was scored as 0 (no staining), 1 (weakly stained), 2 (moderately stained), or 3 (strongly stained). The total IHC score was the product of the above factors, which ranged from 0 to 12. The CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration in tumor tissues was evaluated by staining with CD8, and quantification was performed using ImageJ software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Cell culture\u003c/h2\u003e\u003cp\u003eHuman CRC cell lines (RKO, LoVo and HCT-116) and HEK-293T were utilized in this study. Cell lines were maintained in RPMI-1640 (MA0215, Meilunbio, Liaoning, P. R. China) or DMEM medium (MA0212, Meilunbio, Liaoning, P. R. China). All mediums were supplemented with 10% fetal bovine serum (FBS, NFBS-2500, Noverse\u0026trade;, SN, Germany) and 1% penicillin/streptomycin (MA0110, Meilunbio, Liaoning, P. R. China). All cells were cultured at 37\u0026deg;C in a humidified atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e. Cells in the logarithmic growth phase were used in the subsequent experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Lentiviral production and transduction\u003c/h2\u003e\u003cp\u003eAll short hairpin RNA (shRNA) targeting sequences were cloned into hU6-MCS-CBh-gcGFP-IRES-puromycin vector (Genechem, P R. China). To produce lentivirus, HEK-293T cells were transfected with the aforementioned shRNA vectors and packaging plasmids pLP1, pLP2 and pLP/VSVG at a 4:3:3:2 ratio. Transfection was performed using polyetherimide (PEI) reagent (#24765, Polysciences, PA, USA). The viral supernatant was collected 48 h after transfection, filtered with a 0.45 \u0026micro;m filter and used to infect RKO cells with polybrene (H8761, Solarbio, Beijing, P. R. China). 1 ng/\u0026micro;L puromycin (MB2005, Meilunbio, Liaoning, P. R. China) was applied for following selection. shRNA targeting sequences were listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Primary cell culture\u003c/h2\u003e\u003cp\u003ePeripheral blood mononuclear cells (PBMCs) were isolated through density gradient centrifugation of fresh blood from healthy volunteers, using Human peripheral lymphocyte separation medium (MB0911, Meilunbio, Liaoning, P. R. China). Written informed consent was obtained from each volunteer. PBMC single-cell suspensions were cultured in RPMI-1640 medium supplemented with ImmunoCult\u0026trade; Human CD3/CD28 T Cell Activator (10971, Stemcell, VAN, Canada) and 10 ng/ml rIL-2 (589102, Biolegend, CA, USA). This PBMCs culturing was used to induce the activation and proliferation of T lymphocytes. All experimental procedures were approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (SRRSH 2022-499-01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Animal experiments\u003c/h2\u003e\u003cp\u003eA humanized mouse model was used to explore the interaction between immune and tumor cells of human origin. Briefly, 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e RKO cells with stable knockdown of YTHDF2 (sh-NC and sh-YTHDF2) were resuspended in 100 \u0026micro;L phosphate-buffered saline (PBS, CR20012, Cienry, Zhejiang, P. R. China) and injected subcutaneously into the dorsal right flank of 5-week-old male non-obese oiabetic, severe combined immunodeficiency gamma (NSG) immunodeficient mice purchased from Shanghai Model Organisms Center (Shanghai, P. R. China). NSG mice are short of mature T cells, B cells and nature killer cells and deficient in cytokine signaling pathways. Seven days after inoculation, mice received intravenous 3\u0026times;10\u003csup\u003e6\u003c/sup\u003e activated PBMCs. For treatment model, anti-PD-L1 (A2004, Selleck, TX, USA) or control immune globulin G (IgG, A2051, Selleck, TX, USA) were given intraperitoneally beginning on day 7 (200 \u0026micro;g/mouse). Tumor diameter was measured every 3 days for 3 weeks. Tumor volume (mm\u003csup\u003e3\u003c/sup\u003e) was estimated by measuring the longest and shortest diameter of the tumor. Tumor volume\u0026thinsp;=\u0026thinsp;1/2 length \u0026times; width\u003csup\u003e2\u003c/sup\u003e. When tumors reached the size limit (2 cm), mice were sacrificed and tumors were isolated and weighed. All experimental procedures were approved by the Ethics Committee of Zhejiang University (ZJU20230100).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Flow cytometry assay\u003c/h2\u003e\u003cp\u003eCultured cells were harvested using trypsin (CR25200, Cienry, Zhejiang, P. R. China), washed twice with ice-cold PBS and resuspended. Tumors were dissociated into single-cell suspensions using RPMI-1640 medium containing 2% FBS and 2 mg/ml type Ⅳ collagenase (C5138, Sigma, MO, USA) in a shaker at 200 revolutions per minute and 37\u0026deg;C for 2 h. Afterwards, 100 \u0026micro;L cell suspension (1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/ml) was stained with the indicated antibodies according to the manufacturer\u0026rsquo;s instructions. Tumor cells were stained with anti-PD-L1 antibody (329705, Biolegend, CA, USA). Lymphocytes were stained with anti-CD45 (304016, Biolegend, CA, USA), anti-CD3 (300305, 317335, Biolegend, CA, USA), anti-CD4 (317415, Biolegend CA, USA) and anti-CD8 (344705, Biolegend, CA, USA) antibodies. All live/dead discrimination was performed with Auqa stain kit (L34966, Invitrogen, CA, USA). After incubation for 30 min at room temperature in the dark, the stained cells were washed with PBS and analyzed using a flow cytometer (Beckman Coulter, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Small interfering RNA (siRNA) transfection\u003c/h2\u003e\u003cp\u003eSiRNAs against target genes were designed and synthesized by Tsingke Biotechnology (Beijing, P. R. China) and listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Oligonucleotides were transfected into cells using Lipofectamine\u0026reg; RNAiMAX Reagent (13778, Invitrogen, CA, USA) according to the manufacturer\u0026rsquo;s recommendations. Subsequent experiments were conducted 48 h after transfection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Cell counting kit 8\u003c/h2\u003e\u003cp\u003eCell viability was assessed using CCK-8 reagent (MA0218, Meilunbio, Liaoning, P. R. China) according to the manufacturer\u0026rsquo;s recommendations. Cells were seeded into 96-well plates at a density of 2\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells/well 24 h post transfection. At 0, 24, 48 and 72 h after seeding, CCK-8 solution was supplemented into each well in amount equaling 10% of the volume of the culture medium and incubated at 37\u0026deg;C for 1 hr. The absorbance at 450 nm was measured using a microplate absorbance reader (Biotek, VT, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Trans-well migration and invasion assays\u003c/h2\u003e\u003cp\u003eCell migration and invasion assays were performed using 24-well trans-well chambers (3422, Corning, NY, USA). Cells were harvested and suspended in serum-free medium at a density of 2.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e/ml. Subsequently, 100 \u0026micro;L cell suspension was added into the upper chamber while the lower chamber was filled with 600 \u0026micro;L medium containing 10% FBS. In the invasion assay, the upper chamber was pre-coated with Matrigel Matrix (354230, Corning, NY, USA) and incubated at 37\u0026deg;C for 1 hr. After incubation for 18 hr, cells that did not invade through the membrane were mechanically removed with a cotton swab. Next, 4% paraformaldehyde (G1102, Servicebio, Hubei, P. R. China) was used to fix the cells on the bottom surface of the membrane. Then, cells were stained using a crystal violet solution (MA0148, Meilunbio, Liaoning, P. R. China) and imaged using a digital microscopy (Carl Zeiss Jena, Germany). The number of cells was counted in 5 randomly selected fields.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12. Western blots\u003c/h2\u003e\u003cp\u003eCells were collected and lysed using NP-40 lysis buffer (MA0156, Meilunbio, Liaoning, P. R. China). Samples of the lysates were separated on 10% gels and then transferred to polyvinylidene fluoride membranes. After blocked with 5% nonfat dry milk in tris buffered saline with Tween-20 (TBST), membranes were then incubated with primary antibodies at 4\u0026deg;C overnight. Antibodies for YTHDF2 (ab220163, Abcam Cambridge, MA, UK), PD-L1 (13684, CST, BOS, USA), SPOP (16750, Proteintech, Hubei, P. R. China), c-MYC (D84C12, CST, BOS, USA), BRD2 (sc-130707, Santa cruz, CA, USA) and GAPDH (AF0343, Elabscience, Hubei, P. R. China) were utilized. The following day, membranes were washed in TBST to remove non-specific binding antibodies and incubated with horseradish peroxidase-conjugated secondary antibodies (SY0115 and SY0119, Elabscience, Hubei, P. R. China) at room temperature for 1 hr. By exposing the membranes to the chemiluminescence substrate (MA0186, Meilunbio, Liaoning, P. R. China), protein bands were visualized by ChemiScope 3300 Mini (Clinx, P. R. China) and Amersham Imager 600 (GE, BOS, USA). GAPDH was used as the loading control, and the quantification of indicated protein bands was assessed using ImageJ software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13. Reverse transcription PCR and quantitative real-time PCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from cells using Trizol reagent (R401-01, Vazyme, Jiangsu, P. R. China) according to the manufacturer\u0026rsquo;s instructions. Subsequently, 1 \u0026micro;g RNA was reverse transcribed into cDNA using 1st Strand cDNA Synthesis Kit (R312, Vazyme, Jiangsu, P. R. China). The target genes and the reference gene HPRT1 were quantified using UltraSYBR Mixture (CW0957, CWBIO, Jiangsu, P. R. China) in the Roche LightCycler (Roche, USA). The primers used in qRT-PCR were listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Quantification was performed using the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e formula and the fold change (FC) of target genes was normalized by the internal control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14. PD-1/PD-L1 binding assay\u003c/h2\u003e\u003cp\u003eCells were cultured on coverslips, transfected with corresponding siRNAs and fixed with 4% paraformaldehyde (G1102, Servicebio, Hubei, P. R. China) for 10 min. After blocked with 5% bovine serum albumin (A8010, Solarbio, Beijing, P. R. China) in PBST for 30 min, coverslips were incubated with recombinant human PD-1 FC chimera protein (1086-PD, R\u0026amp;D systems, MN, USA) at 4\u0026deg;C overnight and then incubated with fluorescent conjugated secondary antibody (A-11013, Invitrogen, CA, USA) for 1 hr at room temperature. Slides were imaged on a confocal microscope (Nikon, Tokyo, Japan) and captured with 5 randomly selected fields.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15. Cells killing assay\u003c/h2\u003e\u003cp\u003eCRC cells were plated in a 96-well plate (RKO 5\u0026times;10\u003csup\u003e3\u003c/sup\u003e/well, LoVo 2\u0026times;10\u003csup\u003e3\u003c/sup\u003e/well). Activated PBMCs were added in a 1:10 ratio (CRC cells/PBMCs ratio) and incubated at 37\u0026deg;C for 12 hr. Caspase 3/7 substrate (22796, AAT Bioquest, CA, USA) was added to plate and incubated for 1 hr at room temperature according to the manufacturer\u0026rsquo;s instructions. Green-fluorescent cells were counted as dead cells. The fluorescence intensity (Ex/Em\u0026thinsp;=\u0026thinsp;490/525 nm) was measured and normalized to CRC cells that were incubated in absence of PBMCs to give the percentage of dead cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16. RNA-seq, MeRIP-seq and RIP-seq\u003c/h2\u003e\u003cp\u003eThe immune-precipitation (IP) protocols of MeRIP and RIP, and RNA sequencing protocols in sh-NC and sh-YTHDF2 RKO cells were described previously \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e with the help of Lc-Bio Technologies (Hangzhou, P. R. China). Visualizations of m\u003csup\u003e6\u003c/sup\u003eA modification and YTHDF2-binding sites on the transcripts were produced by using integrative genomics viewer (IGV). Two replicates were used. The possible m\u003csup\u003e6\u003c/sup\u003eA methylation locations of mRNAs were verified using SRAMP \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17. MeRIP-qPCR and RIP-qPCR\u003c/h2\u003e\u003cp\u003eMeRIP-qPCR was performed using a Magna MeRIP\u0026trade; m\u003csup\u003e6\u003c/sup\u003eA kit (17-10499, Millipore, MA, USA) and a miRNeasy Mini Kit (217004, Qiagen, Hilden, Germany) following the manufacturer\u0026rsquo;s protocol. And RIP was performed using a Magna RIP\u0026trade; kit (17\u0026ndash;700, Millipore, MA, USA). An antibody targeting YTHDF2 (ab246514, Abcam, Cambridge, MA, UK) was applied to immune-precipitate the mRNA-YTHDF2 complex. qRT-PCR was carried out following immunoprecipitation to quantify the changes in the m\u003csup\u003e6\u003c/sup\u003eA methylation level and YTHDF2-binding ability of target RNAs. The primers used were as followed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.18. Dot blot assay\u003c/h2\u003e\u003cp\u003eTotal RNA was isolated with Trizol and denatured by heating at 95\u0026deg;C for 3 min. Then RNA was cooled on ice immediately. Then, 2 \u0026micro;L RNA was spotted on Nylon Transfer Membranes (YA1760, Solarbio, Beijing, P. R. China) and cross-linked by UVP (BD, USA) at a total energy of 0.24J/CM\u003csup\u003e2\u003c/sup\u003e. After incubation at 4\u0026deg;C overnight with an anti-m\u003csup\u003e6\u003c/sup\u003eA antibody (ab284130, Abcam, Cambridge, MA, UK), the membranes were washed in TBST next day and incubated with horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 hr. By exposing the membranes to the chemiluminescence substrate (MA0186, Meilunbio, Liaoning, P. R. China), m\u003csup\u003e6\u003c/sup\u003eA-modified dots were visualized by Amersham Imager 600 (GE, BOS, USA). Methylene blue was used as loading control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.19. Measurement of RNA lifespan\u003c/h2\u003e\u003cp\u003eActinomycin D (ActD, HY17559, MCE, NJ, USA) was added to CRC cells to inhibit mRNA transcription. Samples were harvested at 0, 3, and 6 hr after treatment with ActD. Total RNA was isolated and tested via qRT-PCR analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e2.20. Dual-luciferase reporter assay\u003c/h2\u003e\u003cp\u003eThe m\u003csup\u003e6\u003c/sup\u003eA sites were inserted into the pmirGLO vector (E1330, Promega, WI, USA), which was designed to study their effect on transcript stability. SPOP 3\u0026rsquo; untranslated region (3\u0026rsquo;UTR) was cloned into the XhoI site of pmriGLO vector using ClonExpress\u0026reg; II One Step Cloning Kit (C112, Vazyme, Jiangsu, P. R. China). Both wild-type (WT) and mutant-type (Mut) m\u003csup\u003e6\u003c/sup\u003eA sequences in SPOP 3\u0026rsquo;UTR was listed in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Primers used for SPOP 3\u0026rsquo;UTR amplification and mutation were listed in Table S4. All constructs were confirmed by DNA sequencing. Transfection of vector was performed with Lipofectamine 3000 reagent (L3000015, Invitrogen, CA, USA) according to the manufacturer\u0026rsquo;s instructions. Dual-Luciferase Assay kit (RG027, Beyotime, Shanghai, P. R. China) was employed to test mRNA production in cells 48 hr after transfection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e2.21. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll \u003cem\u003ein vitro\u003c/em\u003e data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean (SEM). The majority of experiments were repeated two or three times. The hypothesis test for significance between two groups utilized Student\u0026rsquo;s t test. For three or more groups, results were analyzed with one-way analysis of variance (ANOVA). The Chi-square test was used to analyze IHC results and clinical characteristics of CRC patients. All statistical analyses were conducted using GraphPad Prism or SPSS software, and the statistical significance was inferred at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.050.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. YTHDF2 was a pro-tumorigenic factor in CRC and was closely correlated with immune-related pathways.\u003c/h2\u003e\n \u003cp\u003eTo uncover the relationship between m\u003csup\u003e6\u003c/sup\u003eA regulators and CRC development, we systematically evaluated the expression patterns of 19 m\u003csup\u003e6\u003c/sup\u003eA regulators \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e in human CRC and normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The \u0026ldquo;writers\u0026rdquo; included methyltransferase like 3 (\u003cem\u003eMETTL3\u003c/em\u003e), \u003cem\u003eMETTL14\u003c/em\u003e, WT1-associated protein (\u003cem\u003eWTAP\u003c/em\u003e), RNA-binding motif protein 15 (\u003cem\u003eRBM15\u003c/em\u003e), \u003cem\u003eRBM15B\u003c/em\u003e, and zinc finger CCCH-type containing 13 (\u003cem\u003eZC3H13\u003c/em\u003e). The \u0026ldquo;readers\u0026rdquo; comprised YTH domain-containing 1 (\u003cem\u003eYTHDC1\u003c/em\u003e), \u003cem\u003eYTHDC2\u003c/em\u003e, \u003cem\u003eYTHDF1\u003c/em\u003e, \u003cem\u003eYTHDF2\u003c/em\u003e, \u003cem\u003eYTHDF3\u003c/em\u003e, RNA binding motif protein X-linked (\u003cem\u003eRBMX\u003c/em\u003e), heterogeneous nuclear ribonucleoprotein C (\u003cem\u003eHNRNPC\u003c/em\u003e), \u003cem\u003eHNRNPA2B1\u003c/em\u003e, insulin like growth factor 2 mRNA binding protein 1 (\u003cem\u003eIGF2BP1\u003c/em\u003e), \u003cem\u003eIGF2BP2\u003c/em\u003e and \u003cem\u003eIGF2BP3\u003c/em\u003e. The \u0026ldquo;erasers\u0026rdquo; included alpha-ketoglutarate dependent dioxygenase alkB homolog 5 (\u003cem\u003eALKBH5\u003c/em\u003e) and fat mass- and obesity-associated protein (\u003cem\u003eFTO\u003c/em\u003e). The expression levels of 15 m\u003csup\u003e6\u003c/sup\u003eA regulators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050), including the \u0026ldquo;reader\u0026rdquo; \u003cem\u003eYTHDF2\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were clearly higher in CRC tissues than in normal tissues. In contrast, the expression levels of 4 m\u003csup\u003e6\u003c/sup\u003eA regulators were suppressed in CRC tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), including \u003cem\u003eALKBH5\u003c/em\u003e, \u003cem\u003eHNRNPA2B1\u003c/em\u003e, \u003cem\u003eMETTL3\u003c/em\u003e and \u003cem\u003eYTHDC2\u003c/em\u003e. Apart from that, close connections were revealed among the expression of 18 m\u003csup\u003e6\u003c/sup\u003eA regulators and 14 immune-related genes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), which might explain the mechanisms underlying the above expression patterns.\u003c/p\u003e\n \u003cp\u003eTo assess the effects of m\u003csup\u003e6\u003c/sup\u003eA regulators on prognosis, we then stratified patients into groups with high or low expression levels of m\u003csup\u003e6\u003c/sup\u003eA regulators and plotted survival curves (GSE17537, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC and Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The overall survival (OS) and disease-free survival (DFS) probability for patients with lower \u003cem\u003eYTHDF2\u003c/em\u003e expression levels were higher than for those with overexpressed \u003cem\u003eYTHDF2\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). In addition, other m\u003csup\u003e6\u003c/sup\u003eA regulators did not show significant effect on the prognosis of CRC (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050).\u003c/p\u003e\n \u003cp\u003eTo obtain further information about the clinicopathological features of YTHDF2, we collected 98 CRC patient tissues, 73 normal tissues and the corresponding clinical information. It was evident that the expression of YTHDF2 in CRC tissues was higher than that in normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD-E, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher YTHDF2 expression might be associated with older age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), a higher histologic grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and the T stage (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, P\u0026thinsp;=\u0026thinsp;0.019) and, a worse prognosis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF, P\u0026thinsp;=\u0026thinsp;0.014). We also conducted GO enrichment analysis and found that the differentially expressed genes were enriched in biological processes closely related to immunity (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Collectively, these results enabled us to understand how m\u003csup\u003e6\u003c/sup\u003eA regulators, especially YTHDF2, might affect CRC development.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential expression of YTHDF2 in colorectal cancer and normal tissues.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTissue types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYTHDF2 expression, cases (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (43.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55 (56.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjacent tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72 (98.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between YTHDF2 expression and clinicopathological characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYTHDF2 expression, cases (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (69.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (30.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (56.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (59.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (40.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (66.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (33.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (29.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (70.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50 (60.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 (39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (57.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (42.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003csub\u003e1\u003c/sub\u003e-N\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (55.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (57.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (42.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (55.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. YTHDF2-induced CRC tumor progression was independent of the proliferation, migration or invasion of cells.\u003c/h2\u003e\n \u003cp\u003eTo investigate the role of YTHDF2 in CRC progression, we successfully constructed YTHDF2-knockdown RKO cells, which were labeled sh-NC and sh-YTHDF2 (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Through RNA-seq and GO analysis, these affected genes were found to be related to immune-related pathways (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.005). GSEA analysis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE) also shown that lymphocyte migration (Enrichment score = -0.639, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), T-cell migration (Enrichment score = -0.677, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), T-cell mediated immunity (Enrichment score = -0.527, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and T-cell cytokine production (Enrichment score = -0.658, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) were correlated with \u003cem\u003eYTHDF2\u003c/em\u003e expression. Based on these results, an immunodeficient mouse sub-skin model was applied (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Human PBMCs was activated and amplified into T lymphocytes (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eB). The growth and weight of the subcutaneously transplanted tumors in the NSG mice decreased with YTHDF2 knockdown (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.010). Meanwhile, we observed no instances of lethal xenograft versus host disease, which could result in a body-weight loss (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050). Notably, we also established a mouse sub-skin model without PBMCs (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eC-D), in which the suppression of tumor growth caused by YTHDF2 knockdown disappeared (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). We investigated the tumor-infiltrating lymphocytes of subcutaneous tumors through flow cytometry and IHC. A higher count of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.010; Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eE, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050) was observed. Additionally, our research confirmed that YTHDF2 was not related to cell proliferation (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eF, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050), migration or invasion (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050; Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eH-I, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). Hence, these results further indicated that the pro-tumorigenic effect of YTHDF2 was associated with immune interactions rather than cell proliferation, migration or invasion.\u003c/p\u003e\n \u003cp\u003eConsidering the importance of PD-L1 in T-cell function, we employed western blots to detect the relationship between YTHDF2 and PD-L1 in subcutaneous tumors (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). When YTHDF2 expression decreased, PD-L1 expression declined (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050; Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050; Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eJ, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Besides, we explored the expression pattern of \u003cem\u003eYTHDF2\u003c/em\u003e in patients after atezolizumab treatment (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eA). The results showed that patients with higher expression level of \u003cem\u003eYTHDF2\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050) and \u003cem\u003eCD274\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099) were sensitive to atezolizumab treatment. Our data also showed that \u003cem\u003eYTHDF2\u003c/em\u003e and \u003cem\u003eCD274\u003c/em\u003e expression levels were significantly elevated in dMMR (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eB, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CMS1 patients \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eC, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, we also observed that the expression level of \u003cem\u003eYTHDF2\u003c/em\u003e significantly increased in patients with kirsten rat sarcoma viral oncogene (\u003cem\u003eKRAS\u003c/em\u003e) and B-Raf proto-oncogene (\u003cem\u003eBRAF\u003c/em\u003e) mutation (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eD, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010). Based on the above findings, we further investigated whether depleting YTHDF2 enhances the response to immunotherapy with mouse models. NSG mice bearing tumors were treated with control IgG or anti-PD-L1 antibody. YTHDF2 knockdown synergized with anti-PD-L1 treatment, resulting in the inhibition of tumor growth and decrease in tumor weight (Figure S4A-D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). These effects were accompanied by increased CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration (Figure S4E, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). Taking these results together, we could ascertain that YTHDF2 was a key contributor to CRC progression, enabling tumor cells to produce higher expression of PD-L1 and exhibit stronger resistance to T-cell-mediated cytotoxicity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Depletion of YTHDF2 decreased PD-L1 expression.\u003c/h2\u003e\n \u003cp\u003eBased on above results, it was reasonable to hypothesize that YTHDF2 could promote PD-L1-induced immunosuppression. After YTHDF2 knockdown, the protein expression level of PD-L1 decreased notably (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.010; Figure S5A, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050), whereas the mRNA expression level did not change (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050; Figure S5A, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050) in RKO, LoVo and HCT-116 cells. Accordingly, flow cytometry demonstrated that reduced YTHDF2 suppressed PD-L1 that resided on the surface membrane of CRC cells (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.050; Figure S5B, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010). Additionally, 88 CRC tissues were analyzed and divided into groups with high or low PD-L1 expression depending on the median IHC scores (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Higher YTHDF2 expression levels were clearly more common in the group with higher PD-L1 expression levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). Similar results were obtained in 620 CRC tissues from the TCGA database (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The decreased levels of PD-L1 mediated by reduced YTHDF2 could attenuate binding ability between PD-1 and PD-L1 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE-F, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This decrease in binding activity resulted in an increase in T-cell cytotoxicity (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG, P\u0026thinsp;\u0026lt;\u0026thinsp;0.050).\u003c/p\u003e\n \u003cp\u003eConsidering that m\u003csup\u003e6\u003c/sup\u003eA regulators share common target transcripts, we investigated the impact of other regulators on PD-L1 expression (Figure S5C-D). The experimental results showed that knockdown of METTL3, METTL14, ALKBH5, YTHDF1 or YTHDF3 had no influence on PD-L1 expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). At the same time, we found that knockdown of YTHDF2 did not impact the expression levels of METTL3, METTL14, ALKBH5, FTO, YTHDF1 or YTHDF3, suggesting that YTHDF2 regulated its mRNA targets independently (Figure S6A-B, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Identification of potential target mRNAs of YTHDF2.\u003c/h2\u003e\n \u003cp\u003eRecent studies that systematically investigated YTHDF2 have indicated its role as a m\u003csup\u003e6\u003c/sup\u003eA reader that induces mRNA decay \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This finding seemed to contradict our findings regarding the positive relationship between YTHDF2 and PD-L1, we then subjected sh-NC and sh-YTHDF2 RKO cells to RNA, MeRIP and RIP sequencing.\u003c/p\u003e\n \u003cp\u003eAfter performing MeRIP, we found m\u003csup\u003e6\u003c/sup\u003eA peaks mainly clustered at the 3\u0026rsquo;UTR around the stop codon (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-B), coinciding with the distribution characteristics of m\u003csup\u003e6\u003c/sup\u003eA sites on mRNAs in previous studies (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, fold enrichment\u0026thinsp;\u0026gt;\u0026thinsp;2) \u003csup\u003e33\u003c/sup\u003e. We observed 525 transcripts with increased levels and 534 transcripts with decreased levels (Figure S7A-B). Among them, 306 transcripts were defined as significantly upregulated targets (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, logFC\u0026thinsp;\u0026gt;\u0026thinsp;1.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). In addition, we sequenced mRNAs obtained from RIP to identify YTHDF2-binding mRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). A \u0026ldquo;GGACU\u0026rdquo; motif was also identified in sh-NC and sh-YTHDF2 cells (Figure S7C, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was noteworthy that the previously identified m\u003csup\u003e6\u003c/sup\u003eA consensus motif \u0026ldquo;DRACH\u0026rdquo; (where D represents A, G, or U, R represents A or G, and H represents A, C, or U) was validated in our study \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Furthermore, YTHDF2-binding sites followed the same pattern, with a \u0026ldquo;GAACU\u0026rdquo; motif in sh-NC cells and an \u0026ldquo;AGACU\u0026rdquo; motif in sh-YTHDF2 cells (Figure S7C, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which were consistent with the m\u003csup\u003e6\u003c/sup\u003eA consensus motif.\u003c/p\u003e\n \u003cp\u003eTo be considered the target mRNAs of YTHDF2, three screening requirements needed to be met. First, the expression of the target transcripts needed to be upregulated. Second, the target transcripts needed to contain m\u003csup\u003e6\u003c/sup\u003eA modification sites. Third, YTHDF2 needed to bind the target transcripts directly (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). As a result, 77 transcripts were identified as candidates. Next, we eliminated transcripts with low expression, leaving 45 candidates for further analysis (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). To identify connections between 45 candidates and the PD-L1 regulatory network, we did extensive research. \u003cem\u003eSPOP\u003c/em\u003e and SHOC2 leucine rich repeat scaffold protein (\u003cem\u003eSHOC2\u003c/em\u003e) emerged as the most promising candidates. Using IGV, we produced visualizations of m\u003csup\u003e6\u003c/sup\u003eA modification and YTHDF2-binding sites on transcripts. Compared to the input group, \u003cem\u003eSPOP\u003c/em\u003e mRNA had enriched m\u003csup\u003e6\u003c/sup\u003eA modification and YTHDF2-binding sites at the 3\u0026rsquo;UTR (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE), which suggested that YTHDF2 bound at the exact sites where m\u003csup\u003e6\u003c/sup\u003eA sites coexisted. In addition, SRAMP shown that m\u003csup\u003e6\u003c/sup\u003eA resided on the 1 593, 1 611 and 1 633 adenine residues (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). Therefore, it was possible that YTHDF2 recognized and bound to these m\u003csup\u003e6\u003c/sup\u003eA sites on \u003cem\u003eSPOP\u003c/em\u003e mRNA.\u003c/p\u003e\n \u003cp\u003eIn contrast, although MeRIP-seq data revealed that \u003cem\u003eSHOC2\u003c/em\u003e possessed both m\u003csup\u003e6\u003c/sup\u003eA modification and YTHDF2-binding sites mainly at the coding sequence (CDS, Figure S7D), SRAMP generated a slightly different prediction (CDS, Figure S7F). According to the SRAMP prediction, the m\u003csup\u003e6\u003c/sup\u003eA modification sites occurred around the 5\u0026rsquo;untranslated region (5\u0026rsquo;UTR), which weakened the plausibility of \u003cem\u003eSHOC2\u003c/em\u003e being the target mRNA of YTHDF2. While YTHDF2 was also found to accelerate the translation of mRNA, it was noteworthy that the ability of YTHDF2 to bind to \u003cem\u003eCD274\u003c/em\u003e mRNA cannot be completely excluded. The m\u003csup\u003e6\u003c/sup\u003eA methylation sites on \u003cem\u003eCD274\u003c/em\u003e mRNA occurred at the CDS and 3\u0026rsquo;UTR (Figure S7E and S7G), but the binding of YTHDF2 and \u003cem\u003eCD274\u003c/em\u003e mRNA had no difference between the input and RIP groups (Figure S7E). Upon YTHDF2 knockdown, the \u003cem\u003eSPOP\u003c/em\u003e mRNA expression level increased (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG, P\u0026thinsp;\u0026lt;\u0026thinsp;0.050), while the expression level of \u003cem\u003eSHOC2\u003c/em\u003e remained unchanged (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050). Therefore, it was possible that YTHDF2 recognized and bound to \u003cem\u003eSPOP\u003c/em\u003e mRNA. This observation showed no direct binding between YTHDF2 and \u003cem\u003eCD274\u003c/em\u003e mRNA. We presented the preliminary concept of YTHDF2 recognizing m\u003csup\u003e6\u003c/sup\u003eA sites on \u003cem\u003eSPOP\u003c/em\u003e mRNA, binding to them, and accelerating the degradation of \u003cem\u003eSPOP\u003c/em\u003e mRNA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. \u003cem\u003eSPOP\u003c/em\u003e mRNA was a crucial target by which YTHDF2 regulated immune responses.\u003c/h2\u003e\n \u003cp\u003eAs reported in previous studies, PD-L1 was regulated by the Cullin 3-SPOP E3 ligase via proteasome-mediated degradation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Based on these findings, we aimed to demonstrate the existence of the YTHDF2-SPOP-PD-L1 axis in CRC. After inhibition of YTHDF2, the expression of SPOP was obviously upregulated (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.010; Figure S7H, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010). Given the expression pattern and clinical significance of SPOP in CRC, it was unsurprising to discover that \u003cem\u003eSPOP\u003c/em\u003e had a lower expression level in tumor tissue (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in contrast to \u003cem\u003eYTHDF2\u003c/em\u003e. Additionally, patients with elevated \u003cem\u003eSPOP\u003c/em\u003e expression levels had better prognoses in both GSE31595 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, n\u0026thinsp;=\u0026thinsp;37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) and GSE38832 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;122, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085), indicating the tumor suppressor role of \u003cem\u003eSPOP\u003c/em\u003e. To further uncover the effects of SPOP on CRC progression, cell proliferation, migration and invasion assays were also utilized. Our findings suggested that, consistent with YTHDF2, SPOP did not significantly affect any of these processes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD-E, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050).\u003c/p\u003e\n \u003cp\u003eTo determine the possible function of SPOP, we classified patients into high or low \u003cem\u003eSPOP\u003c/em\u003e expression groups. Through GO analysis, we discovered a correlation between \u003cem\u003eSPOP\u003c/em\u003e and immune-related pathways (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). With the help of GSEA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG), we also found significant relationships between \u003cem\u003eSPOP\u003c/em\u003e expression and cytokine secretion (Enrichment score\u0026thinsp;=\u0026thinsp;0.531, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T-cell activation (Enrichment score\u0026thinsp;=\u0026thinsp;0.383, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), T-cell differentiation (Enrichment score\u0026thinsp;=\u0026thinsp;0.430, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), and T-cell proliferation (Enrichment score\u0026thinsp;=\u0026thinsp;0.455, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Furthermore, knockdown of SPOP resulted in an elevated PD-L1 protein expression level (Figure S8A, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050), but had no impact on the PD-L1 mRNA expression level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSPOP\u003c/em\u003e mutation was widely studied \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and previous studies notified the somatic mutation of \u003cem\u003eSPOP\u003c/em\u003e in CRC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Mutant SPOP lost its interactions with substrates, causing the up-regulation of themselves \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Apart from PD-L1, it was reported that the substrates of SPOP also included c-MYC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, BRD2 \u003csup\u003e41\u003c/sup\u003e, myeloid differentiation primary response gene 88 (MYD88) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, interleukin enhancer binding factor 3 (ILF3) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, GLI family zinc finger 2 (GLI2) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, tumor protein p53 binding protein 1 (TP53BP1) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and SET domain containing 2 (SETD2) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We obtained \u003cem\u003eSPOP\u003c/em\u003e mutation and expression data of CRC patients from Pan-Cancer Atlas project (Figure S8B, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;592). It was revealed that the mRNA expression level of \u003cem\u003eCD274\u003c/em\u003e was significantly increased in \u003cem\u003eSPOP\u003c/em\u003e mutant CRC tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, \u003cem\u003eSPOP\u003c/em\u003e mutation did not lead to obvious changes in the expression levels of other substrates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). After YTHDF2 knockdown, protein expression of c-MYC and BRD2 did not show significant changes (Figure S8C, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). We would infer that YTHDF2-mediated SPOP decay did not affect the stability of other substrates in CRC. Moreover, we conducted a further study examining the relationship between PD-L1 and another candidate, SHOC2. PD-L1 exhibited no significant changes upon SHOC2 knockdown (Figure S8D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). These findings supported the hypothesized YTHDF2-SPOP-PD-L1 axis in CRC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. YTHDF2 modulated \u003cem\u003eSPOP\u003c/em\u003e mRNA in a m\u003csup\u003e6\u003c/sup\u003eA dependent manner.\u003c/h2\u003e\n \u003cp\u003eBased on above evidence, YTHDF2 and SPOP had different effects on PD-L1 expression. The combination of siRNAs resulted in unchanged PD-L1 expression, indicating that SPOP knockdown could reverse the decline in PD-L1 induced by si-YTHDF2 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, P\u0026thinsp;\u0026gt;\u0026thinsp;0.050). Given that \u003cem\u003eSPOP\u003c/em\u003e mRNA was identified as a target mRNA of YTHDF2, the next question was whether this impact relied on m\u003csup\u003e6\u003c/sup\u003eA methylation. A dot blot assay did not show an overall reduction in m\u003csup\u003e6\u003c/sup\u003eA modification after YTHDF2 knockdown in CRC cells (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). However, knockdown of YTHDF2 led to a significant increase in the lifespan of \u003cem\u003eSPOP\u003c/em\u003e mRNA in RKO and LoVo cells (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.050). Hence, YTHDF2 did not alter the overall methylation levels but affected the lifespan of its target mRNAs.\u003c/p\u003e\n \u003cp\u003eTo further confirm the observed results, we performed MeRIP-qPCR and RIP-qPCR. Compared to the IgG group, the anti-m\u003csup\u003e6\u003c/sup\u003eA antibody cross-reacted with m\u003csup\u003e6\u003c/sup\u003eA-containing \u003cem\u003eSPOP\u003c/em\u003e mRNA fragments, and the anti-YTHDF2 antibody successfully isolated the YTHDF2-mRNA complex (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As detected by m\u003csup\u003e6\u003c/sup\u003eA site-specific qPCR primers, \u003cem\u003eSPOP\u003c/em\u003e mRNA levels were noticeably decreased after YTHDF2 knockdown in RIP-qPCR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010), whereas there was no decrease observed in MeRIP-qPCR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050). This result confirmed the role of YTHDF2 as a reader in destabilizing mRNA and directing transcripts to the decay machinery. The decrease in \u003cem\u003eSPOP\u003c/em\u003e mRNA expression could be attributed to the binding of YTHDF2 to m\u003csup\u003e6\u003c/sup\u003eA sites located in the 3\u0026rsquo;UTR.\u003c/p\u003e\n \u003cp\u003eTo prove that YTHDF2 functioned by binding directly to target mRNAs, we introduced mutations into the reporter transcript (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE) and assessed the impact on mRNA abundance. The results showed a significant decrease in luciferase activity when m\u003csup\u003e6\u003c/sup\u003eA sites were introduced into the luciferase mRNA (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall luciferase activity increased following YTHDF2 knockdown, indicating a major role of YTHDF2 in promoting mRNA degradation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010). Conversely, mutations in these sites compromised the ability of YTHDF2 to bind to m\u003csup\u003e6\u003c/sup\u003eA sites. These results confirmed that YTHDF2 hindered \u003cem\u003eSPOP\u003c/em\u003e expression in a m\u003csup\u003e6\u003c/sup\u003eA-dependent manner.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious studies have conducted systematic analyses of YTHDF2, focusing on tumor proliferation \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, metastasis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, apoptosis \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, stem cells \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and metabolism \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. YTHDF2 has been found to be either upregulated \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e in acute myeloid leukemia or downregulated in melanoma \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and CRC \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Additionally, YTHDF2 has been found to be both upregulated and downregulated in lung cancer \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, gastric cancer \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and liver cancer \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The contradictory expression pattern of YTHDF2 highlights the functional complexity of this protein. In this study, we confirmed that YTHDF2 was upregulated in CRC tissues, correlated with a poor prognosis and closely related to immune-related pathways. YTHDF2 promoted CRC growth \u003cem\u003ein vivo\u003c/em\u003e but did not affect CRC cell proliferation, migration or invasion ability \u003cem\u003ein vitro\u003c/em\u003e. A higher count of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells was observed in sh-YTHDF2 tumor obtained from NSG mice. High-throughput sequencing data and clinical sample data further supported a positive correlation between YTHDF2 and PD-L1. After YTHDF2 knockdown, the expression level and the subsequent binding ability of PD-L1 was disrupted, potentially playing a role in relieving T-cell repression in the tumor microenvironment. According to the results of our integrated analysis of transcriptome data and \u003cem\u003ein vitro\u003c/em\u003e experiments, YTHDF2 recognized the m\u003csup\u003e6\u003c/sup\u003eA-modified 3\u0026rsquo;UTR of \u003cem\u003eSPOP\u003c/em\u003e mRNA in CRC cells, subsequently inducing the degradation of \u003cem\u003eSPOP\u003c/em\u003e mRNA. Reduced SPOP helped foster the PD-L1/PD-1 response. Thus, we developed the concept and presented the function of the YTHDF2-SPOP-PD-L1 axis in CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), in order to provide a basis for medical practice.\u003c/p\u003e\u003cp\u003eThe relationship between m\u003csup\u003e6\u003c/sup\u003eA regulators and tumor immunotherapy has been widely reported. It was found that YTHDF2 promoted lymphocyte (PD-1\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, Foxp3\u003csup\u003e+\u003c/sup\u003e and CD45\u003cem\u003eRO\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells) infiltration in non-small-cell lung cancer \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. A similar study also reported that the use of Toll-like receptor 9 agonist-conjugated siRNAs specifically targeting YTHDF2 in tumor-associated macrophages enhanced the efficacy of anti-PD-L1 therapy \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. It made targeting m\u003csup\u003e6\u003c/sup\u003eA regulators a pillar of tumor therapy. Highly effective inhibitors of YTHDF2 showed promising application prospects in tumor treatment. Consistently, we also explored the connection between YTHDF2 knockdown and anti-PD-L1 treatment in the supplementary materials. The results showed that reduced YTHDF2 help improve treatment effect (Figure S4). Furthermore, the inhibition of METTL3 and METTL14 has also been shown to improve response to anti-PD-1 treatment by boosting CD8\u003csup\u003e+\u003c/sup\u003e tumor-infiltrating lymphocytes in pMMR/microsatellite instability-low (MSI-L) CRC and melanoma \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In brief, researchers have exploited distinct inhibitors of m\u003csup\u003e6\u003c/sup\u003eA regulators as a form of combination therapy \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In addition, we also found the connections among \u003cem\u003eYTHDF2\u003c/em\u003e expression, CMS status, \u003cem\u003eKRAS\u003c/em\u003e and \u003cem\u003eBRAF\u003c/em\u003e mutation, indicating the involvement of \u003cem\u003eYTHDF2\u003c/em\u003e in different pathological and molecular characteristics (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies have suggested that m\u003csup\u003e6\u003c/sup\u003eA methylation could provide more precise and effective control of mRNA stability during cancer progression. Our study also revealed the m\u003csup\u003e6\u003c/sup\u003eA-dependent \u003cem\u003eSPOP\u003c/em\u003e mRNA recognition and consequent degradation functions of YTHDF2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-F). SPOP is a nuclear speckle-type pox virus and zinc finger protein that plays an extensive role in tumorigenesis. As an adaptor protein for Cullin 3-based E3 ubiquitin ligases, SPOP has been shown to participate in the regulation of PD-L1 abundance by ubiquitination-mediated degradation in prostate cancer \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and esophageal adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This interaction between SPOP and PD-L1 has also been observed in CRC \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, consistent with our findings in this study (Figure S8A). In addition, there is a high expectation that m\u003csup\u003e6\u003c/sup\u003eA-circular RNAs will serve as prognostic biomarkers in CRC \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, and YTHDF2 has been found to be an important recognition receptor for these circular RNAs \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. This suggests that YTHDF2 impacts CRC progression in multiple ways. Despite the discovery of the YTHDF2-SPOP-PD-L1 axis in this study, further research is needed to investigate other mechanisms linking YTHDF2 and PD-L1 expression.\u003c/p\u003e\u003cp\u003eWhile PD-L1 is regulated at the transcription, posttranscription, translation, and post-translation levels \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, increasing evidence has implied that m\u003csup\u003e6\u003c/sup\u003eA modification plays a vital role in the regulation of PD-L1 \u003csup\u003e63\u003c/sup\u003e. The m\u003csup\u003e6\u003c/sup\u003eA writer METTL3, eraser FTO, and reader YTHDF1 have been shown to bind directly to PD-L1 mRNA in breast cancer \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e and colon cancer \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, and METTL14 has been shown to indirectly affect PD-L1 in cholangiocarcinoma \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Previous research has shown that YTHDF1 binds to mRNAs before YTHDF2 \u003csup\u003e67\u003c/sup\u003e. This study demonstrated that YTHDF2 impacted PD-L1 expression in CRC, with no effect of YTHDF1 on PD-L1 (Figure S5C-D). Although the expression level of PD-L1 in CRC tissues failed to predict the effectiveness of treatment, studies have revealed the expression level of PD-L1 is closely related to tumor stage and prognosis \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. And higher PD-L1 expression was more commonly seen in MSI-H tumors than in MSS tumors \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. We also found higher \u003cem\u003eYTHDF2\u003c/em\u003e and \u003cem\u003eCD274\u003c/em\u003e expression in dMMR CRC (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). In addition, preliminary results have suggested that CRC patients with higher CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration would be more likely to benefit from immunotherapy \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Therefore, there might be underlying connections among PD-L1 expression, immune cell infiltration and immunotherapy efficacy in CRC. The sensitivity of antibodies used in IHC, differences in evaluation criteria, and CRC tissue heterogeneity are possible barriers to obtain accurate measurements of PD-L1. More studies are needed to elucidate whether changing the expression of PD-L1 profoundly affects CRC progression and treatment.\u003c/p\u003e\u003cp\u003eHowever, we could not deny the existence of weaknesses in the study. Given that microRNAs \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and HIF-2α \u003csup\u003e75\u003c/sup\u003e have been found to regulate YTHDF2 expression, the cause of aberrant YTHDF2 expression in CRC remains unknown and deserves further investigation. Additionally, the understanding of interactions between immune cells is just the tip of the iceberg. Although we tried to imitate the humanized immune environment with a humanized mouse model, the PBMC populations used were mainly composed of CD3\u003csup\u003e+\u003c/sup\u003e T cells. Regarding the complex and dynamic nature of the immune response, it is important to rule out the impact of other immune cells. Apart from that, we failed to explore the function of somatic mutations in SPOP in CRC because of the low mutation frequency (\u0026lt;\u0026thinsp;5%) \u003csup\u003e22, 38\u003c/sup\u003e, and more samples are needed for further analysis.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe integration of the aforementioned results led us to propose a mechanism involving m\u003csup\u003e6\u003c/sup\u003eA-dependent \u003cem\u003eSPOP\u003c/em\u003e mRNA degradation boosted by YTHDF2. The SPOP-mediated PD-L1 proteasome-dependent degradation is weakened, promoting PD-L1 expression. Collectively, our results underscore the importance of YTHDF2 and suggest that the combination of targeting m\u003csup\u003e6\u003c/sup\u003eA regulators and anti-PD-1/PD-L1 therapy may be effective in treating CRC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3\u0026rsquo;UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003e3\u0026rsquo; untranslated region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5\u0026rsquo;UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003e5\u0026rsquo; untranslated region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eActD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eactinomycin D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eALKBH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ealpha-ketoglutarate dependent dioxygenase alkB homolog 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eone-way analysis of variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eBRAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eB-Raf proto-oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eBRD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ebromodomain containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ec-MYC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emyelocytomatosis viral oncogene homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCCK-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ecell counting kit-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ecoding sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003econsensus molecular subtypes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ecomplete response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ecolorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCTLA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ecytotoxic T-lymphocyte associated protein 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCXCL9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eC-X-C motif chemokine ligand 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCXCL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eC-X-C motif chemokine ligand 10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003e4\u0026rsquo;,6-diamidino-2-phenylindole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003edisease-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003edMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003edeficient mismatch repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eempty vector\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eF-Luc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003efirefly/renilla luciferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003efetal bovine serum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003efold change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFPKM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003efragments per kilo base per million mapped reads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003efat mass- and obesity-associated protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eglyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGLI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eGLI family zinc finger 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003egene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003egene-set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGTEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGZMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003egranzyme A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGZMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003egranzyme B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHAVCR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ehepatitis A virus cellular receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHNRNPA2B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eheterogeneous nuclear ribonucleoprotein A2/B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHNRNPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eheterogeneous nuclear ribonucleoprotein C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHPRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ehypoxanthine phosphoribosyl transferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ehazard ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIDO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eindoleamine 2,3-dioxygenase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIGF2BP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003einsulin like growth factor 2 mRNA binding protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIGF2BP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003einsulin like growth factor 2 mRNA binding protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIGF2BP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003einsulin like growth factor 2 mRNA binding protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIgG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eimmune globulin G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eintegrative genomics viewer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eimmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eILF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003einterleukin enhancer binding factor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eimmune-precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003ei.v\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eintravenous injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ekirsten rat sarcoma viral oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLAG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003elymphocyte activating 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003em\u003csup\u003e1\u003c/sup\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eN\u003csup\u003e1\u003c/sup\u003e-methyladenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003em\u003csup\u003e5\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003e5- methylcytosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003em\u003csup\u003e6\u003c/sup\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eN\u003csup\u003e6\u003c/sup\u003e-methyladenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMeRIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emethylated RNA immunoprecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMETTL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emethyltransferase like 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMETTL14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emethyltransferase like 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emean fluorescence intensity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMSI-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emicrosatellite instability-high\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMSI-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emicrosatellite instability-low\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emicrosatellite stability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emutated-type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMYD88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emyeloid differentiation primary response gene 88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN. S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eno significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003enegative control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003enon-obese oiabetic, severe combined immunodeficiency gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eoverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePBMCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eperipheral blood mononuclear cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ephosphate-buffered saline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eprogressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eprogrammed death-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePD-L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eprogrammed death ligand 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eprogrammed cell death 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003epolyetherimide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003epMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003emismatch repair-proficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003epartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePRF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eperforin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eqRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003equantitative real-time transcription-polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRBM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eRNA binding motif protein 15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRBM15B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eRNA binding motif protein 15B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRBMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eRNA binding motif protein X-linked\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eRNA binding protein immunoprecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRFU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003erelative fluorescence unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003es.c\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003esubcutaneous injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003estable disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003estandard error of mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSETD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eSET domain containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSHOC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eSHOC2 leucine rich repeat scaffold protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eshRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eshort hairpin RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003esiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003esmall interfering RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003especkle type BTB/POZ protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSRAMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003esequence-based RNA adenosine methylation site predictor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTBST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003etris buffered saline with Tween-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTBX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eT-box transcription factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003etumor necrosis factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTP53BP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003etumor protein p53 binding protein 1;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ewild-type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWTAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eWT1 associated protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYTHDC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eYTH domain-containing 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYTHDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eYTH domain-containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYTHDF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eYTH N\u003csup\u003e6\u003c/sup\u003e-methyladenosine RNA binding protein F1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYTHDF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eYTH N\u003csup\u003e6\u003c/sup\u003e-methyladenosine RNA binding protein F2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYTHDF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003eYTH N\u003csup\u003e6\u003c/sup\u003e-methyladenosine RNA binding protein F3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eZC3H13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 426px;\"\u003e\n \u003cp\u003ezinc finger CCCH-type containing 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe isolation of human peripheral blood mononuclear cells was approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (SRRSH 2022-499-01). Ethical approval for human sample collection was obtained from the Ethics Committee of Shanghai Outdo Biotech Company (YB M-05-02). \u0026nbsp;Written informed consents were received from all patients. All experimental procedures for animals were approved by the Ethics Committee of Zhejiang University (ZJU20230100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data relevant to the study are included in the article or supplemental materials. Data are available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Zhejiang Province (LY23H160018) and National Natural Science Foundation of China (81903160).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXian Xu performed the experiments, analyzed the data and wrote the paper; Hao Chen helped with the experiments; Rongjie Zhao conducted and improved the process of bioinformatics analysis, and helped with supplementary experiment; Jiaying Shen, Hongming Pan and Weidong Han designed and supervised the entire project; the remaining study authors participated in the discussion and data interpretation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr Qiyin Zhou (Institute of Translational Medicine; Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. China) for providing guidance on SPOP study. And we thank Yunbin Yao (Animal Center; Sir Run Run Shaw Hospital; Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. China) for his assistance in animal experiments. We would also like to thank the staff members from Core facilities (Zhejiang University School of Medicine; Hangzhou; Zhejiang; P. R. China) for their assistance in data collection and technical support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2025, \u003cstrong\u003e75\u003c/strong\u003e(1)\u003cstrong\u003e:\u003c/strong\u003e 10-45.\u003c/li\u003e\n\u003cli\u003eQiu H, Cao S, Xu R. Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. \u003cem\u003eCancer communications (London, England)\u003c/em\u003e 2021, \u003cstrong\u003e41\u003c/strong\u003e(10)\u003cstrong\u003e:\u003c/strong\u003e 1037-1048.\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;ckelman C, Engelmann BE, Kaprio T, Hansen TF, Glimelius B. 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However, clear evidence explaining the regulatory mechanisms of programmed death ligand 1 (PD-L1) in CRC has been limited. To illustrate the function of YTH N\u003csup\u003e6\u003c/sup\u003e-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) RNA binding protein F2 (YTHDF2), we conducted a comprehensive evaluation of the expression profiling datasets from online databases and clinical samples. A subcutaneous immunodeficient mouse model was utilized to investigate the impact of YTHDF2 on CRC. Western blots, flow cytometry, PD-1/PD-L1 binding and cell killing assays were employed to detect the relationship between YTHDF2 and PD-L1. We utilized RNA sequencing, along with methylated RNA immunoprecipitation (MeRIP) and RNA binding protein immunoprecipitation (RIP) sequencing to analyze mRNA expression, m\u003csup\u003e6\u003c/sup\u003eA methylation level, and target transcripts of YTHDF2. The m\u003csup\u003e6\u003c/sup\u003eA methylation locations of mRNAs were verified using sequence-based RNA adenosine methylation site predictor (SRAMP), MeRIP-qRT-PCR, RIP-qRT-PCR, and a dual-luciferase reporter system. It was found that YTHDF2 was upregulated in CRC tissues, and patients with higher YTHDF2 expression had a worse prognosis. The \u003cem\u003ein vivo\u003c/em\u003e model illustrated that YTHDF2 promoted CRC growth, while \u003cem\u003ein vitro\u003c/em\u003e experiments showed that the inhibition of YTHDF2 expression did not affect cell proliferation, migration or invasion. Mechanistically, interference with YTHDF2 reduced PD-L1 expression and the binding ability between PD-1 and PD-L1. The use of RNA-seq, MeRIP-seq, RIP-seq, and bioinformatics tools confirmed speckle type BTB/POZ protein (SPOP) mRNA as a YTHDF2 target and validated its m\u003csup\u003e6\u003c/sup\u003eA methylation sites. After YTHDF2 knockdown, SPOP mRNA stability increased, causing an increase in SPOP expression and a decrease in PD-L1 expression. This study demonstrated that YTHDF2 might upregulate PD-L1 expression by destabilizing m\u003csup\u003e6\u003c/sup\u003eA-containing SPOP mRNA and promote CRC development. The biological effect of the YTHDF2-SPOP-PD-L1 axis presented a promising target for CRC treatment and provided an approach to enhance the efficacy of anti-PD-1/PD-L1 therapy.\u003c/p\u003e","manuscriptTitle":"Reduced YTHDF2 inhibits PD-L1 expression by stabilizing m6A-containing SPOP mRNA in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 02:13:44","doi":"10.21203/rs.3.rs-7735800/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f153ea79-be30-470c-aaf4-3e29e95b98fb","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56976430,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":56976431,"name":"Biological sciences/Immunology/Immune evasion"}],"tags":[],"updatedAt":"2026-04-01T07:42:24+00:00","versionOfRecord":{"articleIdentity":"rs-7735800","link":"https://doi.org/10.1038/s41419-026-08615-2","journal":{"identity":"cell-death-and-disease","isVorOnly":false,"title":"Cell Death \u0026 Disease"},"publishedOn":"2026-03-24 04:00:00","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-10-28 02:13:44","video":"","vorDoi":"10.1038/s41419-026-08615-2","vorDoiUrl":"https://doi.org/10.1038/s41419-026-08615-2","workflowStages":[]},"version":"v1","identity":"rs-7735800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7735800","identity":"rs-7735800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}