Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology

Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology | 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 Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology Minjing Zhu, Haibei Dong, Yanyan Hu, Xi Xu, Zejun Fang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8306096/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Colorectal cancer(CRC) is one of the most common malignant tumors worldwide. Patients with different immunophenotypes of CRC could achieve different effect of immunotherapy and yield different prognosis. With the advancement of bioinformatics, multi-omics analysis of the variations at both genomics and epigenomics levels helps a lot to provide a molecular basis for immunophenotype. Methods Gene expression and clinical data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We calculated Spearman correlation of CD274 (programmed cell death ligand-1, PD-L1 ) and IFNG (interferon gamma, IFN-γ ) expressions with immune cell fraction, and screened different immune cell types with CD274 and IFNG by Lasso regression analysis. Multi-omics analysis was exploited to screen out candidate genes with differential in genetic and epigenetic landscapes between two CRC subtypes with the greatest difference in immune infiltration. Finally, a risk scoring model was established and the role of candidate genes in prognosis and oncoimmunology was evaluated at the pan-cancer level. Results Two CRC types (cluster A and cluster B) including five subtypes (subclusters A1, A2, B1, B2A, and B2B) were identified by unsupervised clustering analysis. Somatic mutations, CNVs, and DNA methylation differed between subcluster A2 and B2, and analysis of DEGs correlated with CRC immune phenotypes identified FUT9 and MS4A3 as key genes related to CRC immune-phenotypes and prognosis. Furthermore, FUT9 was validated to act as a key gene related to CRC immune escape in vitro. Conclusion The present study established a risk model for CRC immunophenotyping and prognosis, and highlighted the significance of FUT9 and MS4A3 in oncoimmunology of CRC. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology FUT9 MS4A3 Immunophenotyping Multi-omics analyses colorectal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, with incidence and mortality rates ranking third and fourth, respectively 1 , 2 . Approximately 25% of patients with CRC have metastasis at initial diagnosis. Although surgical resection of lesions and use of chemotherapeutic drugs have improved the survival of individuals with CRC, around 50% of patients will develop tumor progression and metastasis, resulting in high mortality rates 3 . The process of tumor metastasis may be affected not only by the genomic changes or behavioral changes of tumor cells, but also by the tumor microenvironment 4 . The malignant progression of a tumor shows a closely coordinated balance between the immune effect and tolerance. Tumor cells can evade immune recognition and immune attack by modifying their own surface antigens or changing the tumor microenvironment. This biological behavior promotes tumor invasion and metastasis 5 . Therefore, there is an urgent need to study the molecular principles driving the formation and maintenance of CRC immunophenotype. In a previous study, we demonstrated that multi-omics analyses of genomic, epigenomic, and transcriptomic variants is highly significant for the identification of CRC driver genes 6 . In this study, we calculated the Spearman correlation coefficients between programmed cell death ligand-1 ( PD-L1 , also known as CD274 ) and interferon gamma ( IFN-γ , hereafter referred to as IFNG ) expression and immune cell fraction, and screened different immune cell types based on CD274 and IFNG expression by least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we conducted integrated analysis of candidate genes based on differential somatic variations, copy number variations (CNV), and DNA methylation data from the two CRC subtypes with the greatest difference in immune infiltration characteristics. Subsequently, we constructed a risk scoring model, which highlighted two prognosis-related genes, FUT9 and MS4A3 , as novel biomarkers for CRC immune phenotypes. Finally, we demonstrated that FUT9 overexpression correlated with an immunosuppressive phenotype of CRC. 2. Materials and Methods 2.1 Data acquisition & preprocessing RNA-Seq, microRNA (miRNA) expression, MET, somatic mutation, and CNV data, were obtained from UCSC Xena ( https://xenabrowser.net/ ), along with corresponding clinical features and prognosis information from patients with CRC. Validation data sets were downloaded from the Gene Expression Omnibus (GEO) database and five GEO datasets (GSE14095, GSE37892, GSE64256, GSE83889 and GSE17538) were selected for further study. 2.2 Molecular immune subtypes identification Immune cell fractions were calculated for each sample using the LM22 gene signature in CIBERSORTx ( https://cibersortx.stanford.edu/ ) 7 . CD274 and IFNG gene expression levels were exploited to evaluate highly associated immune cell fractions by Spearman correlation analysis. Immune cell types most strongly correlated with CD274 and IFNG expression were screened by LASSO regression analysis. Unsupervised cluster analysis was performed to identify immune-related CRC patient subgroups from The Cancer Genome Atlas (TCGA), according to the immune cell types screened as described above. Other independent GEO datasets were used for validation. 2.3 Somatic variation analysis Somatic variation data were obtained from UCSC Xena and TCGA, and significant somatic variations were identified using VarScan 2 8 . Somatic mutations differing between immune subtypes and mutual exclusion or co-occurrence of somatic mutations were analyzed using the R package, Maftools 9 . 2.4 Alternative splicing analysis Sample data from different CRC immune subtypes were downloaded from the percent splice-in (PSI) database ( https://bioinformatics.mdanderson.org/TCGASpliceSeq/ ), and candidate alternatively spliced transcripts identified based on threshold criteria of occurring in at least 75% of samples, with an average PSI for all samples ≥ 0.05. Differentially expressed alternative splicing (DEAS) between different subtypes was identified using a one sided t-test, followed by Benjamini–Hochberg correction to adjust p values; alternative splicing events with adjusted p value < 0.05 were regarded as significant. In addition, splicing variants of FUT9 and MS4A3 were analyzed via TSVdb 10 ( http://www.tsvdb.com/index.html ). 2.5 CNV analysis CNV data were downloaded from UCSC Xena and TCGA. Segment_Mean values > 0.2 were defined as gains and those < − 0.2 as losses. Differential CNVs between CRC immune subtypes were analyzed using CoNVaQ 11 , while BEDTools 12 was used to annotate differential CNV regions to corresponding genes. 2.6 DNA methylation analysis DNA methylation data were downloaded from UCSC Xena and TCGA. Methylation level spectra were obtained after removing absent loci, and sites differentially methylated between two immune subtypes were analyzed using the R package, LIMMA 13 ; sites with |log 2 fold-change (FC)| ≥ 0.4 and adjusted p value < 0.05 were considered to be significantly different. Finally, differentially methylated sites were mapped to corresponding genes for further study. 2.7 Identification of differentially expressed transcripts Differentially expressed messenger RNA (mRNA) and long non-coding RNA (lncRNA) molecules were analyzed using the R package, DESeq 2, applying a threshold of adjusted p < 0.05 and |log 2 FC| ≥ 1. Differentially expressed miRNAs were identified using DESeq 2 as those with p < 0.05 and log 2 |FC| ≥ 0.05. Volcano plots were generated using Bioconductor in R. 2.8 Gene Set Viration Analysis (GSVA) The gene sets “c2.cp.kegg.v7.4.symbols.gmt” and “h.all.v7.4.symbols.gmt” were obtained from MSigDB, then GSVA was applied in R to identify differential pathways among CRC subtypes 14 . Pathways that differed between CRC subtypes with adjusted p values < 0.05 are presented as a heatmap. 2.9 Kaplan–Meier analysis of gene signature from TCGA datasets using the survival and survminer packages in R In this study, we used R software package survival to integrate survival time, survival status and gene expression data, and exploited Cox method to evaluate the prognostic significance of each gene in CRC. 2.10 Pan-cancer immune infiltration Pan-cancer immune infiltration was assessed using SangerBox ( http://sangerbox.com/ ), a comprehensive tool for bioinformatics analysis based on R. First, unified and standardized pan-cancer data (TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499)) were downloaded from UCSC Xena ( https://xenabrowser.net/ ). Second, target gene expression data were extracted for each solid tumor sample. Third, each expression value was subjected to log 2 (x + 0.001) transformation. Fourth, gene expression profiles for each tumor were extracted, mapped the expression profiles for gene symbols, and the R package “ESTIMATE” 15 used to calculate stromal/immune scores for each sample, based on gene expression levels. Finally, Pearson’s correlation coefficient values between genes and immune infiltration scores in each tumor were calculated using the corr.test function in the R software package, psych (version 2.1.6), to identify significantly associated immune infiltration scores. 2.11 Reagents & materials RPMI 1640 medium was purchased from HyClone (Logan, UT, USA). Lipofectamine 3000 was obtained from Invitrogen (CA, USA). Secondary antibodies for western blotting were purchased from Li-COR Biosciences (NE, USA). Small interfering RNAs (siRNAs) targeting human FUT9 or MS4A3 were purchased from GenePharma (Shanghai, China) and transfections were performed according to the manufacturer’s instructions. Information for siRNAs was as followed: sense GAUCUUCAGUCCAAUGGAATT, antisense UUCCAUUGGACUGAAGAUCTT. 2.12 Cell lines & cultures The CRC cell lines, SW620 and Rko, were purchased from the American Type Culture Collection (USA) and cultured in RPMI 1640 supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA). The TALL-104 cell line was also purchased from the American Type Culture Collection and cultured in RPMI 1640 supplemented with 10% (v/v) FBS (Gibco) and 100 U/mL recombinant interleukin-2 (Peprotech, Cat# 200-02). All cell lines were maintained in a 5% CO 2 atmosphere at 37°C. 2.13 Western blot and immunohistochemistry (IHC) Western blot, IHC staining, and IHC score evaluation were performed as previously described 6 . Tissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissue samples was obtained from Sanmen People’s Hospital on 26/07/2025 and authorized by Sanmen People’s Hospital Ethics Committee (2025-064). Primary antibodies against GAPDH (1:5000, Proteintech, Cat. #60004-1- Ig) and FUT9 (1:500, Proteintech, Cat. #60230-1-Ig) were utilized for western blot, and FUT9 (1:100, Proteintech, Cat. #60230-1-Ig) for microarray chip was used for IHC staining. 2.14 Apoptosis and cytotoxicity assay To evaluate apoptosis, 1 × 10 5 tumor cells were cocultured with 3 × 10 6 TALL-104 cells for 48 h. Next, TALL-104 cells were detected by flow cytometry using an Annexin V-FITC/PI apoptosis kit (MultiSciences, Cat. #AP101). To assess cytotoxicity, 1 × 10 4 tumor cells were cocultured with 5 × 10 4 TALL-104 cells for 48 h in 96-well plates. Lactate dehydrogenase (LDH) released from target cells into the cell-free supernatant was detected using an LDH cytotoxicity detection kit (Genmed, Cat. # GMS10073.1), following the manufacturer’s instructions. The amount of LDH released was used to assess the lysis of target cells, which can be translated as the effectiveness of effector cells. Percentage cytotoxicity was calculated according to optical density (OD) values using the following formula: Cytotoxicity (%) = (Experimental − Effector spontaneous − Target spontaneous) / (Target maximum − Target spontaneous) × 100% 2.15 Statistical analysis Statistical analyses were performed with the GraphPad software(Prism 8) and R software (version 3.5.1). False discovery rate was adjusted through the Benjamini–Hochberg procedure. Student’s t-test was used to compare the differences in expression levels between groups. Overall survival (OS) curves were generated by Kaplan–Meier analysis and compared with the log-rank test. Univariate and multivariable Cox proportional-hazards regression models were executed to calculate hazard ratios (HRs) and 95% confidence intervals. Receiver operator characteristic (ROC) curves and area under the curve (AUC) were exploited to calculate the diagnostic efficacy. For in vitro experiments, at least three replicates were performed. P values < 0.05 were considered statistically significant. 3. Results 3.1 Identification of molecular immune subtypes related to CD274 and IFNG expression in CRC Gene expression data were obtained from TCGA and four independent datasets: GSE14095, GSE37892, GSE64256, and GSE83889, of which, TCGA data set was a training cohort and the GSE data sets were validation cohorts. Subsequently, CIBERSORTx (model = absolute, permutations = 1000, LM22 signature) was used to calculate immune cell fractions of 22 immune cell types within individual samples (Fig. 1 A). Associations of CD274 and IFNG gene expression with immune cell fractions were evaluated by calculating Spearman correlation coefficient values. In TCGA, nine immune cell types were significantly correlated with CD274 or IFNG expression, and the most relevant of these were screened by LASSO regression analysis. The results showed that five immune cell types, CD8 T cells, CD4 memory activated T cells, resting NK cells, M1 macrophages, and neutrophils, were the most relevant. Therefore, these cell types were selected for further study (Fig. 1 B, C). Based on the five immune cell types mentioned above, TCGA-COADREAD cohort immune subtypes were identified by unsupervised clustering analysis. TCGA-COADREAD cohort data could be divided into two types, cluster A and B, comprising five immune subtypes: A1, A2, B1, B2A, and B2B. As shown in Fig. 1 D, cluster A displayed a lower cytotoxic immune-phenotype than cluster B. Among cluster A, subcluster A1 showed slightly elevated neutrophil-related cytotoxic immune-phenotype compared to subcluster A2. In addition, in cluster B, subcluster B1 showed a higher CD8 T cell-related immune-phenotype but a lower M1 macrophage-related immune-phenotype compared to subcluster B2. Furthermore, subcluster B2A showed the highest M1 macrophage-related immune-phenotype while also showing a relatively higher CD8 T cell-related immune-phenotype and resting NK cell-related immune-phenotype among the five immune subtypes. Therefore, we observed the lowest cytotoxic immune-phenotype in subcluster A2 while subcluster B2, particularly subcluster B2A, showed the highest cytotoxic immune-phenotype. Additional data were obtained from GSE14095, GSE64256, and GSE83889 datasets to be used as validation cohorts. Results of molecular immune subtype identification on these validation cohorts also illustrated that CRC samples could be divided into two types, clusters A and B, which comprised subcluster A1, A2, B1, B2A, and B2B (Supplementary Fig. 1A–C). Next, expression of CD274 and IFNG genes were investigated in each TCGA-COADREAD cohort immune subtype. We found that subcluster A2 had the lowest cytotoxic immune-phenotype while subcluster B2, particularly subcluster B2A, expressed the highest levels of CD274 and IFNG , relative to the other subtypes (Fig. 1 E and 1 F). The typical molecular and clinical characteristics of these five immune subtypes were illustrated and we found significant differences in microsatellite instability (MSI) distribution among the different subgroups (Supplementary Fig. 1D). 3.2 Identification of immunophenotype-related somatic mutations in CRC To study differences in somatic mutations between different immunophenotypes, we first downloaded the somatic mutation data from TCGA to analyze the total mutation load in the different TCGA-COADREAD cohort immune subtypes. We found that somatic mutation characteristics differed among the subgroups (Fig. 2 A), with subcluster A2 and B2A having the lowest and highest total mutation load, respectively. This suggested that different immune status of subclusters might correlate with different somatic mutation status. Next, we used Maftools to analyze somatic mutation characteristics of each immune subtype, and the distribution of major oncogenes in each subtype was plotted. The results revealed that the mutation frequency of APC was higher in cluster A than in cluster B, of which, subcluster B2A had the lowest APC mutation frequency. Moreover, KMT2D , a methylation-related gene, showed the highest and lowest mutation frequency in subcluster B2A and A2, respectively, which suggests that the immune status of these subclusters may be linked to DNA methylation (Fig. 2 B–F). In addition, we analyzed the concurrent and mutually exclusive mutations of somatic mutations in each immune subtype, and the top 25 mutations in each immune subtype were mapped. Our data show that compared to subclusters A1 and A2, subcluster B2A exhibits more concurrent mutations (Supplementary Fig. 2A–E). 3.3 Analysis of alternative splicing events Alternative splicing events always occur during tumorigenesis; therefore, we analyzed differential alternative splicing events between CRC subtypes. Subclusters A2 and B2 subtype sample data were downloaded from the PSI database ( https://bioinformatics.mdanderson.org/TCGASpliceSeq/ ). Only alternative splicing events meeting the following screening conditions were included: (1) occurred in at least 75% of the samples; and (2) average PSI in all samples ≥ 0.05. A total of 33371 alternative splicing events from 9918 genes were screened (Fig. 3 A). Differential alternative splicing events between subcluster A2 and B2 subtypes were identified by t-test, followed by Benjamini–Hochberg P value correction; alternative splicing events with a p < 0.05 after Benjamini–Hochberg correction were considered significantly different. Finally, 1238 significantly different alternative splicing events were screened (Fig. 3 B), of which 719 and 519 were upregulated in subcluster A2 (Fig. 3 C) and subcluster B2 (Fig. 3 D) subtype tumors, respectively. 3.4 Comprehensive analysis of DEGs, CNV-disrupted genes, & DNA methylation in CRC Subclusters A2 and B2 were selected as the two most significantly different immune subtypes and analyzed using the R package “DESeq2” to identify DEGs with |log 2 FC| ≥ 1 and P < 0.05. A total of 929 DEGs were identified, of which, 56 and 873 were upregulated in subcluster A2 and B2, respectively (Fig. 4 A and Supplementary Table 1). Further, GSVA enrichment analysis showed that 16 pathways were significantly differentially enriched between subcluster A2 and B2, including the immune-related pathways, cytokine—cytokine receptor interaction, JAK/STAT signaling, and chemokine signaling (Fig. 4 B). Next, we downloaded CRC CNV data from TCGA and defined Segment_Mean value > 0.2 as copy number gain and Segment_Mean value < − 0.2 as copy number loss. CoNVaQ was used to analyze differential CNV regions between subcluster A2 and B2, and we performed gene annotations of differential CNV regions using bedtools, resulting in 32 DEGs annotated within the CNV regions with a P value < 0.001 (Fig. 4 C, Supplementary Table 2 and Supplementary Table 3). Finally, DNA methylation data were downloaded from UCSC Xena and TCGA. Methylation expression spectra were obtained after removing absent loci, and sites that were differentially methylated between two immune subtypes were analyzed. We used the R package “limma” to screen for differentially methylated sites between subcluster A2 and B2. A total of 7344 differentially methylated sites with an adjusted p value < 0.05 and |log 2 FC| ≥ 0.4 were identified, of which 5894 were hypermethylated in subcluster A2 and 1450 in subcluster B2 (Fig. 4 D- 4 E). Annotation information for all methylation sites was shown in Supplementary Table 4. 3.5 Construction of a DEG-miRNA-lncRNA regulatory network The competitive endogenous RNA (CeRNA) theory hypothesizes that mRNAs, pseudogenes, lncRNAs, and circular RNA may bind competitively with miRNA through miRNA response elements (MREs), to hinder miRNA inhibition of coding RNA, thereby upregulating target gene expression 16 . According to this hypothesis, lncRNAs restrain miRNA and promote miRNA-related downstream genes by acting as sponges 17 . Thus, we constructed a DEG-miRNA-lncRNA regulatory network. A total of 66 DEMs were identified between subcluster A2 and B2, including 27 and 39 upregulated DEMs in subcluster A2 and B2, respectively (Fig. 5 A). Target genes of these DEMs were predicted using the miRDB, miRTarBase, and TargetScan databases. Next, we merged upregulated DEM-related DEGs in subcluster A2 and B2, and filtered candidate DEM-DEG regulatory networks using the threshold criterion and DEM-DEG regulatory networks were simultaneously predicted in at least two databases. We found that 257 and 310 DEM-related DEGs were upregulated in subcluster A2 and B2, respectively (Fig. 5 B, C). We also analyzed DELs between subcluster A2 and B2 using DESeq2 and identified 281 DELs, of which six were upregulated in subcluster A2 and 275 in subcluster B2 (Fig. 5 D). Analysis of data from the MiRCode database detected 2122 DEL-related DEMs, and target genes of these miRNAs were predicted using the miRDB, miRTarBase, and TargetScan databases. Venn diagrams were generated and illustrated that 83 DEL-related DEM-DEG regulatory networks were upregulated in subcluster A2, while 1261 were elevated in subcluster B2; all candidate regulatory networks identified by analysis of Venn diagrams were screened using the threshold criterion that DEL-related DEM-DEG regulatory networks were simultaneously predicted in at least two databases (Fig. 5 E and 5 F). Finally, a ceRNA network was constructed to visualize the regulatory correlations among DELs, DEMs, and DEGs (Fig. 5 G). 3.6 Evaluation of immunotherapy effects and survival analysis We intersected our data on differentially methylated genes, CNV-disrupted genes, DEM-related genes, and DEL-related genes, resulting in screening of 47 candidate DEGs that appeared in at least three data types (Fig. 6 A). Subsequently, we predicted the immunotherapeutic responses of subcluster A2 and B2 subtype samples using the TIDE (Tumor Immune Dysfunction and Exclusion) analytic tool ( http://tide.dfci.harvard.edu/ ) 18 , and investigated the distribution of TIDE fractions between subcluster A2 and B2 (Supplementary Fig. 3A and Supplementary Table 5). We also conducted Cox regression survival analysis according to the expression levels of the 47 DEGs screened in the previous step. LASSO cox regression analysis indicated that FUT9 and MS4A3 showed a significant impact on overall survival. Further, combined with result of LASSO cox regression, we established a multifactor prognosis prediction model and a risk score formula was constructed as follows: Risk score = (0.277 × FUT9 ) – (0.508 × MS4A3 ) Risk scores for each sample were then calculated based on the risk score formula, and samples divided into high and low risk groups, according to median risk score (Fig. 6 B). As illustrated in Fig. 6 C, patients with higher risk scores had shorter survival times and increased disease recurrence rates. Further, Kaplan–Meier survival analysis demonstrated unfavorable prognosis for CRC patients with high risk scores (Fig. 6 D). Multivariable Cox regression analysis demonstrated risk score as an independent risk factor (Fig. 6 E). Distributions of CRC patients by the two molecular immune subtypes, two risk score groups, and prognosis status are shown in Fig. 6 F. Principal component analysis indicated that dimensions were discernible between the two risk score groups (Fig. 6 G). In addition, to developing a prognostic prediction model for clinical application, a nomogram including risk score and clinicopathological parameters was established to predict the prognosis of patients with CRC (Fig. 6 H). The resulting AUC values showed that our model had a better classification effect than random choice, while the nomogram showed superior value for predicting prognosis at 5 years (AUC = 0.650) (Fig. 6 I). In addition, we validated our model on GSE17538 and our model successfully predicted prognosis of CRC patients in the GSE17538 cohort (Supplementary Fig. 3B and 3C). 3.7 High FUT9 expression is correlated with CRC immunosuppressive phenotype Our risk score formula demonstrated that patients with CRC and higher FUT9 expression levels had unfavorable prognosis, indicating that FUT9 may be a novel biomarker for CRC immune-phenotype and patient prognosis. Therefore, we explored the biological function and prognostic significance of FUT9 in CRC. A tissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissues samples was used for IHC validation experiments. FUT9 levels were higher in tumor than in normal tissues (Fig. 7 A). We also assessed the effect of FUT9 on CRC oncoimmunology in vitro. FUT9 gene expression levels in different CRC cell lines were obtained from the Cancer Cell Line Encyclopedia database ( https://portals.broadinstitute.org/ccle ); FUT9 was expressed at higher levels in Rko cells, while its expression was lower in SW620 cells. Therefore, we overexpressed FUT9 in SW620 cells and knocked down its expression in Rko cells. Western blot demonstrated that FUT9 was overexpressed in SW620 cells efficiently, meanwhile siRNA- FUT9 -1 downregulated FUT9 most significantly and was exploited for further study (Fig. 7 B). To evaluate the effect of FUT9 on anti-T cell killing ability, CRC cells were co-cultured with TALL-104, a human acute T lymphocyte leukemia cell line with high CD8 + T cell cytotoxicity 19 for further study. FUT9 induced CRC resistance to CD8 + T cell cytotoxicity (Fig. 7 C). In addition, overexpression of FUT9 reduced apoptosis of CRC cells co-cultured with TALL-104 cells (Fig. 7 D and 7 E). Overall, these results illustrate that FUT9 expression correlates with an immunosuppressive phenotype in CRC. 3.8 Overview of FUT9 and MS4A3 in prognosis and oncoimmunology at the pan-cancer level To explore the broader prognostic and oncoimmuological value of FUT9 and MS4A3, we conducted pan-cancer analysis of FUT9 and MS4A3 in solid cancers. High FUT9 expression was correlated with unfavorable prognosis in CRC (hazard ratio (HR) = 2.11, 95% confidence interval (CI) 1.05–4.24), thyroid carcinoma (HR = 2.86, 95% CI: 1.06–7.70), uterine corpus endometrial carcinoma (HR = 2.57, 95% CI: 1.15–5.76), stomach adenocarcinoma (HR = 1.74, 95% CI: 1.18–2.55), and uterine carcinosarcoma (HR = 2.20, 95% CI: 1.09–4.44), but was associated with better survival in bladder urothelial carcinoma (HR = 0.73, 95% CI: 0.54–0.99), sarcoma (HR = 0.58, 95% CI: 0.37–0.90), glioma (HR = 0.17, 95% CI: 0.13–0.22), and brain lower grade glioma (HR = 0.35, 95% CI: 0.24–0.51) (Fig. 8 A–I). We obtained 22 types of immune cell infiltration scores of 35 forms of solid cancer samples. We calculated the Pearson's correlation coefficient between FUT9 and immune cell infiltration score in each type of cancer to determine the significant related immune infiltration score. Finally, we found that FUT9 levels were significantly associated with at least one type of immune cell infiltration in 33 types of solid cancers, but had no significant correlation with immune infiltration in mesothelioma and uterine carcinosarcoma (Fig. 8 J). FUT9 expression was significantly positively associated with the stromal score in CRC (R = 0.19, P = 2.0e-4) and prostate cancer (R = 0.26, P = 3.1e-9), while it was significantly negatively correlated with the stromal score in 12 other cancers (Supplementary Fig. 4). In addition, FUT9 expression was significantly positively associated with the immune score in prostate cancer (R = 0.23, P = 3.4e-7) and thyroid cancer (R = 0.11, P = 0.01), while it was significantly negatively correlated with the immune score in 13 other cancers (Supplementary Fig. 5). High MS4A3 expression was positively correlated with shorter survival time in patients with glioma (HR = 3.39, 95% CI: 2.37–4.85), while it was associated with better survival in CRC (HR = 0.54, 95% CI: 0.34–0.87) (Fig. 9 A and 9 B). Furthermore, we also obtained 22 types of immune cell infiltration scores of 35 types of solid cancer samples and calculated the Pearson's correlation coefficient between MS4A3 along with the immune cell infiltration score in each type of cancer to determine the significant related immune infiltration score. MS4A3 levels were significantly correlated with at least one kind of immune cell infiltration in 33 types of solid cancers, but had no significant correlation with immune infiltration in mesothelioma and uveal melanoma (Fig. 9 C). Furthermore, MS4A3 was positively associated with the immune score in 23 tumors, including CRC (R = 0.34, P = 7.7e-12) (Supplementary Fig. 6). Because our findings suggest the involvement of somatic mutations and alternative splicing events in CRC oncoimmunology, we sought to understand whether FUT9 and MS4A3 underwent somatic mutations or alternative splicing. Our results demonstrated that FUT9 , but not MS4A3 , showed significant single-nucleotide variants (missense mutation) in subcluster B2 subtype CRC (Fig. 8 K and Supplementary Fig. 7A). Further, MS4A3 had three transcript variants (uc001nom.3, uc001non.3, and uc001noo.3), only one of which (uc001noo.3) was expressed at significantly higher levels in normal tissues compared to cancer tissues (Fig. 9 D and Supplementary Fig. 7B and 7C); the distribution of MS4A3 transcript variant 3 (uc001noo.3) between subcluster A2 and B2 CRC subtypes is illustrated in Fig. 9 E. 4. Discussion With the development of resistance in traditional cancer therapies, molecular targeted therapy and immune checkpoint inhibitors have become the focus of considerable research efforts. However, patients with differing tumor infiltrating immune cells achieve varying therapeutic efficacy. In recent years, some studies have roughly divided tumors into “cold tumors”, with few immune cells and a large proportion of immunosuppressive cells, and “hot tumors”, with increased infiltration of activated immune cells, such as CD8 + T cells and Th1 cells, according to their immune cell infiltration characteristics 20 . Clinical trials have shown that patients with “hot tumors” can achieve better responses to immunotherapy. Although this tumor immunophenotyping method provides researchers with a more accurate approach, its application in CRC has been limited because many patients with CRC are diagnosed at an advanced stage. Moreover, it is impractical to conduct tumor IHC staining for tumor immunophenotyping by taking tissue sections from each patient. Hence, identification of the molecular classifications and the immune characteristics of CRC through bioinformatic methods are important for CRC prognosis. Mutations in genes often occur at the transcriptional or post-transcriptional levels during tumorigenesis, with somatic mutations, copy number variations (CNVs), and DNA methylation playing vital roles during tumorigenesis 21 – 23 . Therefore, integrative analyses of multi-omics data have been useful for the identification of tumor-related biomarkers. In this study, we performed integrative analyses of multi-omics data obtained from TCGA and the GEO database. After evaluating associations between CD274 and IFNG gene expression and immune cell fractions, two CRC types (cluster A and cluster B) including five subtypes (subclusters A1, A2, B1, B2A, and B2B) were identified by unsupervised clustering analysis. Among these subtypes, subcluster A2 showed a lower cytotoxic immune-phenotype, while subcluster B2 exhibited a higher cytotoxic immune-phenotype. Integrative analysis indicated that somatic mutations, CNVs, and DNA methylation differed between subcluster A2 and B2, and analysis of DEGs correlated with CRC immune phenotypes identified FUT9 and MS4A3 as key genes related to CRC immune-phenotypes and prognosis. MS4A3 (membrane spanning 4-domains A3)—also known as CD20L or HTM4 —is a member of the membrane-spanning 4A gene family, which was first identified by Liang et al. in 2001 24 . Microarray, RT-PCR, and immunofluorescence studies demonstrated that MS4A3 is preferentially expressed in basophiles, rather than other granulocytes, B cells, or T cells 25 , 26 . In addition, MS4A3 is expressed in breast ductal epithelium, testis (seminiferous tubules and rete), pancreas, prostate, stomach, thymus, and hematopoietic cells within fetal liver. In hematopoietic cells, MS4A3 functions as a cell cycle regulator that modulates G1/S transition through CDKN3 / KAP -dependent phosphorylation of CDK2 27 . Therefore, MS4A3 is an important regulator of the hematopoietic cell cycle. Heller et al. 28 found that MS4A3 may be downregulated by EVI1 at the transcriptional level, and that MS4A3 knockdown can promote lymphoma proliferation. MS4A3 was also shown to induce chronic myeloid leukemia differentiation through cytokine receptor endocytosis 29 . In addition, although MS4A3 is mainly expressed in hematopoietic cells, its expression in prostate, ovarian, and breast cancer differ significantly from those in normal tissues, suggesting that MS4A3 may play a role in tumorigenesis 25 . In the present study, we demonstrate that MS4A3 can act as a novel biomarker for immune-phenotype analysis in CRC and that increased MS4A3 expression is correlated with improved prognosis of patients with CRC. FUT9 (Fucosyltransferase 9) primarily functions as a modifier during recognition of selectin on endothelial cell surfaces, mediated by sialyl Lewis(x) antigen expressed on the leukocyte surface. FUT9 is mainly distributed in the brain, stomach, and spleen 30 , and abnormal FUT9 expression is often closely correlated with immune system disorders, such as inflammation 31 . Recently, the involvement of FUT9 during tumorigenesis has become the focus of considerable attention. Survival analysis, based on microarray mRNA expression data, illustrated that FUT9 expression was an unfavorable prognostic factor, and that high FUT9 levels were associated with decreased overall survival of ovarian cancer patients after intraperitoneal injection compared to intravenous injection 32 . In addition, FUT9 is downregulated in a Helicobacter pylori -associated gastric cancer relative to patients with atrophic gastritis 33 . Regarding CRC, FUT9 has been revealed to be a double-edged sword during CRC tumorigenesis. Auslander et al. 34 illustrated that, although FUT9 inhibited CRC proliferation and metastasis, it promoted CRC tumor-initiating cells and acted as a metabolic driver of advanced-stage CRC. Further, Blanas et al. 35 demonstrated that FUT9 can augment the cancer stemness characteristics of CRC. In the present study, we show that high FUT9 expression is correlated with unfavorable prognosis and using in vitro experiments, we also show that FUT9 has an immunosuppressive effect on CRC. The strength of the present study lies in the application of multi-omics analyses to identify candidate genes, and our data suggest that FUT9 and MS4A3 are potential novel biomarkers for CRC immune-phenotype. Functionally, our in vitro experiments demonstrated an immunosuppressive role for FUT9 . To our knowledge, this is the first report of the prognostic value of FUT9 and MS4A3 in CRC, and we also assessed the biological role of FUT9 in CRC immunology. Our study highlights the value of novel immune phenotype-related genes and potential regulatory noncoding RNA networks in CRC. Methods aimed at detecting these features may provide novel strategies for improving prognoses and immune therapy efficiency for patients with CRC. The present study also has several limitations. We only validated the biological and clinical significance of FUT9 , and additional validation study for MS4A3 is needed. Further, the mechanistic aspects of FUT9 and MS4A3 were not explored in detail. In addition, we constructed a ceRNA network for CRC-related genes, but did not confirm the regulation patterns of lncRNA, miRNA, and mRNA within the predicted ceRNA network. Therefore, additional studies to elucidate these underlying mechanisms should be conducted. Furthermore, tumor infiltrating lymphocytes (TIL) have an important impact on CRC invasion, metastasis, and prognosis 36 – 38 . Among them, TILs with CD3 + , CD4 + , and CD8 + are closely related to CRC prognosis 39 – 41 . Hence, additional correlation analysis of FUT9 , MS4A3, CD3 , CD4 , and CD8 should be carried out in further studies. We believe that such studies will provide a better understanding of the mechanisms involved in CRC progression. 5. Conclusion Our research focused on establishing a risk model for CRC immunophenotyping and prognosis through multi-omics analysis, and we highlight FUT9 and MS4A3 as novel immunophenotyping-related genes for CRC. Our study underscores the value of novel immunophenotyping-related genes and explores the immunosuppressive role of FUT9 while also laying a foundation for further studies. Declarations Availability of data and materials All data supporting the conclusions of this article are included within the article and its supplementary files. Further inquiries can be directed to the corresponding authors. Ethics approval and consent to participate Tissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissue samples was obtained from Sanmen People’s Hospital. The study protocol was approved by Sanmen People’s Hospital Ethics Committee (2025-064). All experiments were performed in compliance with the relevant regulations, and all patients provided written informed consent. Funding This work was supported by the National Natural Science Foundation of China (grant number: 82303594), Zhejiang Medical and Health Science and Technology Plan (grant number: 2024XY091 and 2019RC175) and Zhejiang Provincial County Level Advantageous Disciplines of Traditional Chinese Medicine Construction Plan (2023-XK-D040). Authors’ Contributions ZF and XX conceived the idea and handled bioinformatic analyses, designed and monitored the research. MZ wrote the main manuscript text and prepared the figures and tables. MZ, HD and YH measured FUT9 expression, MZ and YH assessed and confirmed the staining results in clinical tissue samples. All authors contributed to the article and approved the submitted version. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgements We acknowledge public databases including TCGA and GEO for providing their platforms and contributors for uploading their meaningful datasets. We thank Charlesworth Author Services for the English language editing of the article. References Sanchez-Gundin, J., Fernandez-Carballido, A. M., Martinez-Valdivieso, L. & Barreda-Hernandez, D. Torres-Suarez, A. I. 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Supplementary Files Supplementarylegends.docx SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx OrinigalDataofWesternBlot.tif SupplementaryFigure1.tif SupplementaryFigure2.tif SupplementaryFigure3.tif SupplementaryFigure4.tif SupplementaryFigure5.tif SupplementaryFigure6.tif SupplementaryFigure7.tif Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 24 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 13 Dec, 2025 Editor invited by journal 11 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 08 Dec, 2025 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. 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(E) Heatmap of differentially methylated sites between subcluster A2 and subcluster B2 subtypes\u003cstrong\u003e. \u003c/strong\u003e* \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/c1b4ad2ef802e4a7bc6f2341.png"},{"id":97898063,"identity":"4070817a-e623-4b5a-a1ec-666d41c3e38a","added_by":"auto","created_at":"2025-12-10 15:38:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19469935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDEGs-microRNA-lncRNA regulatory network construction.\u003c/strong\u003e (A) Volcano plot of differentially expressed microRNAs between subcluster A2 and subcluster B2 subtypes. (B) Venn plot of microRNA-targeted genes upregulated in subcluster A2. (C) Venn plot of microRNA-targeted genes upregulated in subcluster B2. (D) Volcano plot of differentially expressed lncRNAs between subcluster A2 and subcluster B2 subtypes. (E) and (F) Venn plot for the prediction of microRNA-gene links based on differentially expressed lncRNAs between (E) subcluster A2 and (F) subcluster B2 subtypes. (G) Competing endogenous RNA network related to DEGs-microRNA-lncRNA regulation.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/ab88539d325c373683fea4af.png"},{"id":97897951,"identity":"0800f20c-e1cd-414d-89ac-3a82acd0ce53","added_by":"auto","created_at":"2025-12-10 15:38:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3065609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunotherapy effect evaluation and survival analysis.\u003c/strong\u003e (A) Venn diagram of DEGs related to differential methylation, differential CNV region, differential expression of microRNA, and differential expression of lncRNA. (B) Distribution of risk score in CRC patients. (C) Survival status of CRC patients along with the risk score distribution. (D) Kaplan–Meier analysis of the prognosis between high- and low-risk groups. (E) Forest plot of multivariable Cox regression analysis demonstrated risk score as an independent risk factor in TCGA-COADREAD. (F) Alluvial diagram of subtype distributions in groups with different risk scores and prognoses. (G) PCA analysis of dimensions between two risk score-related groups. (H) Nomogram for predicting the prognosis of CRC patients. (I) ROC analysis for 1-, 3- and 5-year survival using the predictive nomogram.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/9341528636327c0dc1987285.png"},{"id":97895461,"identity":"89f5476a-1d05-4a3f-8529-082d33ce374b","added_by":"auto","created_at":"2025-12-10 15:34:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7137538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunosuppressive role of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFUT9\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e during CRC tumorigenesis.\u003c/strong\u003e (A) Expression of \u003cem\u003eFUT9\u003c/em\u003e in CRC tissues and adjacent normal tissues. (B) Overexpression of \u003cem\u003eFUT9\u003c/em\u003e in SW620 cell and knockdown of \u003cem\u003eFUT9\u003c/em\u003e in RKO cell. (C) Cytotoxicity assay of SW620-NC/\u003cem\u003eFUT9\u003c/em\u003e with/without cocultured with TALL-104 cells, RKO-NC/si\u003cem\u003eFUT9\u003c/em\u003ewith/without cocultured with TALL-104 cells. (D) Flow cytometry apoptosis analysis of SW620-NC/\u003cem\u003eFUT9\u003c/em\u003ewith/without cocultured with TALL-104 cells. (E) Flow cytometry apoptosis analysis of RKO-NC/si\u003cem\u003eFUT9\u003c/em\u003ewith/without cocultured with TALL-104 cells. ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/34ca4417d264fb52b80c00b6.png"},{"id":97897607,"identity":"7bc17e44-c90d-458c-9bcf-8db1bffac8fd","added_by":"auto","created_at":"2025-12-10 15:37:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2959884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFUT9 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein cancer prognosis and oncoimmunology. \u003c/strong\u003eKaplan–Meier survival analyses were performed to evaluate the association between \u003cem\u003eFUT9\u003c/em\u003eexpression and survival status in TCGA of different tumors, in which (A) CRC, (B) thyroid carcinoma, (C) uterine corpus endometrial carcinoma, (D) stomach adenocarcinoma, (E) uterine carcinosarcoma, (F) bladder urothelial carcinoma, (G) sarcoma, (H) glioma, and (I) brain lower grade glioma were significant. (J) Correlations of \u003cem\u003eFUT9\u003c/em\u003e and immune cell phenotypes at the pan-cancer level. (K) Somatic mutation location of \u003cem\u003eFUT9\u003c/em\u003e. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/7e34084d9edc14299ee0ba17.png"},{"id":97896445,"identity":"cf0759aa-7891-4991-b372-884572998c9e","added_by":"auto","created_at":"2025-12-10 15:36:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4119260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMS4A3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in cancer prognosis and oncoimmunology. \u003c/strong\u003eKaplan–Meier survival analyses were performed to evaluate the association between \u003cem\u003eMS4A3\u003c/em\u003eexpression and survival status in TCGA of different tumors, in which (A) glioma and (B) CRC were significant. (C) Correlations of \u003cem\u003eMS4A3\u003c/em\u003e and immune cell phenotypes at the pan-cancer level. (D) Expression of \u003cem\u003eMS4A3\u003c/em\u003e transcript variant 3(uc001noo.3) in normal tissues and cancer tissues. (E) Distribution of \u003cem\u003eMS4A3\u003c/em\u003e transcript variant 3(uc001noo.3) between subcluster A2 and subcluster B2. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/4fa18be06acd44e06a79e240.png"},{"id":105755693,"identity":"e7a6df04-4904-4e96-9081-601499d8b122","added_by":"auto","created_at":"2026-03-30 16:29:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":62891586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/bf6f3971-e7cb-427f-8663-32c00450b95e.pdf"},{"id":97774943,"identity":"f9a93538-16c5-4a2d-b65d-518e747518f2","added_by":"auto","created_at":"2025-12-09 08:48:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14861,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarylegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/7a67f5f322916f3e459795a4.docx"},{"id":97774948,"identity":"fe810f6f-ae3c-4fc4-ab29-014af864af1d","added_by":"auto","created_at":"2025-12-09 08:48:34","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":153387,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/e52754e9c9fb17e6e4db59f9.xlsx"},{"id":97897923,"identity":"23d32566-2336-4958-863c-8cdd57378618","added_by":"auto","created_at":"2025-12-10 15:38:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":544367,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/67d4a520a5d2a5f432670440.xlsx"},{"id":97895655,"identity":"b719ddd8-f4a6-4dac-ba05-dde992639d2c","added_by":"auto","created_at":"2025-12-10 15:34:37","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":448818,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/df0fd93f102c32d1f558fa11.xlsx"},{"id":97774954,"identity":"239e9df9-bead-4e76-80fc-6db9b2d0e2b8","added_by":"auto","created_at":"2025-12-09 08:48:34","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":454198,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/479d6386ab25baac63bdcbd2.xlsx"},{"id":97895339,"identity":"ae0b6a8a-c265-43d2-94f6-70fe792a36ab","added_by":"auto","created_at":"2025-12-10 15:34:01","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":32940,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/fec26c333150b12196250170.xlsx"},{"id":97896936,"identity":"fb4de87c-5ac1-4707-870f-d9aaf150a115","added_by":"auto","created_at":"2025-12-10 15:37:14","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":4017792,"visible":true,"origin":"","legend":"","description":"","filename":"OrinigalDataofWesternBlot.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/57a315275aea90591e055da2.tif"},{"id":97774971,"identity":"8aa184f1-290a-4772-819a-fe4cc474ced2","added_by":"auto","created_at":"2025-12-09 08:48:35","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1746228,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/6b5fe86f1114e7a2ce7eb78a.tif"},{"id":97774968,"identity":"96fd24be-5048-4df4-b26d-02ccfeabb8af","added_by":"auto","created_at":"2025-12-09 08:48:35","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2034588,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/3b2ebfa301c53d31a6bc6573.tif"},{"id":98420910,"identity":"8c3fc4bc-88b9-430e-a2b8-451aece28212","added_by":"auto","created_at":"2025-12-17 16:18:36","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14248644,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/4da70af3b2c95c5dacd9a307.tif"},{"id":97897288,"identity":"35f66065-5d47-4dcd-976b-e207bd942848","added_by":"auto","created_at":"2025-12-10 15:37:42","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1898376,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/37fffcbd8bef7e22894d4624.tif"},{"id":97897992,"identity":"2cb6e7ac-753d-4739-b336-c3ac3be198e9","added_by":"auto","created_at":"2025-12-10 15:38:33","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2061230,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/dd2459d38bafa26c9a39e6e3.tif"},{"id":97774973,"identity":"40e54614-cf76-4129-bc4e-61a00f22e823","added_by":"auto","created_at":"2025-12-09 08:48:35","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1733700,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/c4a322fda3b32cf609d4e36f.tif"},{"id":97774974,"identity":"8b267af9-8452-4782-af71-97970217eab7","added_by":"auto","created_at":"2025-12-09 08:48:35","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":627766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure7.tif","url":"https://assets-eu.researchsquare.com/files/rs-8306096/v1/d7e415b6413fce9fb25b8541.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most common malignant tumors worldwide, with incidence and mortality rates ranking third and fourth, respectively \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Approximately 25% of patients with CRC have metastasis at initial diagnosis. Although surgical resection of lesions and use of chemotherapeutic drugs have improved the survival of individuals with CRC, around 50% of patients will develop tumor progression and metastasis, resulting in high mortality rates \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe process of tumor metastasis may be affected not only by the genomic changes or behavioral changes of tumor cells, but also by the tumor microenvironment \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The malignant progression of a tumor shows a closely coordinated balance between the immune effect and tolerance. Tumor cells can evade immune recognition and immune attack by modifying their own surface antigens or changing the tumor microenvironment. This biological behavior promotes tumor invasion and metastasis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, there is an urgent need to study the molecular principles driving the formation and maintenance of CRC immunophenotype.\u003c/p\u003e\u003cp\u003eIn a previous study, we demonstrated that multi-omics analyses of genomic, epigenomic, and transcriptomic variants is highly significant for the identification of CRC driver genes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this study, we calculated the Spearman correlation coefficients between programmed cell death ligand-1 (\u003cem\u003ePD-L1\u003c/em\u003e, also known as \u003cem\u003eCD274\u003c/em\u003e) and interferon gamma (\u003cem\u003eIFN-γ\u003c/em\u003e, hereafter referred to as \u003cem\u003eIFNG\u003c/em\u003e) expression and immune cell fraction, and screened different immune cell types based on \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e expression by least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we conducted integrated analysis of candidate genes based on differential somatic variations, copy number variations (CNV), and DNA methylation data from the two CRC subtypes with the greatest difference in immune infiltration characteristics. Subsequently, we constructed a risk scoring model, which highlighted two prognosis-related genes, \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e, as novel biomarkers for CRC immune phenotypes. Finally, we demonstrated that \u003cem\u003eFUT9\u003c/em\u003e overexpression correlated with an immunosuppressive phenotype of CRC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data acquisition \u0026amp; preprocessing\u003c/h2\u003e\u003cp\u003eRNA-Seq, microRNA (miRNA) expression, MET, somatic mutation, and CNV data, were obtained from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), along with corresponding clinical features and prognosis information from patients with CRC. Validation data sets were downloaded from the Gene Expression Omnibus (GEO) database and five GEO datasets (GSE14095, GSE37892, GSE64256, GSE83889 and GSE17538) were selected for further study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Molecular immune subtypes identification\u003c/h2\u003e\u003cp\u003eImmune cell fractions were calculated for each sample using the \u003cem\u003eLM22\u003c/em\u003e gene signature in CIBERSORTx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e7\u003c/sup\u003e. \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e gene expression levels were exploited to evaluate highly associated immune cell fractions by Spearman correlation analysis. Immune cell types most strongly correlated with \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e expression were screened by LASSO regression analysis. Unsupervised cluster analysis was performed to identify immune-related CRC patient subgroups from The Cancer Genome Atlas (TCGA), according to the immune cell types screened as described above. Other independent GEO datasets were used for validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Somatic variation analysis\u003c/h2\u003e\u003cp\u003eSomatic variation data were obtained from UCSC Xena and TCGA, and significant somatic variations were identified using VarScan 2 \u003csup\u003e8\u003c/sup\u003e. Somatic mutations differing between immune subtypes and mutual exclusion or co-occurrence of somatic mutations were analyzed using the R package, Maftools \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Alternative splicing analysis\u003c/h2\u003e\u003cp\u003eSample data from different CRC immune subtypes were downloaded from the percent splice-in (PSI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.mdanderson.org/TCGASpliceSeq/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.mdanderson.org/TCGASpliceSeq/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and candidate alternatively spliced transcripts identified based on threshold criteria of occurring in at least 75% of samples, with an average PSI for all samples\u0026thinsp;\u0026ge;\u0026thinsp;0.05. Differentially expressed alternative splicing (DEAS) between different subtypes was identified using a one sided t-test, followed by Benjamini\u0026ndash;Hochberg correction to adjust \u003cem\u003ep\u003c/em\u003e values; alternative splicing events with adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as significant.\u003c/p\u003e\u003cp\u003eIn addition, splicing variants of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e were analyzed via TSVdb \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tsvdb.com/index.html\u003c/span\u003e\u003cspan address=\"http://www.tsvdb.com/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 CNV analysis\u003c/h2\u003e\u003cp\u003eCNV data were downloaded from UCSC Xena and TCGA. Segment_Mean values\u0026thinsp;\u0026gt;\u0026thinsp;0.2 were defined as gains and those \u0026lt; \u0026minus;\u0026thinsp;0.2 as losses. Differential CNVs between CRC immune subtypes were analyzed using CoNVaQ \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, while BEDTools \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e was used to annotate differential CNV regions to corresponding genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 DNA methylation analysis\u003c/h2\u003e\u003cp\u003eDNA methylation data were downloaded from UCSC Xena and TCGA. Methylation level spectra were obtained after removing absent loci, and sites differentially methylated between two immune subtypes were analyzed using the R package, LIMMA \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e ; sites with |log\u003csub\u003e2\u003c/sub\u003e fold-change (FC)| \u0026ge; 0.4 and adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be significantly different. Finally, differentially methylated sites were mapped to corresponding genes for further study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Identification of differentially expressed transcripts\u003c/h2\u003e\u003cp\u003eDifferentially expressed messenger RNA (mRNA) and long non-coding RNA (lncRNA) molecules were analyzed using the R package, DESeq 2, applying a threshold of adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e FC| \u0026ge; 1. Differentially expressed miRNAs were identified using DESeq 2 as those with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and log\u003csub\u003e2\u003c/sub\u003e |FC| \u0026ge; 0.05. Volcano plots were generated using Bioconductor in R.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Gene Set Viration Analysis (GSVA)\u003c/h2\u003e\u003cp\u003eThe gene sets \u0026ldquo;c2.cp.kegg.v7.4.symbols.gmt\u0026rdquo; and \u0026ldquo;h.all.v7.4.symbols.gmt\u0026rdquo; were obtained from MSigDB, then GSVA was applied in R to identify differential pathways among CRC subtypes \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Pathways that differed between CRC subtypes with adjusted \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are presented as a heatmap.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.9 Kaplan\u0026ndash;Meier analysis of gene signature from TCGA datasets using the survival and survminer packages in R\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn this study, we used R software package survival to integrate survival time, survival status and gene expression data, and exploited Cox method to evaluate the prognostic significance of each gene in CRC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Pan-cancer immune infiltration\u003c/h2\u003e\u003cp\u003ePan-cancer immune infiltration was assessed using SangerBox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a comprehensive tool for bioinformatics analysis based on R. First, unified and standardized pan-cancer data (TCGA Pan-Cancer (PANCAN, N\u0026thinsp;=\u0026thinsp;10535, G\u0026thinsp;=\u0026thinsp;60499)) were downloaded from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Second, target gene expression data were extracted for each solid tumor sample. Third, each expression value was subjected to log\u003csub\u003e2\u003c/sub\u003e (x\u0026thinsp;+\u0026thinsp;0.001) transformation. Fourth, gene expression profiles for each tumor were extracted, mapped the expression profiles for gene symbols, and the R package \u0026ldquo;ESTIMATE\u0026rdquo;\u003csup\u003e15\u003c/sup\u003e used to calculate stromal/immune scores for each sample, based on gene expression levels. Finally, Pearson\u0026rsquo;s correlation coefficient values between genes and immune infiltration scores in each tumor were calculated using the corr.test function in the R software package, psych (version 2.1.6), to identify significantly associated immune infiltration scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Reagents \u0026amp; materials\u003c/h2\u003e\u003cp\u003eRPMI 1640 medium was purchased from HyClone (Logan, UT, USA). Lipofectamine 3000 was obtained from Invitrogen (CA, USA). Secondary antibodies for western blotting were purchased from Li-COR Biosciences (NE, USA). Small interfering RNAs (siRNAs) targeting human \u003cem\u003eFUT9\u003c/em\u003e or \u003cem\u003eMS4A3\u003c/em\u003e were purchased from GenePharma (Shanghai, China) and transfections were performed according to the manufacturer\u0026rsquo;s instructions. Information for siRNAs was as followed: sense GAUCUUCAGUCCAAUGGAATT, antisense UUCCAUUGGACUGAAGAUCTT.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Cell lines \u0026amp; cultures\u003c/h2\u003e\u003cp\u003eThe CRC cell lines, SW620 and Rko, were purchased from the American Type Culture Collection (USA) and cultured in RPMI 1640 supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA). The TALL-104 cell line was also purchased from the American Type Culture Collection and cultured in RPMI 1640 supplemented with 10% (v/v) FBS (Gibco) and 100 U/mL recombinant interleukin-2 (Peprotech, Cat# 200-02). All cell lines were maintained in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere at 37\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Western blot and immunohistochemistry (IHC)\u003c/h2\u003e\u003cp\u003eWestern blot, IHC staining, and IHC score evaluation were performed as previously described \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Tissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissue samples was obtained from Sanmen People\u0026rsquo;s Hospital on 26/07/2025 and authorized by Sanmen People\u0026rsquo;s Hospital Ethics Committee (2025-064). Primary antibodies against GAPDH (1:5000, Proteintech, Cat. #60004-1- Ig) and FUT9 (1:500, Proteintech, Cat. #60230-1-Ig) were utilized for western blot, and FUT9 (1:100, Proteintech, Cat. #60230-1-Ig) for microarray chip was used for IHC staining.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Apoptosis and cytotoxicity assay\u003c/h2\u003e\u003cp\u003eTo evaluate apoptosis, 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e tumor cells were cocultured with 3 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e TALL-104 cells for 48 h. Next, TALL-104 cells were detected by flow cytometry using an Annexin V-FITC/PI apoptosis kit (MultiSciences, Cat. #AP101).\u003c/p\u003e\u003cp\u003eTo assess cytotoxicity, 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e tumor cells were cocultured with 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e TALL-104 cells for 48 h in 96-well plates. Lactate dehydrogenase (LDH) released from target cells into the cell-free supernatant was detected using an LDH cytotoxicity detection kit (Genmed, Cat. # GMS10073.1), following the manufacturer\u0026rsquo;s instructions. The amount of LDH released was used to assess the lysis of target cells, which can be translated as the effectiveness of effector cells. Percentage cytotoxicity was calculated according to optical density (OD) values using the following formula:\u003c/p\u003e\u003cp\u003eCytotoxicity (%) = (Experimental\u0026thinsp;\u0026minus;\u0026thinsp;Effector spontaneous\u0026thinsp;\u0026minus;\u0026thinsp;Target spontaneous) / (Target maximum\u0026thinsp;\u0026minus;\u0026thinsp;Target spontaneous) \u0026times; 100%\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed with the GraphPad software(Prism 8) and R software (version 3.5.1). False discovery rate was adjusted through the Benjamini\u0026ndash;Hochberg procedure. Student\u0026rsquo;s t-test was used to compare the differences in expression levels between groups. Overall survival (OS) curves were generated by Kaplan\u0026ndash;Meier analysis and compared with the log-rank test. Univariate and multivariable Cox proportional-hazards regression models were executed to calculate hazard ratios (HRs) and 95% confidence intervals. Receiver operator characteristic (ROC) curves and area under the curve (AUC) were exploited to calculate the diagnostic efficacy. For in vitro experiments, at least three replicates were performed. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of molecular immune subtypes related to \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e expression in CRC\u003c/h2\u003e\u003cp\u003eGene expression data were obtained from TCGA and four independent datasets: GSE14095, GSE37892, GSE64256, and GSE83889, of which, TCGA data set was a training cohort and the GSE data sets were validation cohorts. Subsequently, CIBERSORTx (model\u0026thinsp;=\u0026thinsp;absolute, permutations\u0026thinsp;=\u0026thinsp;1000, LM22 signature) was used to calculate immune cell fractions of 22 immune cell types within individual samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Associations of \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e gene expression with immune cell fractions were evaluated by calculating Spearman correlation coefficient values. In TCGA, nine immune cell types were significantly correlated with \u003cem\u003eCD274\u003c/em\u003e or \u003cem\u003eIFNG\u003c/em\u003e expression, and the most relevant of these were screened by LASSO regression analysis. The results showed that five immune cell types, CD8 T cells, CD4 memory activated T cells, resting NK cells, M1 macrophages, and neutrophils, were the most relevant. Therefore, these cell types were selected for further study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the five immune cell types mentioned above, TCGA-COADREAD cohort immune subtypes were identified by unsupervised clustering analysis. TCGA-COADREAD cohort data could be divided into two types, cluster A and B, comprising five immune subtypes: A1, A2, B1, B2A, and B2B. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, cluster A displayed a lower cytotoxic immune-phenotype than cluster B. Among cluster A, subcluster A1 showed slightly elevated neutrophil-related cytotoxic immune-phenotype compared to subcluster A2. In addition, in cluster B, subcluster B1 showed a higher CD8 T cell-related immune-phenotype but a lower M1 macrophage-related immune-phenotype compared to subcluster B2. Furthermore, subcluster B2A showed the highest M1 macrophage-related immune-phenotype while also showing a relatively higher CD8 T cell-related immune-phenotype and resting NK cell-related immune-phenotype among the five immune subtypes. Therefore, we observed the lowest cytotoxic immune-phenotype in subcluster A2 while subcluster B2, particularly subcluster B2A, showed the highest cytotoxic immune-phenotype.\u003c/p\u003e\u003cp\u003eAdditional data were obtained from GSE14095, GSE64256, and GSE83889 datasets to be used as validation cohorts. Results of molecular immune subtype identification on these validation cohorts also illustrated that CRC samples could be divided into two types, clusters A and B, which comprised subcluster A1, A2, B1, B2A, and B2B (Supplementary Fig.\u0026nbsp;1A\u0026ndash;C). Next, expression of \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e genes were investigated in each TCGA-COADREAD cohort immune subtype. We found that subcluster A2 had the lowest cytotoxic immune-phenotype while subcluster B2, particularly subcluster B2A, expressed the highest levels of \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e, relative to the other subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The typical molecular and clinical characteristics of these five immune subtypes were illustrated and we found significant differences in microsatellite instability (MSI) distribution among the different subgroups (Supplementary Fig.\u0026nbsp;1D).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Identification of immunophenotype-related somatic mutations in CRC\u003c/h2\u003e\u003cp\u003eTo study differences in somatic mutations between different immunophenotypes, we first downloaded the somatic mutation data from TCGA to analyze the total mutation load in the different TCGA-COADREAD cohort immune subtypes. We found that somatic mutation characteristics differed among the subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with subcluster A2 and B2A having the lowest and highest total mutation load, respectively. This suggested that different immune status of subclusters might correlate with different somatic mutation status. Next, we used Maftools to analyze somatic mutation characteristics of each immune subtype, and the distribution of major oncogenes in each subtype was plotted. The results revealed that the mutation frequency of \u003cem\u003eAPC\u003c/em\u003e was higher in cluster A than in cluster B, of which, subcluster B2A had the lowest \u003cem\u003eAPC\u003c/em\u003e mutation frequency. Moreover, \u003cem\u003eKMT2D\u003c/em\u003e, a methylation-related gene, showed the highest and lowest mutation frequency in subcluster B2A and A2, respectively, which suggests that the immune status of these subclusters may be linked to DNA methylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u0026ndash;F). In addition, we analyzed the concurrent and mutually exclusive mutations of somatic mutations in each immune subtype, and the top 25 mutations in each immune subtype were mapped. Our data show that compared to subclusters A1 and A2, subcluster B2A exhibits more concurrent mutations (Supplementary Fig.\u0026nbsp;2A\u0026ndash;E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of alternative splicing events\u003c/h2\u003e\u003cp\u003eAlternative splicing events always occur during tumorigenesis; therefore, we analyzed differential alternative splicing events between CRC subtypes. Subclusters A2 and B2 subtype sample data were downloaded from the PSI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.mdanderson.org/TCGASpliceSeq/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.mdanderson.org/TCGASpliceSeq/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only alternative splicing events meeting the following screening conditions were included: (1) occurred in at least 75% of the samples; and (2) average PSI in all samples\u0026thinsp;\u0026ge;\u0026thinsp;0.05. A total of 33371 alternative splicing events from 9918 genes were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Differential alternative splicing events between subcluster A2 and B2 subtypes were identified by t-test, followed by Benjamini\u0026ndash;Hochberg \u003cem\u003eP\u003c/em\u003e value correction; alternative splicing events with a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after Benjamini\u0026ndash;Hochberg correction were considered significantly different. Finally, 1238 significantly different alternative splicing events were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), of which 719 and 519 were upregulated in subcluster A2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and subcluster B2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) subtype tumors, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Comprehensive analysis of DEGs, CNV-disrupted genes, \u0026amp; DNA methylation in CRC\u003c/h2\u003e\u003cp\u003eSubclusters A2 and B2 were selected as the two most significantly different immune subtypes and analyzed using the R package \u0026ldquo;DESeq2\u0026rdquo; to identify DEGs with |log\u003csub\u003e2\u003c/sub\u003e FC| \u0026ge; 1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 929 DEGs were identified, of which, 56 and 873 were upregulated in subcluster A2 and B2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Table\u0026nbsp;1). Further, GSVA enrichment analysis showed that 16 pathways were significantly differentially enriched between subcluster A2 and B2, including the immune-related pathways, cytokine\u0026mdash;cytokine receptor interaction, JAK/STAT signaling, and chemokine signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we downloaded CRC CNV data from TCGA and defined Segment_Mean value\u0026thinsp;\u0026gt;\u0026thinsp;0.2 as copy number gain and Segment_Mean value \u0026lt; \u0026minus;\u0026thinsp;0.2 as copy number loss. CoNVaQ was used to analyze differential CNV regions between subcluster A2 and B2, and we performed gene annotations of differential CNV regions using bedtools, resulting in 32 DEGs annotated within the CNV regions with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Supplementary Table\u0026nbsp;2 and Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eFinally, DNA methylation data were downloaded from UCSC Xena and TCGA. Methylation expression spectra were obtained after removing absent loci, and sites that were differentially methylated between two immune subtypes were analyzed. We used the R package \u0026ldquo;limma\u0026rdquo; to screen for differentially methylated sites between subcluster A2 and B2. A total of 7344 differentially methylated sites with an adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e FC| \u0026ge; 0.4 were identified, of which 5894 were hypermethylated in subcluster A2 and 1450 in subcluster B2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Annotation information for all methylation sites was shown in Supplementary Table\u0026nbsp;4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Construction of a DEG-miRNA-lncRNA regulatory network\u003c/h2\u003e\u003cp\u003eThe competitive endogenous RNA (CeRNA) theory hypothesizes that mRNAs, pseudogenes, lncRNAs, and circular RNA may bind competitively with miRNA through miRNA response elements (MREs), to hinder miRNA inhibition of coding RNA, thereby upregulating target gene expression \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. According to this hypothesis, lncRNAs restrain miRNA and promote miRNA-related downstream genes by acting as sponges \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Thus, we constructed a DEG-miRNA-lncRNA regulatory network.\u003c/p\u003e\u003cp\u003eA total of 66 DEMs were identified between subcluster A2 and B2, including 27 and 39 upregulated DEMs in subcluster A2 and B2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Target genes of these DEMs were predicted using the miRDB, miRTarBase, and TargetScan databases. Next, we merged upregulated DEM-related DEGs in subcluster A2 and B2, and filtered candidate DEM-DEG regulatory networks using the threshold criterion and DEM-DEG regulatory networks were simultaneously predicted in at least two databases. We found that 257 and 310 DEM-related DEGs were upregulated in subcluster A2 and B2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also analyzed DELs between subcluster A2 and B2 using DESeq2 and identified 281 DELs, of which six were upregulated in subcluster A2 and 275 in subcluster B2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Analysis of data from the MiRCode database detected 2122 DEL-related DEMs, and target genes of these miRNAs were predicted using the miRDB, miRTarBase, and TargetScan databases. Venn diagrams were generated and illustrated that 83 DEL-related DEM-DEG regulatory networks were upregulated in subcluster A2, while 1261 were elevated in subcluster B2; all candidate regulatory networks identified by analysis of Venn diagrams were screened using the threshold criterion that DEL-related DEM-DEG regulatory networks were simultaneously predicted in at least two databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Finally, a ceRNA network was constructed to visualize the regulatory correlations among DELs, DEMs, and DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Evaluation of immunotherapy effects and survival analysis\u003c/h2\u003e\u003cp\u003eWe intersected our data on differentially methylated genes, CNV-disrupted genes, DEM-related genes, and DEL-related genes, resulting in screening of 47 candidate DEGs that appeared in at least three data types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, we predicted the immunotherapeutic responses of subcluster A2 and B2 subtype samples using the TIDE (Tumor Immune Dysfunction and Exclusion) analytic tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e \u003csup\u003e18\u003c/sup\u003e, and investigated the distribution of TIDE fractions between subcluster A2 and B2 (Supplementary Fig.\u0026nbsp;3A and Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also conducted Cox regression survival analysis according to the expression levels of the 47 DEGs screened in the previous step. LASSO cox regression analysis indicated that \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e showed a significant impact on overall survival. Further, combined with result of LASSO cox regression, we established a multifactor prognosis prediction model and a risk score formula was constructed as follows:\u003c/p\u003e\u003cp\u003eRisk score = (0.277 \u0026times; \u003cem\u003eFUT9\u003c/em\u003e) \u0026ndash; (0.508 \u0026times; \u003cem\u003eMS4A3\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eRisk scores for each sample were then calculated based on the risk score formula, and samples divided into high and low risk groups, according to median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, patients with higher risk scores had shorter survival times and increased disease recurrence rates. Further, Kaplan\u0026ndash;Meier survival analysis demonstrated unfavorable prognosis for CRC patients with high risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Multivariable Cox regression analysis demonstrated risk score as an independent risk factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Distributions of CRC patients by the two molecular immune subtypes, two risk score groups, and prognosis status are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF. Principal component analysis indicated that dimensions were discernible between the two risk score groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). In addition, to developing a prognostic prediction model for clinical application, a nomogram including risk score and clinicopathological parameters was established to predict the prognosis of patients with CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). The resulting AUC values showed that our model had a better classification effect than random choice, while the nomogram showed superior value for predicting prognosis at 5 years (AUC\u0026thinsp;=\u0026thinsp;0.650) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). In addition, we validated our model on GSE17538 and our model successfully predicted prognosis of CRC patients in the GSE17538 cohort (Supplementary Fig.\u0026nbsp;3B and 3C).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7 High \u003cem\u003eFUT9\u003c/em\u003e expression is correlated with CRC immunosuppressive phenotype\u003c/h2\u003e\u003cp\u003eOur risk score formula demonstrated that patients with CRC and higher \u003cem\u003eFUT9\u003c/em\u003e expression levels had unfavorable prognosis, indicating that \u003cem\u003eFUT9\u003c/em\u003e may be a novel biomarker for CRC immune-phenotype and patient prognosis. Therefore, we explored the biological function and prognostic significance of \u003cem\u003eFUT9\u003c/em\u003e in CRC. A tissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissues samples was used for IHC validation experiments. \u003cem\u003eFUT9\u003c/em\u003e levels were higher in tumor than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also assessed the effect of \u003cem\u003eFUT9\u003c/em\u003e on CRC oncoimmunology in vitro. \u003cem\u003eFUT9\u003c/em\u003e gene expression levels in different CRC cell lines were obtained from the Cancer Cell Line Encyclopedia database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ccle\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ccle\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); \u003cem\u003eFUT9\u003c/em\u003e was expressed at higher levels in Rko cells, while its expression was lower in SW620 cells. Therefore, we overexpressed \u003cem\u003eFUT9\u003c/em\u003e in SW620 cells and knocked down its expression in Rko cells. Western blot demonstrated that \u003cem\u003eFUT9\u003c/em\u003e was overexpressed in SW620 cells efficiently, meanwhile siRNA-\u003cem\u003eFUT9\u003c/em\u003e-1 downregulated \u003cem\u003eFUT9\u003c/em\u003e most significantly and was exploited for further study (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). To evaluate the effect of \u003cem\u003eFUT9\u003c/em\u003e on anti-T cell killing ability, CRC cells were co-cultured with TALL-104, a human acute T lymphocyte leukemia cell line with high CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxicity \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e for further study. \u003cem\u003eFUT9\u003c/em\u003e induced CRC resistance to CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In addition, overexpression of \u003cem\u003eFUT9\u003c/em\u003e reduced apoptosis of CRC cells co-cultured with TALL-104 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Overall, these results illustrate that \u003cem\u003eFUT9\u003c/em\u003e expression correlates with an immunosuppressive phenotype in CRC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Overview of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e in prognosis and oncoimmunology at the pan-cancer level\u003c/h2\u003e\u003cp\u003eTo explore the broader prognostic and oncoimmuological value of \u003cem\u003eFUT9\u003c/em\u003e and MS4A3, we conducted pan-cancer analysis of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e in solid cancers. High \u003cem\u003eFUT9\u003c/em\u003e expression was correlated with unfavorable prognosis in CRC (hazard ratio (HR)\u0026thinsp;=\u0026thinsp;2.11, 95% confidence interval (CI) 1.05\u0026ndash;4.24), thyroid carcinoma (HR\u0026thinsp;=\u0026thinsp;2.86, 95% CI: 1.06\u0026ndash;7.70), uterine corpus endometrial carcinoma (HR\u0026thinsp;=\u0026thinsp;2.57, 95% CI: 1.15\u0026ndash;5.76), stomach adenocarcinoma (HR\u0026thinsp;=\u0026thinsp;1.74, 95% CI: 1.18\u0026ndash;2.55), and uterine carcinosarcoma (HR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.09\u0026ndash;4.44), but was associated with better survival in bladder urothelial carcinoma (HR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: 0.54\u0026ndash;0.99), sarcoma (HR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.37\u0026ndash;0.90), glioma (HR\u0026thinsp;=\u0026thinsp;0.17, 95% CI: 0.13\u0026ndash;0.22), and brain lower grade glioma (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI: 0.24\u0026ndash;0.51) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;I). We obtained 22 types of immune cell infiltration scores of 35 forms of solid cancer samples. We calculated the Pearson's correlation coefficient between \u003cem\u003eFUT9\u003c/em\u003e and immune cell infiltration score in each type of cancer to determine the significant related immune infiltration score. Finally, we found that \u003cem\u003eFUT9\u003c/em\u003e levels were significantly associated with at least one type of immune cell infiltration in 33 types of solid cancers, but had no significant correlation with immune infiltration in mesothelioma and uterine carcinosarcoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ). \u003cem\u003eFUT9\u003c/em\u003e expression was significantly positively associated with the stromal score in CRC (R\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.0e-4) and prostate cancer (R\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.1e-9), while it was significantly negatively correlated with the stromal score in 12 other cancers (Supplementary Fig.\u0026nbsp;4). In addition, \u003cem\u003eFUT9\u003c/em\u003e expression was significantly positively associated with the immune score in prostate cancer (R\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.4e-7) and thyroid cancer (R\u0026thinsp;=\u0026thinsp;0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), while it was significantly negatively correlated with the immune score in 13 other cancers (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigh \u003cem\u003eMS4A3\u003c/em\u003e expression was positively correlated with shorter survival time in patients with glioma (HR\u0026thinsp;=\u0026thinsp;3.39, 95% CI: 2.37\u0026ndash;4.85), while it was associated with better survival in CRC (HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.34\u0026ndash;0.87) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Furthermore, we also obtained 22 types of immune cell infiltration scores of 35 types of solid cancer samples and calculated the Pearson's correlation coefficient between \u003cem\u003eMS4A3\u003c/em\u003e along with the immune cell infiltration score in each type of cancer to determine the significant related immune infiltration score. \u003cem\u003eMS4A3\u003c/em\u003e levels were significantly correlated with at least one kind of immune cell infiltration in 33 types of solid cancers, but had no significant correlation with immune infiltration in mesothelioma and uveal melanoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Furthermore, \u003cem\u003eMS4A3\u003c/em\u003e was positively associated with the immune score in 23 tumors, including CRC (R\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.7e-12) (Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBecause our findings suggest the involvement of somatic mutations and alternative splicing events in CRC oncoimmunology, we sought to understand whether \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e underwent somatic mutations or alternative splicing. Our results demonstrated that \u003cem\u003eFUT9\u003c/em\u003e, but not \u003cem\u003eMS4A3\u003c/em\u003e, showed significant single-nucleotide variants (missense mutation) in subcluster B2 subtype CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eK and Supplementary Fig.\u0026nbsp;7A). Further, \u003cem\u003eMS4A3\u003c/em\u003e had three transcript variants (uc001nom.3, uc001non.3, and uc001noo.3), only one of which (uc001noo.3) was expressed at significantly higher levels in normal tissues compared to cancer tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;7B and 7C); the distribution of \u003cem\u003eMS4A3\u003c/em\u003e transcript variant 3 (uc001noo.3) between subcluster A2 and B2 CRC subtypes is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the development of resistance in traditional cancer therapies, molecular targeted therapy and immune checkpoint inhibitors have become the focus of considerable research efforts. However, patients with differing tumor infiltrating immune cells achieve varying therapeutic efficacy. In recent years, some studies have roughly divided tumors into \u0026ldquo;cold tumors\u0026rdquo;, with few immune cells and a large proportion of immunosuppressive cells, and \u0026ldquo;hot tumors\u0026rdquo;, with increased infiltration of activated immune cells, such as CD8\u003csup\u003e+\u003c/sup\u003e T cells and Th1 cells, according to their immune cell infiltration characteristics \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Clinical trials have shown that patients with \u0026ldquo;hot tumors\u0026rdquo; can achieve better responses to immunotherapy. Although this tumor immunophenotyping method provides researchers with a more accurate approach, its application in CRC has been limited because many patients with CRC are diagnosed at an advanced stage. Moreover, it is impractical to conduct tumor IHC staining for tumor immunophenotyping by taking tissue sections from each patient. Hence, identification of the molecular classifications and the immune characteristics of CRC through bioinformatic methods are important for CRC prognosis.\u003c/p\u003e\u003cp\u003eMutations in genes often occur at the transcriptional or post-transcriptional levels during tumorigenesis, with somatic mutations, copy number variations (CNVs), and DNA methylation playing vital roles during tumorigenesis \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Therefore, integrative analyses of multi-omics data have been useful for the identification of tumor-related biomarkers. In this study, we performed integrative analyses of multi-omics data obtained from TCGA and the GEO database. After evaluating associations between \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e gene expression and immune cell fractions, two CRC types (cluster A and cluster B) including five subtypes (subclusters A1, A2, B1, B2A, and B2B) were identified by unsupervised clustering analysis. Among these subtypes, subcluster A2 showed a lower cytotoxic immune-phenotype, while subcluster B2 exhibited a higher cytotoxic immune-phenotype. Integrative analysis indicated that somatic mutations, CNVs, and DNA methylation differed between subcluster A2 and B2, and analysis of DEGs correlated with CRC immune phenotypes identified \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e as key genes related to CRC immune-phenotypes and prognosis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMS4A3\u003c/em\u003e (membrane spanning 4-domains A3)\u0026mdash;also known as \u003cem\u003eCD20L\u003c/em\u003e or \u003cem\u003eHTM4\u003c/em\u003e\u0026mdash;is a member of the membrane-spanning 4A gene family, which was first identified by Liang et al. in 2001 \u003csup\u003e24\u003c/sup\u003e. Microarray, RT-PCR, and immunofluorescence studies demonstrated that \u003cem\u003eMS4A3\u003c/em\u003e is preferentially expressed in basophiles, rather than other granulocytes, B cells, or T cells \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In addition, \u003cem\u003eMS4A3\u003c/em\u003e is expressed in breast ductal epithelium, testis (seminiferous tubules and rete), pancreas, prostate, stomach, thymus, and hematopoietic cells within fetal liver. In hematopoietic cells, \u003cem\u003eMS4A3\u003c/em\u003e functions as a cell cycle regulator that modulates G1/S transition through \u003cem\u003eCDKN3\u003c/em\u003e/\u003cem\u003eKAP\u003c/em\u003e-dependent phosphorylation of \u003cem\u003eCDK2\u003c/em\u003e \u003csup\u003e27\u003c/sup\u003e. Therefore, \u003cem\u003eMS4A3\u003c/em\u003e is an important regulator of the hematopoietic cell cycle. Heller et al. \u003csup\u003e28\u003c/sup\u003e found that \u003cem\u003eMS4A3\u003c/em\u003e may be downregulated by \u003cem\u003eEVI1\u003c/em\u003e at the transcriptional level, and that \u003cem\u003eMS4A3\u003c/em\u003e knockdown can promote lymphoma proliferation. \u003cem\u003eMS4A3\u003c/em\u003e was also shown to induce chronic myeloid leukemia differentiation through cytokine receptor endocytosis \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In addition, although \u003cem\u003eMS4A3\u003c/em\u003e is mainly expressed in hematopoietic cells, its expression in prostate, ovarian, and breast cancer differ significantly from those in normal tissues, suggesting that \u003cem\u003eMS4A3\u003c/em\u003e may play a role in tumorigenesis \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In the present study, we demonstrate that \u003cem\u003eMS4A3\u003c/em\u003e can act as a novel biomarker for immune-phenotype analysis in CRC and that increased \u003cem\u003eMS4A3\u003c/em\u003e expression is correlated with improved prognosis of patients with CRC.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFUT9\u003c/em\u003e (Fucosyltransferase 9) primarily functions as a modifier during recognition of selectin on endothelial cell surfaces, mediated by sialyl Lewis(x) antigen expressed on the leukocyte surface. \u003cem\u003eFUT9\u003c/em\u003e is mainly distributed in the brain, stomach, and spleen \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and abnormal \u003cem\u003eFUT9\u003c/em\u003e expression is often closely correlated with immune system disorders, such as inflammation \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recently, the involvement of \u003cem\u003eFUT9\u003c/em\u003e during tumorigenesis has become the focus of considerable attention. Survival analysis, based on microarray mRNA expression data, illustrated that \u003cem\u003eFUT9\u003c/em\u003e expression was an unfavorable prognostic factor, and that high \u003cem\u003eFUT9\u003c/em\u003e levels were associated with decreased overall survival of ovarian cancer patients after intraperitoneal injection compared to intravenous injection \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In addition, \u003cem\u003eFUT9\u003c/em\u003e is downregulated in a \u003cem\u003eHelicobacter pylori\u003c/em\u003e-associated gastric cancer relative to patients with atrophic gastritis \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Regarding CRC, \u003cem\u003eFUT9\u003c/em\u003e has been revealed to be a double-edged sword during CRC tumorigenesis. Auslander \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e34\u003c/sup\u003e illustrated that, although \u003cem\u003eFUT9\u003c/em\u003e inhibited CRC proliferation and metastasis, it promoted CRC tumor-initiating cells and acted as a metabolic driver of advanced-stage CRC. Further, Blanas \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e35\u003c/sup\u003e demonstrated that \u003cem\u003eFUT9\u003c/em\u003e can augment the cancer stemness characteristics of CRC. In the present study, we show that high \u003cem\u003eFUT9\u003c/em\u003e expression is correlated with unfavorable prognosis and using \u003cem\u003ein vitro\u003c/em\u003e experiments, we also show that \u003cem\u003eFUT9\u003c/em\u003e has an immunosuppressive effect on CRC.\u003c/p\u003e\u003cp\u003eThe strength of the present study lies in the application of multi-omics analyses to identify candidate genes, and our data suggest that \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e are potential novel biomarkers for CRC immune-phenotype. Functionally, our \u003cem\u003ein vitro\u003c/em\u003e experiments demonstrated an immunosuppressive role for \u003cem\u003eFUT9\u003c/em\u003e. To our knowledge, this is the first report of the prognostic value of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e in CRC, and we also assessed the biological role of \u003cem\u003eFUT9\u003c/em\u003e in CRC immunology. Our study highlights the value of novel immune phenotype-related genes and potential regulatory noncoding RNA networks in CRC. Methods aimed at detecting these features may provide novel strategies for improving prognoses and immune therapy efficiency for patients with CRC.\u003c/p\u003e\u003cp\u003eThe present study also has several limitations. We only validated the biological and clinical significance of \u003cem\u003eFUT9\u003c/em\u003e, and additional validation study for \u003cem\u003eMS4A3\u003c/em\u003e is needed. Further, the mechanistic aspects of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e were not explored in detail. In addition, we constructed a ceRNA network for CRC-related genes, but did not confirm the regulation patterns of lncRNA, miRNA, and mRNA within the predicted ceRNA network. Therefore, additional studies to elucidate these underlying mechanisms should be conducted. Furthermore, tumor infiltrating lymphocytes (TIL) have an important impact on CRC invasion, metastasis, and prognosis \u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Among them, TILs with CD3\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003e, and CD8\u003csup\u003e+\u003c/sup\u003e are closely related to CRC prognosis \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Hence, additional correlation analysis of \u003cem\u003eFUT9\u003c/em\u003e, \u003cem\u003eMS4A3, CD3\u003c/em\u003e, \u003cem\u003eCD4\u003c/em\u003e, and \u003cem\u003eCD8\u003c/em\u003e should be carried out in further studies. We believe that such studies will provide a better understanding of the mechanisms involved in CRC progression.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur research focused on establishing a risk model for CRC immunophenotyping and prognosis through multi-omics analysis, and we highlight \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e as novel immunophenotyping-related genes for CRC. Our study underscores the value of novel immunophenotyping-related genes and explores the immunosuppressive role of \u003cem\u003eFUT9\u003c/em\u003e while also laying a foundation for further studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the conclusions of this article are included within the article and its supplementary files. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue microarray chip comprising 94 human CRC and 86 paired adjacent normal tissue samples was obtained from Sanmen People\u0026rsquo;s Hospital. The study protocol was approved by Sanmen People\u0026rsquo;s Hospital Ethics Committee (2025-064). All experiments were performed in compliance with the relevant regulations, and all patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;This work was supported by the National Natural Science Foundation of China (grant number: 82303594), Zhejiang Medical and Health Science and Technology Plan (grant number: 2024XY091 and 2019RC175) and Zhejiang Provincial County Level Advantageous Disciplines of Traditional Chinese Medicine Construction Plan\u0026nbsp;(2023-XK-D040).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ZF and XX conceived the idea and handled bioinformatic analyses, designed and monitored the research. MZ wrote the main manuscript text and prepared the figures and tables. MZ, HD and YH measured \u003cem\u003eFUT9\u003c/em\u003e expression, MZ and YH assessed and confirmed the staining results in clinical tissue samples. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; We acknowledge public databases including TCGA and GEO for providing their platforms and contributors for uploading their meaningful datasets. 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Oncol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12957-021-02433-w\u003c/span\u003e\u003cspan address=\"10.1186/s12957-021-02433-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FUT9, MS4A3, Immunophenotyping, Multi-omics analyses, colorectal cancer","lastPublishedDoi":"10.21203/rs.3.rs-8306096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8306096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eColorectal cancer(CRC) is one of the most common malignant tumors worldwide. Patients with different immunophenotypes of CRC could achieve different effect of immunotherapy and yield different prognosis. With the advancement of bioinformatics, multi-omics analysis of the variations at both genomics and epigenomics levels helps a lot to provide a molecular basis for immunophenotype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eGene expression and clinical data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We calculated Spearman correlation of \u003cem\u003eCD274\u003c/em\u003e (programmed cell death ligand-1, \u003cem\u003ePD-L1\u003c/em\u003e) and \u003cem\u003eIFNG\u003c/em\u003e (interferon gamma, \u003cem\u003eIFN-γ\u003c/em\u003e\u003cb\u003e)\u003c/b\u003e expressions with immune cell fraction, and screened different immune cell types with \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e by Lasso regression analysis. Multi-omics analysis was exploited to screen out candidate genes with differential in genetic and epigenetic landscapes between two CRC subtypes with the greatest difference in immune infiltration. Finally, a risk scoring model was established and the role of candidate genes in prognosis and oncoimmunology was evaluated at the pan-cancer level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTwo CRC types (cluster A and cluster B) including five subtypes (subclusters A1, A2, B1, B2A, and B2B) were identified by unsupervised clustering analysis. Somatic mutations, CNVs, and DNA methylation differed between subcluster A2 and B2, and analysis of DEGs correlated with CRC immune phenotypes identified \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e as key genes related to CRC immune-phenotypes and prognosis. Furthermore, \u003cem\u003eFUT9\u003c/em\u003e was validated to act as a key gene related to CRC immune escape in vitro.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe present study established a risk model for CRC immunophenotyping and prognosis, and highlighted the significance of \u003cem\u003eFUT9\u003c/em\u003e and \u003cem\u003eMS4A3\u003c/em\u003e in oncoimmunology of CRC.\u003c/p\u003e","manuscriptTitle":"Integrative multi-omics analysis identified FUT9 and MS4A3 as novel immune-phenotype and prognosis biomarkers for colorectal cancer and analyze the role of FUT9 in oncoimmunology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 08:48:29","doi":"10.21203/rs.3.rs-8306096/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-30T11:54:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T15:29:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276327886026012095490430414319840481701","date":"2026-01-24T07:52:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T17:10:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201990216475851497083009826100226774173","date":"2025-12-17T13:00:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239600091067742131857393405478732639688","date":"2025-12-15T17:19:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-13T15:24:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-11T11:25:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T06:56:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T06:55:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-08T09:20:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"025e79c8-4ab0-4d15-b05b-b9694b02e477","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59264739,"name":"Health sciences/Biomarkers"},{"id":59264740,"name":"Biological sciences/Cancer"},{"id":59264741,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59264742,"name":"Biological sciences/Immunology"},{"id":59264743,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-30T16:20:03+00:00","versionOfRecord":{"articleIdentity":"rs-8306096","link":"https://doi.org/10.1038/s41598-026-45508-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-23 16:13:09","publishedOnDateReadable":"March 23rd, 2026"},"versionCreatedAt":"2025-12-09 08:48:29","video":"","vorDoi":"10.1038/s41598-026-45508-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-45508-y","workflowStages":[]},"version":"v1","identity":"rs-8306096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8306096","identity":"rs-8306096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}