Comprehensive pan-cancer analysis reveals that FBXO2 as a potential therapeutic target associated with immune infiltration

Comprehensive pan-cancer analysis reveals that FBXO2 as a potential therapeutic target associated with immune infiltration | 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 Research Article Comprehensive pan-cancer analysis reveals that FBXO2 as a potential therapeutic target associated with immune infiltration Ming Wei, Huilin Zang, Qianqian Gao, Yuan Zhang, Jiexia Wen, Yunhuan Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8578038/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To comprehensively characterize the immunological functions and prognostic significance of FBXO2 across human cancers, and elucidate the functional role of FBXO2 in colorectal cancer progression. Methods This study represents the first systematic multi-omics investigation of FBXO2 across 33 TCGA cancer types, thoroughly characterizing its expression patterns, mutational landscape, epigenetic modifications, and immune infiltration relationships. To investigate the functional role of FBXO2 in COAD, this study successfully established stable FBXO2-knockdown (shFBXO2) and FBXO2-overexpression (oeFBXO2) cell models in the colorectal cancer cell lines Caco2 and HCT116. CCK-8 cell proliferation assay and colony formation assay were conducted to evaluate the role of FBXO2 in the proliferation of colorectal cancer cells. While, regarding metastatic phenotypes, transwell migration and invasion assays were carried out. Results Through an integrated multi-omics analysis, we identified pronounced tumor-type-specific expression patterns of FBXO2 and demonstrated its strong diagnostic and prognostic potential in multiple malignancies. Functional assays further revealed that FBXO2 knockdown markedly inhibited the proliferation, migration, and invasion of colorectal cancer cells. Conclusion We established FBXO2 as a potential prognostic biomarker and therapeutic target and showed that FBXO2 expression may serve as a meaningful indicator of tumor initiation and progression in colorectal cancer. FBXO2 Colorectal cancer Cancer progression Multi-omics analysis Genomic instability Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Malignant tumors are widely recognized as a major global health burden (Bray et al., 2018 ). Although medical technologies have advanced considerably, mortality rates associated with malignant cancers continue to rise, highlighting their ongoing threat to human life and longevity (Santucci et al., 2020 ). These unsatisfactory clinical outcomes emphasize the need for a deeper investigation into the biological mechanisms that drive tumor progression. Dysregulation of critical genes, including both tumor suppressors and oncogenes, contributes to uncontrolled cellular proliferation and malignant transformation (Cancer Genome Atlas Research et al., 2013 ).Compounding this challenge, the tumor microenvironment is highly complex, shaped by diverse genetic and epigenetic alterations and aberrant activation of signaling pathways, all of which may promote therapeutic resistance (Saidak et al., 2021 ). Consequently, the identification of pan-cancer signature genes is essential for uncovering molecular features shared across different cancer types and for discovering potential therapeutic targets. In recent years, multi-omics approaches have proven especially powerful for pan-cancer research, enabling the discovery of numerous biomarkers closely linked to metastatic progression and clinical outcomes (Liu, Zhang, Dai, Xie, & Li, 2020 ; Z. Wang, Zhang, & Cheng, 2020 ). Previous studies have indicated that aberrant expression of F-box proteins plays a critical role in malignant tumor progression by modulating the accumulation or degradation of substrate proteins (Z. Wang, Liu, Inuzuka, & Wei, 2014 ; Yumimoto, Yamauchi, & Nakayama, 2020 ). For instance, FBXO6 functions as an oncogene in ovarian cancer, whereas FBXO16 acts as a tumor suppressor (M. Ji, Zhao, Li, Xu, Shi, Li, Wang, Huang, Ji, et al., 2021 ; M. Ji, Zhao, Li, Xu, Shi, Li, Wang, Huang, & Liu, 2021). FBXO2 (F-box only protein 2) is distinguished by its high specificity for glycoproteins, mediated through its F-box-associated (FBA) domain (Yoshida et al., 2002 ). Notably, FBXO2 regulates neuronal function via ubiquitin-dependent degradation of neuronal glycoproteins (Atkin et al., 2015 ). Emerging evidence highlights the functional heterogeneity of FBXO2 across cancer types: it acts as an oncogenic driver in some malignancies but exhibits tumor-suppressive properties in others. Specifically, FBXO2 promotes glioma cell proliferation(Buehler et al., 2023 ), accelerates hepatocellular carcinoma progression by facilitating USP49 degradation(Hang et al., 2025 ), and contributes to ovarian tumorigenesis by targeting glycosylated SUN2(J. Ji et al., 2022 ). In thyroid cancer, FBXO2 knockout triggers apoptosis by inhibiting ubiquitin-mediated degradation of p53, markedly suppressing tumor growth (Guo, Ren, & Qiu, 2024 ). Additionally, FBXO2 dysregulation is implicated in the progression of colorectal and gastric cancers (Sun et al., 2018 ; Wei et al., 2018 ). Conversely, in renal cell carcinoma, FBXO2 inhibits tumor progression by mediating ubiquitination and degradation of the WEE1 protein (L. Wang et al., 2025 ). Despite these insights, FBXO2’s role in many other tumor types remains poorly defined. To address this gap, we performed comprehensive pan-cancer analyses integrating multi-omics data to systematically characterize FBXO2’s functions across malignancies. Materials and methods Reagents and oligos used in study were listed in Table S1 . Mice Six- to eight-week-old female BALB/c nude mice were purchased from the Nanjing Animal Center, Nanjing, China. All mice were bred and kept in the Animal Center of Nankai University under specific pathogen-free (SPF) conditions. Every procedure was conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Model Animal Research Center. All mouse experiments were approved by the Animal Ethics Committee of Nankai University. The maximal permitted tumor burden approved by the Animal Ethics Committee of Nankai University was a tumor volume of 2000 mm³.This limit was not exceeded in any of the animals used in this study. Throughout the experiments, variables such as environmental influences, parental genotypes, and husbandry conditions were strictly controlled. Data Collection Normalized transcriptomic data from batch-corrected pan-cancer and corresponding normal tissues were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Data download, normalization, and integration of gene expression profiles, prognostic information, and immune-related datasets across multiple cancer types were performed using the TCGAbiolinks R package (version 2.28.4). Analysis of FBXO2 Gene Expression in Pan-Cancer and Normal Tissues . Transcriptomic data for FBXO2 were obtained from TCGA, encompassing 33 cancer types (including LIHC, BRCA, among others) and corresponding normal tissues. Differential mRNA expression of FBXO2 between tumor and normal groups was assessed using the Wilcoxon rank-sum test (Mann-Whitney U test)(Fangal, Saferali, Castaldi, Hersh, & Weiss, 2024 ). Additionally, FBXO2 expression differences across cancer stages (I-IV) were analyzed using data from the UALCAN database. Protein expression levels of FBXO2 in tumor versus normal tissues were examined utilizing datasets from The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) ( https://proteomics.cancer.gov/programs/cptac ). All expression analyses and visualizations were performed using the ggplot2 package (version 3.5.1) in R (version 4.4.1). Prognostic Analysis Survival analyses of FBXO2 expression, including Overall Survival (OS), Disease-Specific Survival (DSS), Disease-Free Interval (DFI), and Progression-Free Interval (PFI), were conducted and visualized using Kaplan-Meier curves generated with the survminer R package (version 0.5.0). Multivariate Cox proportional hazards regression models were employed to assess the association between FBXO2 expression levels and survival outcomes, reporting hazard ratios (HR) with corresponding 95% confidence intervals (CI). Cox Proportional Hazards Regression Analysis Cox proportional hazards regression analysis was conducted using a multivariate model that included FBXO2 expression levels alongside clinical covariates such as age, gender, and pathological stage. This approach was employed to assess the independent prognostic value of FBXO2 expression in both the GSE39582 and TCGA cohorts. HRs and corresponding 95% CIs were calculated to quantify the risk associated with each variable concerning relapse-free survival (RFS). All statistical analyses were performed using the survival R package (version 3.7), with a p-value < 0.05 considered statistically significant. Analysis of tumor-associated genomic features of FBXO2 Genomic alteration frequencies of FBXO2, including amplification, deletion, and mutation rates, were analyzed across cancers using the cBioPortal database (version 6.0.24). Tumor mutation burden (TMB), representing the number of non-synonymous mutations per megabase of exon sequence, was calculated accordingly. Microsatellite instability (MSI) scores were assessed using tools such as MSIsensor (version 0.5). The relationship between FBXO2 expression and five mismatch repair (MMR) genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) was evaluated using Pearson correlation analysis (R package stats, version 4.4.1), with results visualized via the ggnewscale (v0.5.0) and jjPlot (v0.0.3) packages. To explore the prognostic significance of FBXO2 copy number variations (CNVs), Kaplan-Meier survival curves were generated using the Copy Number module of the TIDE (Tumor Immune Dysfunction and Exclusion) platform. Additionally, cancer stemness indices across tumor types were quantified using the TCGA analyze_Stemness function in the TCGA biolinks R package (version 2.28.4), and their correlations with FBXO2 mRNA expression were assessed. Immune Infiltration Assessment The association between FBXO2 mRNA expression and immune cell infiltration was evaluated using CIBERSORT ( https://gdc.cancer.gov/about-data/publications/ panimmune). Additionally, correlations between FBXO2 expression and the abundance of various immune cell subpopulations were assessed with the ESTIMATE algorithm (R package estimate , version 1.0.4). Data from the ImmPort database were further analyzed to explore the relationships between FBXO2 expression and genes involved in major histocompatibility complex (MHC) molecules, chemokines, and their receptors. Pearson correlation analysis was employed for all correlation calculations (R package stats ), and heatmaps visualizing these associations were generated using the pheatmap package (version 1.0.12). DNA methylation analysis Methylation levels across pan-cancer samples from the TCGA database were compared using the scCancer Explorer tool (version 1.0), with group differences assessed via the Wilcoxon rank-sum test. Subsequently, the relationship between FBXO2 promoter methylation, cytotoxic T lymphocyte (CTL) infiltration, and patient survival was investigated using the Methylation module of the Tumor Immune Dysfunction and Exclusion (TIDE) platform. Correlations between FBXO2 expression and four key methyltransferases were visualized using a circos plot generated with the circlize R package (version 0.4.16). Clinically relevant alternative splicing analyses of FBXO2 Clinically relevant AS events of FBXO2 were identified using the ClinicalAS tool available on the OncoSplicing server, leveraging data from the SpliceSeq and SplAdder projects. Visualization of splicing percentages (PSI values) across TCGA cancer types and GTEx normal tissues was performed using the PanPlot function. The PanDiff plot was employed to compare PSI differences between tumor samples and matched adjacent normal tissues or GTEx controls. Additionally, Kaplan-Meier survival analyses were conducted to assess the prognostic significance of specific AS events across pan-cancer cohorts. PPI and functional enrichment analysis The STRING database (version 12.0) was utilized to construct the protein-protein interaction (PPI) network for FBXO2. A minimum interaction score threshold of 0.4 was applied to ensure high confidence in the identified interactions. To simplify the network, the maximum number of interactors displayed was limited to 20. The top 20 interacting proteins were then selected for subsequent functional enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler R package (version 4.8.1). Immunofluorescence Immunofluorescence staining was performed on paraffin-embedded pan-cancer tissue sections to validate the potential of FBXO2 as an NK cell biomarker, following a previously reported protocol with modifications (Su et al., 2014 ). Tissue sections from five cancer types were fixed in 4% paraformaldehyde-buffered saline, embedded in paraffin, sectioned, and stored at 4°C until use. Sections were blocked with 5% goat serum blocking buffer and then incubated overnight with primary antibodies: anti-FBXO2 (rabbit, 1:200, A8579) and anti-CD56 (mouse, 1:100, 60238-1-Ig). After washing with PBS, sections were incubated at room temperature with Alexa Fluor 488-conjugated secondary antibody (1:200, SA100013-2) and Alexa Fluor 594-conjugated secondary antibody (1:200, SA100013-3). Following three washes with PBS, nuclei were counterstained with DAPI. Finally, stained sections were prepared for scanning and image acquisition. Cell lines and culture The human colon cancer cell line HCT116 and Caco-2 was obtained from the American Type Culture Collection (ATCC, catalog number CCL-247 for HCT116 and HTB-37 for Caco-2). Cell line HCT116 and Caco-2 were cultured in Minimum Essential Medium (MEM; Gibco, C12571500BT) supplemented with 10% fetal bovine serum (FBS; Gibco, C0235) and 1% penicillin/streptomycin (Gibco, 15140148). Cells were maintained at 37°C in a humidified incubator with 5% CO₂. Cell transfection HCT116 and Caco-2 cells were seeded in 6-well plates and transfected with lentiviral particles (Genechem) at a multiplicity of infection (MOI) of 20 for 24 hours. Following transfection, cells were selected with 3 µg/mL puromycin to establish stable knockdown or overexpression lines. RNA isolation and qRT-PCR Total RNA was extracted from transfected cells using TRIzol reagent (Sparkjade, AC0101-B) according to the manufacturer’s instructions. FBXO2 gene expression was quantified by quantitative real-time PCR (qRT-PCR) using SYBR Green Master Mix (Yeasen, 11201ES50) on a Bio-Rad Real-Time PCR Detection System. Relative expression levels were calculated using the 2^ −ΔΔCT method. Western blotting Transfected cells were washed twice with PBS and lysed in appropriate lysis buffer to extract total protein. Protein samples were separated by SDS-PAGE and transferred onto membranes, which were blocked with 5% non-fat milk for 1 hour at room temperature. Membranes were then incubated overnight at 4°C with primary antibody against FBXO2 (rabbit, 1:200, A8579). Following three washes with PBST, membranes were incubated with secondary antibodies. Protein bands were detected using an enhanced chemiluminescence (ECL) system. Cell proliferation and trans-well assays HCT116 and Caco-2 cells transfected with shRNA targeting FBXO2 were harvested at approximately 90% confluence and seeded into 96-well plates at a density of 3 × 10³ cells per well (n = 5 replicates per group). Cells were maintained under standard culture conditions (37°C, 5% CO₂). Proliferation was assessed at 0, 24-, 48-, 72- and 96-hours post-seeding using the CCK-8 assay kit (Beyotime, C0038) following the manufacturer’s instructions. Briefly, 10 µL of CCK-8 reagent was added to each well and incubated for 2 hours, after which absorbance was measured at 450 nm using a microplate reader. Cell migration and invasion were evaluated using Transwell chambers. Migration assays were performed in uncoated chambers, whereas invasion assays used chambers coated with Matrigel. For both assays, 1 × 10⁵ cells were seeded per chamber. After incubation, cells that migrated or invaded through the membrane were fixed, stained with crystal violet, and quantified by counting five random fields per replicate (n = 3 independent experiments). Plate clone formation assay Transfected cells were seeded into 6-well plates at clonal densities ranging from 200 to 1,000 cells per well. Cells were cultured at 37°C with 5% CO₂ for 10–14 days, with media refreshed every 72 hours. Colonies were washed with PBS, fixed in 4% paraformaldehyde for 20 minutes at room temperature, and stained with 0.5% crystal violet solution (prepared in 20% methanol) for 30 minutes. After rinsing and air drying, colonies containing ≥ 50 cells were manually counted. Statistical analyses Data were analyzed using two-tailed Student’s t-tests, one-way ANOVA with Bonferroni post hoc correction, or Mann-Whitney U tests, as appropriate, using GraphPad Prism 7. Statistical significance was defined as *P < 0.05; **P < 0.01; ***P < 0.001. Results Pan-cancer analyses of FBXO2 expression and prognosis In this study, a comprehensive pan-cancer analysis using TCGA data revealed significant tissue-specific variation in FBXO2 mRNA expression across malignant tumors. FBXO2 was markedly upregulated (fold change > 2.0, FDR < 0.05) in ten cancer types, including bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), lung adenocarcinoma (LUAD), rectal adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Conversely, downregulation trends were observed in breast carcinoma (BRCA), glioblastoma multiforme (GBM), and prostate adenocarcinoma (PRAD) (Fig. 1 A). Paired-sample analyses within TCGA further confirmed significantly higher FBXO2 expression in tumor tissues compared to adjacent normal tissues in COAD, ESCA, HNSC, and THCA (Fig. 1 B), highlighting its potential functional relevance in these cancers. Analysis through the UALCAN database demonstrated a positive correlation between FBXO2 expression and clinical stage progression in multiple malignancies. Specifically, cancers with elevated FBXO2 expression, such as ESCA, LIHC, and THCA, exhibited increasing mRNA levels with advancing tumor stages (Fig. 1 C). Significant associations between FBXO2 expression and advanced clinical stages were also noted in UCEC, READ, BLCA, and cervical squamous cell carcinoma (CESC) (Supplementary Fig. 1). Multidimensional survival analyses based on TCGA data (Supplementary Figures S2A-D) and multivariate Cox proportional hazards models, adjusting for age, gender, and pathological stage, revealed that high FBXO2 expression correlated with poorer DFI in cholangiocarcinoma (CHOL), COAD, and stomach adenocarcinoma (STAD), but with improved survival in sarcoma (SARC). DSS was reduced in CHOL, HNSC, and lung squamous cell carcinoma (LUSC), while mesothelioma (MESO) patients with high FBXO2 expression exhibited better survival outcomes. OS and PFI were significantly decreased in ESCA and HNSC but improved in MESO (Figs. 1 D-G, and Supplementary Figures S2E-H). Collectively, these findings suggest that FBXO2 may act as a tumor promoter in CHOL, COAD, ESCA, and HNSC, yet function as a tumor suppressor in MESO, underscoring its context-dependent gene function across cancer types. This duality likely reflects complex interactions involving distinct signaling pathways, regulatory networks modulated by FBXO2, and tumor heterogeneity. Previous studies have reported significantly elevated FBXO2 mRNA levels in various cancers compared to normal tissues. To validate these findings at the protein level, we performed immunofluorescence staining on clinical samples from COAD, ESCA, LIHC, and THCA, comparing FBXO2 protein expression between tumor and adjacent normal tissues. The results showed significantly higher FBXO2 protein levels in tumor tissues across all four cancer types (Figs. 2 A-D), corroborating the mRNA expression data and underscoring the potential importance of FBXO2 in the pathogenesis of COAD, ESCA, LIHC, and THCA. FBXO2 is associated with genomic alterations and instability To investigate the genomic features of FBXO2 in tumorigenesis, we conducted a comprehensive analysis of genomic variations. Data from cBioPortal revealed significant copy number amplification of FBXO2 in ESCA and pronounced deletions in CHOL. Notably, the frequency of FBXO2 single nucleotide variants (SNVs) was generally low (< 1%) across most cancer types (Figs. 3 A-B). Further evaluation using the TIDE platform indicated that FBXO2 copy number amplification was significantly associated with poorer prognosis in carcinosarcoma (CARC) and lower grade glioma (LGG) (Fig. 3 C). Genomic instability analyses showed a positive correlation between FBXO2 expression and TMB in acute myeloid leukemia (LAML), while negative correlations were observed in BLCA and three other cancers. Regarding MSI, FBXO2 expression correlated positively with testicular germ cell tumors (TGCT) but negatively with COAD and UCEC (Fig. 3 D). Homologous recombination deficiency (HRD) analysis revealed significant positive correlations between FBXO2 and UCEC, among other cancers, whereas negative correlations were found in uveal melanoma (UVM) and kidney chromophobe carcinoma (KICH) (Fig. 3 E). Aneuploidy scores demonstrated positive associations with FBXO2 expression in thymoma (THYM) and UCEC (Fig. 3 F). Considering the predictive role of tumor-specific neoantigen load in immunotherapy response (Jumper et al., 2021 ), we examined the relationship between FBXO2 and neoantigen burden based on somatic mutation profiles. By identifying potential MHC-binding neoantigens derived from SNVs and indels (N. Zhang et al., 2021 ), we found that FBXO2 expression was significantly negatively correlated with neoantigen load in UCEC, adrenocortical carcinoma (ACC), and HNSC (Figs. 3 G-H). Collectively, these findings suggest that FBXO2 may contribute to tumor immune evasion through modulation of genomic stability and neoantigen generation. FBXO2 Is associated with DNA repair and methylation The maintenance of genomic stability in tumors depends on complex DNA damage repair mechanisms, including MMR and homologous recombination repair (HRR), which also influence tumor stemness (Gorodetska, Kozeretska, & Dubrovska, 2019 ; X. Wang et al., 2021 ). Our comprehensive analysis of TCGA pan-cancer data revealed that FBXO2 expression is significantly positively correlated with key MMR genes, such as EPCAM, MLH1, and MSH2, in HNSC, kidney renal papillary cell carcinoma (KIRP), and GBM, with the strongest correlation observed in LGG (Fig. 4 A). Furthermore, FBXO2 expression showed a strong positive correlation with tumor stemness indices in LAML but a negative correlation in kidney renal clear cell carcinoma (KIRC) (Fig. 4 B). To investigate the role of FBXO2 methylation in tumor progression, we conducted systematic epigenetic analyses. Paired-sample comparisons across multiple cancers demonstrated significantly elevated FBXO2 promoter methylation in tumor tissues relative to adjacent normal tissues, notably in KIRC, breast invasive carcinoma (BRCA), and others (Fig. 4 C). Survival analyses indicated that high FBXO2 promoter methylation was significantly associated with improved OS in patients with LGG, pancreatic adenocarcinoma (PAAD), ovarian cancer (OV), and sarcoma (SARC) (Fig. 4 D). Notably, FBXO2 expression exhibited broad positive correlations with RNA modification enzymes involved in m^1A, m^5C, and m^6A modifications across diverse cancer types (Fig. 4 E). Integrated DNA methylation pseudotime (DMPsi) analysis revealed significant negative correlations between FBXO2 expression and DNA methyltransferase activity in THYM, skin cutaneous melanoma (SKCM), MESO, and LGG, whereas positive correlations were observed in COAD, TGCT, STAD, and OV (Fig. 4 F). These multi-omics findings highlight the multifaceted role of FBXO2 in tumorigenesis, linking its expression to DNA repair, epigenetic regulation, and tumor stemness. Alternative FBXO2 splicing predicted survival outcomes AS is a pivotal post-transcriptional regulatory mechanism that generates diverse mRNA isoforms, thereby modulating protein function and non-coding RNA expression, with a well-established role in tumorigenesis (Pradella, Naro, Sette, & Ghigna, 2017 ). Our analysis focused on FBXO2 AS events identified an aberrantly regulated intron retention event, Intron_Retention_8113, across multiple malignancies. Notably, the percent spliced in (PSI) value of this event was significantly elevated in tumor tissues compared to adjacent normal tissues in LIHC and READ (Fig. 5 A). Comparative analyses further revealed pronounced PSI differences in PAAD and GBM (Fig. 5 B). Clinical correlation analyses demonstrated significant associations between FBXO2_Intron_Retention_8113 and patient prognosis in MESO, HNSC, and BRCA (Figs. 5 C-D). Survival analyses indicated that high PSI values of this splicing event correlated with prolonged OS in KICH but with reduced OS in LUSC (Figs. 5 E-F). These findings underscore the importance of FBXO2 AS in tumorigenesis and suggest that distinct transcript isoforms may contribute to tumor heterogeneity. The contrasting prognostic implications across cancer types likely reflect tissue-specific regulatory mechanisms, highlighting the potential for splicing-based precision therapies tailored to cancer subtype. FBXO2 is involved in metabolism and immune pathways To elucidate the functional roles and interaction networks of FBXO2 in cancer, we first identified FBXO2-interacting proteins using the STRING database. Following stringent filtering, 20 core interactors were identified, including E3 ubiquitin ligases such as STUB1 and BTRC, the tumor suppressor FBXW7, and the cullin-associated and neddylation-dissociated protein CAND1 (Fig. 6 A). KEGG and GO enrichment analyses were conducted based on these interactors. KEGG pathway analysis highlighted FBXO2’s involvement in five key pathways: ubiquitin-mediated proteolysis, protein processing in the endoplasmic reticulum, Shigellosis infection, circadian rhythm, and Hedgehog signaling (Figs. 6 B-C). GO analysis further revealed significant enrichment in processes related to protein ubiquitination and proteasome-dependent degradation (Fig. 6 D). Importantly, Gene Set Enrichment Analysis (GSEA) uncovered strong associations between FBXO2 and metabolic pathways, including oxidative phosphorylation and bile acid metabolism (Figs. 6 E-F), suggesting that FBXO2 may modulate the tumor microenvironment through regulation of metabolic reprogramming. Collectively, these systematic bioinformatics analyses offer valuable insights into the molecular mechanisms by which FBXO2 contributes to tumorigenesis. FBXO2 correlated with immune infiltration and cytokine-mediated immune modulations To further investigate FBXO2’s role in regulating the tumor immune microenvironment, we conducted a pan-cancer analysis of ESTIMATE scores. FBXO2 expression demonstrated cancer type-specific correlations with ESTIMATE scores, showing positive associations in BLCA, CESC, LAML, LUSC, PRAD, THCA, and UVM, but negative correlations in KICH, KIRC, KIRP, OV, PCPG, STAD, and TGCT. Similar tissue-specific patterns emerged in stromal cell infiltration analyses: FBXO2 was positively correlated with stromal scores in BLCA and LAML, while negative correlations were observed in KICH and KIRC (Fig. 7 A). Further analyses revealed significant associations between FBXO2 expression and regulation of the chemokine network. In KICH and THYM, FBXO2 showed significant negative correlations with inflammation-related chemokines (CXCL2, CXCL5) and chemokine receptors (CCR1, CCR2) (Figs. 7 B-C). Notably, FBXO2’s relationship with MHC molecule expression exhibited cancer-specific patterns: positive correlations with MHC class I molecules (HLA-A, HLA-B) were detected in THCA, UVM, and PRAD, whereas negative correlations were found in OV, TGCT, and LGG (Fig. 7 D). Immune regulatory network analysis further showed that in cancers with negative FBXO2-ESTIMATE correlations (e.g., KICH, OV, STAD), FBXO2 expression negatively correlated with immune checkpoint molecules (PD-L1, CTLA-4) and diverse immune activation/suppression markers. Conversely, positive correlations were observed in LAML and UVM (Figs. 7 E-G). Analysis of immune and molecular subtypes via the TISDB database revealed significant associations between FBXO2 expression and immune subtypes in UCEC and sarcoma (SARC), as well as molecular subtypes in STAD and UCEC (Figs. 7 H-I). Collectively, these multi-dimensional findings highlight the pivotal role of FBXO2 in tumor immune evasion and suggest promising targets for novel immune microenvironment-based therapeutic strategies. FBXO2 is a potential biomarker of infiltration by NK cell Previous studies have established significant correlations between FBXO2 expression and various malignancies (Figs. 1 – 2 ). To validate these findings at higher resolution, we analyzed single-cell transcriptomic data from the Tumor Immune Single-cell Hub (TISCH) database. FBXO2 exhibited significantly elevated expression across single-cell profiles in multiple cancer types (Fig. 8 A), further supporting its involvement in tumorigenesis at the single-cell level. To elucidate the role of FBXO2 in the tumor immune microenvironment, we examined its correlations with 22 immune cell infiltrates using CIBERSORT. FBXO2 expression was significantly negatively correlated with resting CD4 + memory T cells and M2 macrophages, while showing a positive correlation with natural killer (NK) cell infiltration in 13 cancer types (Fig. 8 B). Single-cell subpopulation analysis confirmed that this positive association primarily involved activated NK cells (Fig. 8 C). Notably, MESO exhibited the strongest positive correlation between FBXO2 expression and NK cell infiltration, whereas negative correlations were observed in LIHC and COAD (Fig. 8 B). This tumor type-specific pattern suggests differential functional roles of FBXO2 across cancers. Multiplex immunofluorescence staining further revealed co-localization of FBXO2 with the NK cell marker CD56 in both tumor and adjacent normal tissues of MESO (Fig. 8 D). Similar co-expression patterns were observed in KICH and ESCA (Figs. 8 E-F). Collectively, these multi-omics data spanning genomic, transcriptomic, and protein levels consistently demonstrate that FBXO2 may serve as a novel molecular marker of NK cell infiltration in specific cancers. This discovery highlights the complex and multifaceted functions of FBXO2 in tumor biology and offers new insights into the regulation of the tumor immune microenvironment. FBXO2 plays an important role in colorectal cancer cell proliferation, metastasis and tumorigenesis Building on preliminary analyses that highlighted the significance of FBXO2 in malignancies such as colorectal adenocarcinoma (COAD), LIHC, and THCA, yet lacking sufficient experimental validation, this study specifically focused on elucidating the functional role of FBXO2 in colorectal cancer progression. Before conducting functional experiments, we validated prior observations derived from the TCGA dataset by analyzing an independent external cohort, GSE39582. Consistent with TCGA findings, FBXO2 expression was significantly elevated in tumor tissues compared to normal tissues within the GSE39582 dataset (Supplementary Figure S3A, p < 1 × 1e-4). Furthermore, Kaplan-Meier survival analysis revealed that patients with high FBXO2 expression experienced significantly shorter RFS than those with low expression (Supplementary Figure S3B, Log-rank test, p < 0.045). Multivariate Cox proportional hazards regression analysis confirmed that FBXO2 expression remained an independent prognostic factor for RFS (Hazard Ratio [HR] = 1.57; 95% Confidence Interval [CI], 1.07–2.30; p < 0.05) (Supplementary Figure S3C). Together, these results reinforce FBXO2’s potential as a robust prognostic biomarker and justify further experimental investigation into its mechanistic role in colorectal cancer development. To investigate the functional role of FBXO2, this study successfully established stable FBXO2-knockdown (shFBXO2) and FBXO2-overexpression (oeFBXO2) cell models in the colorectal cancer cell lines Caco2 and HCT116 (Figs. 9 A-B, and Supplementary Figures S4A-B). The CCK-8 cell proliferation assay and colony formation assay demonstrated that knockdown of FBXO2 significantly inhibited cell proliferation and reduced colony-forming ability, whereas overexpression of FBXO2 markedly enhanced both cell proliferation and colony formation (Figs. 9 C-D, and Supplementary Figures S4C-D). Regarding metastatic phenotypes, the wound healing assay showed that FBXO2 overexpression significantly accelerated wound closure at 48 hours, while FBXO2 knockdown delayed the healing process (Fig. 9 E, and Supplementary Figure S4E). Transwell migration and invasion assays further confirmed this trend: FBXO2 overexpression increased the number of cells penetrating the membrane, whereas FBXO2 knockdown significantly suppressed cell migration and invasion (Figs. 9 F-G, Supplementary Figures S4F-G). Together, these results indicate that FBXO2 is a key regulator driving metastasis in colorectal cancer cells. To validate the function of FBXO2 in vivo, a subcutaneous xenograft tumor model was established in nude mice using HCT116 cells with different FBXO2 expression levels. The results showed that tumors formed by FBXO2-overexpressing cells exhibited significantly larger volumes and greater weights compared to the control group. In contrast, tumors derived from FBXO2-knockdown cells were smaller and lighter (Figs. 9 H-J). These in vivo findings further confirm the tumor-promoting role of FBXO2 in colorectal carcinogenesis. In summary, this study comprehensively demonstrates through in vitro and in vivo experiments that FBXO2 plays a crucial role in colorectal cancer progression by promoting cell proliferation, enhancing migratory and invasive capabilities, and increasing tumorigenicity in vivo. Discussion Recent studies have indicated that FBXO2 influences tumor cell processes; however, existing evidence remains limited and sometimes contradictory (13–19). To clarify the relationship between FBXO2 and various cancers, this study conducted a comprehensive multi-omics analysis across 33 cancer types from TCGA. We systematically examined FBXO2 expression, mutation status, epigenetic modifications, and its association with immune infiltration. Our findings reveal that FBXO2 expression is significantly associated with patient prognosis, tumor stage, and the tumor microenvironment in multiple malignancies. Notably, FBXO2 was found to be highly expressed in COAD, LIHC, (THCA), and ESCA. Experimental validation confirmed that FBXO2 protein levels were significantly elevated in tumor tissues compared to adjacent normal tissues across these four cancer types, underscoring its potential regulatory role in tumor development. Tumorigenesis and progression are often accompanied by genomic alterations (Lopez-Otin, Pietrocola, Roiz-Valle, Galluzzi, & Kroemer, 2023 ). In this context, FBXO2 frequently exhibits copy number amplification in certain cancers, such as ESCA, with higher FBXO2 CNV levels correlating with poorer prognosis in malignancies including CARC and LGG. Analyses of TMB and MSI further demonstrated significant positive correlations between FBXO2 expression and several cancer types. Additionally, HRD and aneuploidy scores showed strong positive associations with FBXO2 expression in UCEC and other cancers. Collectively, these findings suggest that FBXO2 may contribute to tumor immune evasion by modulating genomic stability and neoantigen production. The maintenance of genomic stability in malignant tumors depends on complex, multi-layered DNA damage repair systems (Bradley & Anczukow, 2023 ). In this study, we found that FBXO2 potentially contributes to genomic stability across various cancers by modulating key DNA repair pathways, including MMR and HRR. Specifically, FBXO2 expression demonstrated significant positive correlations with critical MMR genes, such as EPCAM, MLH1, and MSH2, in several malignancies, including HNSC, KIRP, and GBM. Concurrently, epigenetic regulation of FBXO2 via promoter methylation appears to be closely associated with cancer development (Davalos & Esteller, 2023 ). Significant differences in FBXO2 promoter methylation levels were observed among normal tissues, primary tumors, and metastatic lesions. For example, FBXO2 promoter methylation was markedly elevated in tumor tissues relative to adjacent normal tissues in diverse cancers, including GBM and BRCA. These findings suggest that FBXO2-related genomic instability may play a regulatory role in cancer initiation and progression, offering novel insights into its pivotal function in tumorigenesis. Functional enrichment analyses revealed that FBXO2 is involved in various metabolic pathways and disease processes, highlighting its potential to influence the tumor microenvironment through metabolism-immune crosstalk (Karami Fath et al., 2024 ; Wu et al., 2021 ; S. Zhang et al., 2024 ). Building on this, we performed in-depth investigations into FBXO2’s role in tumor immunomodulation. Our study systematically uncovers, for the first time, that FBXO2 expression correlates positively with both immune-activating and immune-suppressive molecules across multiple cancer types. Moreover, GSEA identified significant associations between FBXO2 and metabolic pathways such as oxidative phosphorylation and bile acid metabolism, suggesting that FBXO2 may modulate the tumor microenvironment by regulating metabolic reprogramming. The prognosis of cancer patients is often influenced by tumor immune evasion and the tumor microenvironment(Lawal et al., 2021 ). To gain deeper insight into the role of FBXO2 within the tumor immune microenvironment, this study comprehensively assessed its correlation with the infiltration of 22 immune cell types. FBXO2 exhibited a significant positive correlation with NK cell infiltration in 13 cancer types, predominantly associated with activated NK cell subsets. NK cells are critical effectors in anti-tumor immunity, capable not only of directly recognizing and lysing tumor cells through cytolytic granule release but also modulating immune responses via secretion of specific chemokines(Shimasaki, Jain, & Campana, 2020 ). To further evaluate whether FBXO2 expression is closely associated with NK cell infiltration in specific cancers, co-expression of FBXO2 and the NK cell marker CD56 was examined in selected malignancies using fluorescence staining. The experimental results confirmed co-localization of FBXO2 and CD56 in multiple cancer types. Given that NK cell infiltration generally correlates with tumor suppression, these findings suggest that, in contrast to its tumor-promoting role in cancers such as COAD and LIHC, FBXO2 may exert tumor-suppressive functions in malignancies like MESO. This observation aligns with the prognostic analyses (Figs. 1 D-G, and Figs. 2 E-H) and highlights the tumor-type-specific roles of FBXO2. However, since the study included a limited range of cancers, whether FBXO2 co-localizes with CD56 in other cancer types remains to be determined. Moreover, defining FBXO2 as a novel biomarker for NK cell infiltration requires further functional validation. Given the overexpression of FBXO2 and its association with poor prognosis in COAD, LIHC, and THCA, coupled with its negative correlation with NK cell infiltration in COAD and LIHC, this study focused on elucidating FBXO2’s functional role in these cancers, particularly COAD. Experimental evidence confirmed that FBXO2 plays a critical role in colorectal cancer cell proliferation, migration and tumorigenesis, consistent with previous studies indicating that FBXO2 may regulate tumor growth via ubiquitin-proteasome system (UPS)-mediated degradation of N-cadherin (18). Since aberrant N-cadherin expression promotes colon cancer progression through β-catenin dysregulation(Zhao et al., 2022 ), these findings collectively suggest that FBXO2 upregulation is closely associated with colorectal cancer proliferation and metastasis. FBXO2 thus emerges as a potential biomarker for colorectal cancer metastasis, although further functional experiments are necessary to confirm this role. Given the similar association between FBXO2 overexpression and poor prognosis in hepatocellular carcinoma and thyroid cancer, it is plausible that FBXO2 performs comparable functions in these malignancies, warranting experimental verification. This study represents the first systematic multi-omics investigation of FBXO2 across 33 TCGA cancer types, thoroughly characterizing its expression patterns, mutational landscape, epigenetic modifications, and immune infiltration relationships. Through pan-cancer analysis, we establish FBXO2 as a potential prognostic biomarker and therapeutic target. Our findings elucidate FBXO2’s dual role in maintaining tumor genomic stability and modulating the tumor immune microenvironment, while also demonstrating its contribution to tumor proliferation, migration and tumorigenesis in colorectal cancer. These insights provide a foundation for future mechanistic studies and the development of novel therapeutic strategies aimed at improving clinical outcomes for cancer patients. Conclusions This pan-cancer study delineates the multifaceted role of FBXO2 in tumorigenesis. FBXO2 exhibits significant, context-dependent dysregulation across cancers, correlating with clinical stage and patient prognosis. It contributes to genomic stability through associations with DNA repair pathways, copy number alterations, and epigenetic regulation. FBXO2 is also implicated in shaping the tumor immune microenvironment, showing notable correlations with immune cell infiltration (particularly activated NK cells) and immune-modulatory molecules. Functional validation in colorectal cancer confirms that FBXO2 promotes tumor cell proliferation, migration, invasion, and in vivo tumorigenesis. Collectively, these findings position FBXO2 as a promising prognostic biomarker and therapeutic target across multiple cancers, highlighting its dual functions in genomic integrity and immune modulation. Declarations Consent for publication Not applicable. Ethics statement The human tumor tissue sections used in this study were obtained from the First Hospital of Qinhuangdao. This study was reviewed and approved by the Institutional Review Boards (IRB) of the First Hospital of Qinhuangdao (Approval No.: 2025K-093-01) and Nankai University (Approval No.: NKUIRB2025134). Written informed consent was waived by the ethics committees due to the retrospective nature of the study using anonymized archival specimens. This study strictly adheres to the ethical principles outlined in the Declaration of Helsinki. Ethics approval All mice were bred and kept in the Animal Center of Nankai University under specific pathogen-free (SPF) conditions. Every procedure was conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Model Animal Research Center. All mouse experiments were approved by the Animal Ethics Committee of Nankai University. The maximal permitted tumor burden approved by the Animal Ethics Committee of Nankai University was a tumor volume of 2000 mm³.This limit was not exceeded in any of the animals used in this study. Throughout the experiments, variables such as environmental influences, parental genotypes, and husbandry conditions were strictly controlled. Conflict 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. Funding This research was supported by NSFC grants, 82271779, 81901677, 81802372; Tianjin science and technology commission (23JCYBJC01760); Hebei Natural Science Foundation (Grant No. C2025107013). Author Contribution WM: Conceptualization, Investigation, Writing–original draft, Software, Supervision, Data curation, Project administration, Methodology. ZH: Visualization, Writing–original draft, Conceptualization, Methodology, Supervision, Project administration, Investigation. GQ: Formal analysis, Methodology, Software, Validation, Writing–review & editing. ZY: Formal analysis, Methodology, Software, Validation, Writing–review & editing. WJ: Funding acquisition, Investigation, Project administration, Supervision, Writing–review & editing. GY: Funding acquisition, Conceptualization, Project administration, Writing–review & editing. All authors read and approved the final manuscript. Acknowledgments This work was implemented based on the platform of Nankai University and the First Hospital of Qinhuangdao. Data Availability The datasets used in this study are publicly available from TCGA, CPTAC, UALCAN, cBioPortal (v6.0.24), ImmPort, TIDE, and OncoSplicing. The following is a detailed description of the data sources utilized in this study.TCGA Transcriptomic & Clinical Data: Gene expression quantification data (workflow: STAR - Counts) for 33 cancer types were retrieved from the GDC Data Portal (https://portal.gdc.cancer.gov/) via the TCGAbiolinks R package. Clinical phenotype and survival data were obtained from the UCSC Xena platform (https://xenabrowser.net/datapages/), specifically utilizing the ‘Phenotype’ (PANCAN\_clinicalMatrix) and ‘Survival Data’ (Survival\_Supplemental Table\_S1\_20171025\_xena\_sp) datasets from the TCGA Pan-Cancer (PANCAN) cohort. Proteomic Data: Proteomic expression analysis was conducted using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, which was accessed and analyzed via the UALCAN web portal (specifically the ‘CPTAC Analysis’ module, available at [https://ualcan.path.uab.edu/analysis-prot.html).](https:/ualcan.path.uab.edu/analysis-prot.html).) cBioPortal (v6.0.24): The genetic alteration frequency (mutations, amplifications, deep deletions) and mutation landscape of FBXO2 were analyzed using the ‘TCGA PanCancer Atlas Studies’ dataset (https://www.cbioportal.org/). TIDE: The prognostic value of FBXO2 copy number variations (CNVs) was analyzed using the ‘Copy Number’ module of the Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/). Immune Cell Proportions: Pre-calculated CIBERSORT immune cell fractions were retrieved from the GDC ‘The Immune Landscape of Cancer’ publication page (https://gdc.cancer.gov/about-data/publications/panimmune), specifically using the file TCGA.Kallisto.fullIDs.cibersort.relative.tsv (Thorsson et al., Immunity 2018) OncoSplicing: Alternative splicing events were analyzed using the OncoSplicing Pan-Cancer module (http://www.oncosplicing.com/indexPanCancer), incorporating data from the SpliceSeq and SplAdder projects. References Atkin G, Moore S, Lu Y, Nelson RF, Tipper N, Rajpal G, Paulson H. Loss of F-box only protein 2 (Fbxo2) disrupts levels and localization of select NMDA receptor subunits, and promotes aberrant synaptic connectivity. J Neurosci. 2015;35(15):6165–78. https://doi.org/10.1523/JNEUROSCI.3013-14.2015 . Bradley RK, Anczukow O. RNA splicing dysregulation and the hallmarks of cancer. Nat Rev Cancer. 2023;23(3):135–55. https://doi.org/10.1038/s41568-022-00541-7 . 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Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets. Signal Transduct Target Ther. 2024;9(1):149. https://doi.org/10.1038/s41392-024-01848-7 . Zhao H, Ming T, Tang S, Ren S, Yang H, Liu M, Xu H. Wnt signaling in colorectal cancer: pathogenic role and therapeutic target. Mol Cancer. 2022;21(1):144. https://doi.org/10.1186/s12943-022-01616-7 . Additional Declarations No competing interests reported. Supplementary Files PSIvalueofallASevents.xlsx supportinginformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8578038","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587684163,"identity":"f84c52ed-4c49-464f-8998-36d319d402a0","order_by":0,"name":"Ming Wei","email":"","orcid":"","institution":"Nankai University School of Medicine, Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Wei","suffix":""},{"id":587684164,"identity":"d5d1843b-fb10-4f17-868a-13825ff40fc3","order_by":1,"name":"Huilin Zang","email":"","orcid":"","institution":"Tianjin Academy of Educational Sciences","correspondingAuthor":false,"prefix":"","firstName":"Huilin","middleName":"","lastName":"Zang","suffix":""},{"id":587684167,"identity":"e045695f-0053-4a72-a1f1-63e1bb623a5b","order_by":2,"name":"Qianqian Gao","email":"","orcid":"","institution":"Nankai University School of Medicine, Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Gao","suffix":""},{"id":587684168,"identity":"c1c79451-a759-4f0e-a2b3-3ee90502b388","order_by":3,"name":"Yuan Zhang","email":"","orcid":"","institution":"Nankai University School of Medicine, Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhang","suffix":""},{"id":587684170,"identity":"1f0819b7-1863-448b-aa85-48040970f4ca","order_by":4,"name":"Jiexia Wen","email":"","orcid":"","institution":"State Key Laboratory of Metastable Materials Science and Technology, Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Jiexia","middleName":"","lastName":"Wen","suffix":""},{"id":587684171,"identity":"69ca1145-4980-464b-b4c1-ab9239fd91ec","order_by":5,"name":"Yunhuan Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYPCCA0DMDCISGNiApASRWtgSSNbCYwDWwkBIi7z74WPSvDvuJPbP7vn24OeONHk+BuaDt3kY7PJwaTE8k5YmzXvmWeKMO2e3G/aeyTFsY2BLtuZhSC7GqaUhx0yat+1wYsON3G0SvG0VjG0MPGbSPAwHEhtwael/A9Ey/0bOM8m/bRX2bQz83/BqkZeA2rLhRg4bkJGTCLSFDa8WA4lnyZZz2w4bb7yRZiYt25aW3MbMZmw5xyAZty39yQdvvG07LDvvRvIzybdtybbz25sf3nhTYYfblgMMLGixwAwWx6EeZEsDA/MH3NKjYBSMglEwCoAAAI9/V6uSnRVgAAAAAElFTkSuQmCC","orcid":"","institution":"Nankai University School of Medicine, Nankai University","correspondingAuthor":true,"prefix":"","firstName":"Yunhuan","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2026-01-12 06:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8578038/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8578038/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102222643,"identity":"5fb4fecf-b677-4058-a3e9-7d1e3f7cc4f4","added_by":"auto","created_at":"2026-02-09 14:07:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6472438,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential FBXO2 expression predicts cancer survival outcomes. \u003cstrong\u003e(A) \u003c/strong\u003eDifferential FBXO2 mRNA expression between tumor (red) and normal tissues (blue) based on TCGA data, analyzed using the Wilcoxon rank-sum test (Mann-Whitney U test). \u003cstrong\u003e(B) \u003c/strong\u003eComparative visualization of FBXO2 mRNA expression across twenty TCGA cancer types. \u003cstrong\u003e(C) \u003c/strong\u003eStage-specific FBXO2 expression patterns in six cancer types obtained from the UALCAN database. \u003cstrong\u003e(D-G) \u003c/strong\u003eCox proportional hazards regression analyses demonstrating the prognostic value of FBXO2 expression for disease-free interval (DFI for D), disease-specific survival (DSS for E), overall survival (OS for F), and progression-free interval (PFI for G). Hazard ratios (HRs) with 95% confidence intervals (CIs) quantify the risk associated with relapse-free survival (RFS). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/88f5ff48ba8961233ce3500b.jpg"},{"id":102297190,"identity":"bd6a3c0a-a129-4150-8459-231a8ffd576f","added_by":"auto","created_at":"2026-02-10 10:26:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1814903,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 protein expression differs significantly between tumor and adjacent normal tissues.\u003cstrong\u003e(A-D)\u003c/strong\u003e Immunofluorescence staining of FBXO2 in paired tumor and adjacent normal tissues from (A) colon adenocarcinoma, (B) esophageal carcinoma, (C) hepatocellular carcinoma, and (D) chromophobe renal cell carcinoma. Scale bar = 50 μm.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/bbef49bc8f90fe91ec331dcb.jpg"},{"id":102297299,"identity":"0ac29a1f-d599-43a6-9d8e-1edd0255936f","added_by":"auto","created_at":"2026-02-10 10:26:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4878249,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 is associated with genomic instability across cancers. \u003cstrong\u003e(A)\u003c/strong\u003e Genomic alteration frequency of FBXO2 in pan-cancer datasets from cBioPortal, including mutation, amplification, and deletion events. \u003cstrong\u003e(B)\u003c/strong\u003e Landscape of FBXO2 single-nucleotide variants (SNVs), including missense, frameshift, and splice-site mutations. \u003cstrong\u003e(C)\u003c/strong\u003eKaplan-Meier curves from the TIDE platform assessing the prognostic significance of FBXO2 copy number variations (CNVs) in three cancer types. \u003cstrong\u003e(D)\u003c/strong\u003eRadar plots showing correlations between FBXO2 expression and tumor mutation burden (TMB) or microsatellite instability (MSI). \u003cstrong\u003e(E-H)\u003c/strong\u003e Associations between FBXO2 expression and homologous recombination deficiency (HRD), aneuploidy, SNV-derived neoantigens, and indel-derived neoantigens. Dot size indicates correlation strength; color reflects statistical significance.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/0daff158874f0da56046f826.jpg"},{"id":102222645,"identity":"6897d7c6-d1b9-4b5a-9ed1-8f39e0ce52b6","added_by":"auto","created_at":"2026-02-09 14:07:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7370756,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 is involved in DNA repair, stemness, and epigenetic regulation. \u003cstrong\u003e(A)\u003c/strong\u003e Heatmap showing correlations between FBXO2 and five mismatch-repair genes (EPCAM, MLH1, MSH2, MSH6, PMS2) using Pearson correlation; \u003cem\u003ep\u003c/em\u003e values indicated as \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. \u003cstrong\u003e(B) \u003c/strong\u003eCorrelation between FBXO2 expression and tumor stemness index (dot size = sample size, color = \u003cem\u003ep\u003c/em\u003e-value magnitude). \u003cstrong\u003e(C)\u003c/strong\u003eDifferential methylation of FBXO2 between tumor and adjacent normal tissues in multiple cancer types (Wilcoxon test; *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). \u003cstrong\u003e(D)\u003c/strong\u003eTIDE survival analysis evaluating the association between FBXO2 promoter methylation and patient outcomes. \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap of correlations between FBXO2 expression and RNA modification regulators. \u003cstrong\u003e(F)\u003c/strong\u003e Circular plot representing correlations between FBXO2 expression and DNA methyltransferases (DNMT1, DNMT2, DNMT3A, DNMT3B). Rings depict cancer types, DNMTs, correlation coefficients (positive = red, negative = brown), and \u003cem\u003ep\u003c/em\u003e-values (inner blue ring).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/9840258a352f7aaced3e7848.jpg"},{"id":102296901,"identity":"3ab32d78-7183-4230-bbe0-bff58c00647b","added_by":"auto","created_at":"2026-02-10 10:22:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5742792,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 alternative splicing events and their association with tumor immunity and oncogenic pathways. \u003cstrong\u003e(A)\u003c/strong\u003eAnalysis of the FBXO2_Intron_Retention_8113 splicing event across tumor, adjacent normal, and healthy tissues, showing read-in, read-out, and PSI values. \u003cstrong\u003e(B)\u003c/strong\u003e PSI comparison between tumor and normal/adjacent tissues using PanDiff plots. \u003cstrong\u003e(C-D)\u003c/strong\u003e Correlation between FBXO2_Intron_Retention_8113 PSI values and clinical prognosis. \u003cstrong\u003e(E-F) \u003c/strong\u003eKaplan-Meier survival curves evaluating PSI-associated prognosis in KICH and LUSC.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/94054651201f084888d00b85.jpg"},{"id":102297297,"identity":"6e6dde9b-76ff-4822-afc7-22d95e9c5959","added_by":"auto","created_at":"2026-02-10 10:26:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3339187,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional characterization of FBXO2 in chromatin remodeling, immunity, metabolism, and cilia-related pathways. \u003cstrong\u003e(A)\u003c/strong\u003eSTRING-derived protein-protein interaction network showing FBXO2-associated chromatin-modifying complexes (node size = binding affinity; edge thickness = interaction confidence). \u003cstrong\u003e(B) \u003c/strong\u003eKEGG pathway enrichment of FBXO2-associated genes. \u003cstrong\u003e(C)\u003c/strong\u003e Network representation of the top five enriched KEGG pathways. \u003cstrong\u003e(D)\u003c/strong\u003e Gene Ontology (GO) enrichment analysis of FBXO2-related genes. \u003cstrong\u003e(E-F) \u003c/strong\u003ePan-cancer GSEA results showing KEGG (E) and HALLMARK (F) pathways enriched in association with FBXO2 expression.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/cb7f177042c73f4330351e96.jpg"},{"id":102222644,"identity":"519fbbc4-096a-42a5-a568-5eb76970db8d","added_by":"auto","created_at":"2026-02-09 14:07:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8082470,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between FBXO2 and the tumor immune microenvironment. \u003cstrong\u003e(A) \u003c/strong\u003eCorrelations between FBXO2 expression and ESTIMATE, Immune, and Stromal scores. \u003cstrong\u003e(B-D)\u003c/strong\u003e Heatmaps showing correlations between FBXO2 and chemokines (B), chemokine receptors (C), and MHC genes (D). \u003cstrong\u003e(E-G)\u003c/strong\u003e Heatmaps showing correlations between FBXO2 and immune activators, immune checkpoints, and immune suppressors, calculated using Pearson correlation and visualized with \u003cem\u003epheatmap\u003c/em\u003e. \u003cstrong\u003e(H-I)\u003c/strong\u003eAssociations between FBXO2 expression and tumor immune (H) and molecular (I) subtypes based on the TISIDB database.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/7c643197e818b9b78c498ed4.jpg"},{"id":102222646,"identity":"e98addd0-4c32-401f-ac24-cf8fea5a0c1f","added_by":"auto","created_at":"2026-02-09 14:07:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6244255,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 as a pan-cancer biomarker of NK cell infiltration. \u003cstrong\u003e(A) \u003c/strong\u003eTISCH-based heatmap of FBXO2 expression across single-cell clusters from multiple cancer types. \u003cstrong\u003e(B)\u003c/strong\u003e CIBERSORT-based heatmap showing correlations between FBXO2 expression and immune cell subsets. \u003cstrong\u003e(C)\u003c/strong\u003ePartial correlations between FBXO2 expression and NK cell infiltration estimated by multiple TIMER2.0 algorithms (e.g., xCell, EPIC). \u003cstrong\u003e(D-F)\u003c/strong\u003eImmunofluorescence validation of spatial co-expression of FBXO2 (green) and NK cell marker CD56 (red) in MESO, KICH, and ESCA (left: 100 μm; right: 25 μm).\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/cf451b9caa2deacb2276e778.jpg"},{"id":102222638,"identity":"46d255fa-9602-4a27-b191-61c05f1bd726","added_by":"auto","created_at":"2026-02-09 14:07:35","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":453376,"visible":true,"origin":"","legend":"\u003cp\u003eFBXO2 enhances proliferation, migration and tumorigenesis of colorectal cancer cells. \u003cstrong\u003e(A)\u003c/strong\u003eqRT-PCR detection of FBXO2 expression efficiency in HCT116 cells.\u003cstrong\u003e (B) \u003c/strong\u003eWestern blot validation of FBXO2 protein expression levels in HCT116 cells (β-actin served as the internal control). \u003cstrong\u003e(C)\u003c/strong\u003e CCK-8 assay detection of cell proliferation activity within 96 hours for each group(n=6). \u003cstrong\u003e(D)\u003c/strong\u003e Colony formation assay detection of cell clonogenic ability, with quantitative statistics of colony formation efficiency. \u003cstrong\u003e(E) \u003c/strong\u003eWound healing assay detection of cell migration ability at 0h and 48h, with quantitative statistics of wound healing rate(n=6). \u003cstrong\u003e(F)\u003c/strong\u003e Migration assay detection of transmembrane cell count, with quantitative statistics of migrating cells(n=6).\u003cstrong\u003e (G)\u003c/strong\u003e Invasion assay detection of transmembrane cell count, with quantitative statistics of invading cells(n=6).\u003cstrong\u003e (H)\u003c/strong\u003e Photograph of tumor tissues from the nude mouse xenograft tumor experiment (HCT116 cells) (n=6).\u003cstrong\u003e (I)\u003c/strong\u003e Statistics of final tumor weight from the xenograft tumor experiment (n=6). (J) Growth curve of xenograft tumors (monitoring tumor weight changes over 28 days) (n=6).\u003c/p\u003e\n\u003cp\u003eshFBXO2: knockdown group; shNC: knockdown control; oeFBXO2: overexpression group; oeNC: overexpression control. Data are presented as mean ± SD. Statistical analysis was performed using Student’s t-test; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/4129ba8ab0fdf4223b729302.jpg"},{"id":104428770,"identity":"8e02c4a0-c93b-440d-aff1-49e89ae91422","added_by":"auto","created_at":"2026-03-11 15:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":45517309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/0add67ff-700e-47fc-b613-87f4a23046ca.pdf"},{"id":102297459,"identity":"aa83570c-53fc-4cc1-ba77-34f35cf38f1e","added_by":"auto","created_at":"2026-02-10 10:27:28","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":648761,"visible":true,"origin":"","legend":"","description":"","filename":"PSIvalueofallASevents.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/4f30b3aa472204e2337221c6.xlsx"},{"id":102222641,"identity":"24c77e36-45ce-4e09-ba90-01a743e3221e","added_by":"auto","created_at":"2026-02-09 14:07:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3552690,"visible":true,"origin":"","legend":"","description":"","filename":"supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8578038/v1/7440122c651284f6f95e293f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive pan-cancer analysis reveals that FBXO2 as a potential therapeutic target associated with immune infiltration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalignant tumors are widely recognized as a major global health burden (Bray et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although medical technologies have advanced considerably, mortality rates associated with malignant cancers continue to rise, highlighting their ongoing threat to human life and longevity (Santucci et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These unsatisfactory clinical outcomes emphasize the need for a deeper investigation into the biological mechanisms that drive tumor progression. Dysregulation of critical genes, including both tumor suppressors and oncogenes, contributes to uncontrolled cellular proliferation and malignant transformation (Cancer Genome Atlas Research et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Compounding this challenge, the tumor microenvironment is highly complex, shaped by diverse genetic and epigenetic alterations and aberrant activation of signaling pathways, all of which may promote therapeutic resistance (Saidak et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, the identification of pan-cancer signature genes is essential for uncovering molecular features shared across different cancer types and for discovering potential therapeutic targets. In recent years, multi-omics approaches have proven especially powerful for pan-cancer research, enabling the discovery of numerous biomarkers closely linked to metastatic progression and clinical outcomes (Liu, Zhang, Dai, Xie, \u0026amp; Li, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Z. Wang, Zhang, \u0026amp; Cheng, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that aberrant expression of F-box proteins plays a critical role in malignant tumor progression by modulating the accumulation or degradation of substrate proteins (Z. Wang, Liu, Inuzuka, \u0026amp; Wei, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yumimoto, Yamauchi, \u0026amp; Nakayama, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, FBXO6 functions as an oncogene in ovarian cancer, whereas FBXO16 acts as a tumor suppressor (M. Ji, Zhao, Li, Xu, Shi, Li, Wang, Huang, Ji, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; M. Ji, Zhao, Li, Xu, Shi, Li, Wang, Huang, \u0026amp; Liu, 2021). FBXO2 (F-box only protein 2) is distinguished by its high specificity for glycoproteins, mediated through its F-box-associated (FBA) domain (Yoshida et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Notably, FBXO2 regulates neuronal function via ubiquitin-dependent degradation of neuronal glycoproteins (Atkin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Emerging evidence highlights the functional heterogeneity of FBXO2 across cancer types: it acts as an oncogenic driver in some malignancies but exhibits tumor-suppressive properties in others. Specifically, FBXO2 promotes glioma cell proliferation(Buehler et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), accelerates hepatocellular carcinoma progression by facilitating USP49 degradation(Hang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and contributes to ovarian tumorigenesis by targeting glycosylated SUN2(J. Ji et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In thyroid cancer, FBXO2 knockout triggers apoptosis by inhibiting ubiquitin-mediated degradation of p53, markedly suppressing tumor growth (Guo, Ren, \u0026amp; Qiu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, FBXO2 dysregulation is implicated in the progression of colorectal and gastric cancers (Sun et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, in renal cell carcinoma, FBXO2 inhibits tumor progression by mediating ubiquitination and degradation of the WEE1 protein (L. Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite these insights, FBXO2\u0026rsquo;s role in many other tumor types remains poorly defined. To address this gap, we performed comprehensive pan-cancer analyses integrating multi-omics data to systematically characterize FBXO2\u0026rsquo;s functions across malignancies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eReagents and oligos used in study were listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eSix- to eight-week-old female BALB/c nude mice were purchased from the Nanjing Animal Center, Nanjing, China. All mice were bred and kept in the Animal Center of Nankai University under specific pathogen-free (SPF) conditions. Every procedure was conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Model Animal Research Center. All mouse experiments were approved by the Animal Ethics Committee of Nankai University. The maximal permitted tumor burden approved by the Animal Ethics Committee of Nankai University was a tumor volume of 2000 mm\u0026sup3;.This limit was not exceeded in any of the animals used in this study. Throughout the experiments, variables such as environmental influences, parental genotypes, and husbandry conditions were strictly controlled.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eNormalized transcriptomic data from batch-corrected pan-cancer and corresponding normal tissues were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data download, normalization, and integration of gene expression profiles, prognostic information, and immune-related datasets across multiple cancer types were performed using the TCGAbiolinks R package (version 2.28.4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of FBXO2 Gene Expression in Pan-Cancer and Normal Tissues\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTranscriptomic data for FBXO2 were obtained from TCGA, encompassing 33 cancer types (including LIHC, BRCA, among others) and corresponding normal tissues. Differential mRNA expression of FBXO2 between tumor and normal groups was assessed using the Wilcoxon rank-sum test (Mann-Whitney U test)(Fangal, Saferali, Castaldi, Hersh, \u0026amp; Weiss, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, FBXO2 expression differences across cancer stages (I-IV) were analyzed using data from the UALCAN database. Protein expression levels of FBXO2 in tumor versus normal tissues were examined utilizing datasets from The National Cancer Institute\u0026rsquo;s Clinical Proteomic Tumor Analysis Consortium (CPTAC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteomics.cancer.gov/programs/cptac\u003c/span\u003e\u003cspan address=\"https://proteomics.cancer.gov/programs/cptac\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All expression analyses and visualizations were performed using the \u003cem\u003eggplot2\u003c/em\u003e package (version 3.5.1) in R (version 4.4.1).\u003c/p\u003e\n\u003ch3\u003ePrognostic Analysis\u003c/h3\u003e\n\u003cp\u003eSurvival analyses of FBXO2 expression, including Overall Survival (OS), Disease-Specific Survival (DSS), Disease-Free Interval (DFI), and Progression-Free Interval (PFI), were conducted and visualized using Kaplan-Meier curves generated with the survminer R package (version 0.5.0). Multivariate Cox proportional hazards regression models were employed to assess the association between FBXO2 expression levels and survival outcomes, reporting hazard ratios (HR) with corresponding 95% confidence intervals (CI).\u003c/p\u003e\n\u003ch3\u003eCox Proportional Hazards Regression Analysis\u003c/h3\u003e\n\u003cp\u003eCox proportional hazards regression analysis was conducted using a multivariate model that included FBXO2 expression levels alongside clinical covariates such as age, gender, and pathological stage. This approach was employed to assess the independent prognostic value of FBXO2 expression in both the GSE39582 and TCGA cohorts. HRs and corresponding 95% CIs were calculated to quantify the risk associated with each variable concerning relapse-free survival (RFS). All statistical analyses were performed using the \u003cem\u003esurvival\u003c/em\u003e R package (version 3.7), with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eAnalysis of tumor-associated genomic features of FBXO2\u003c/h3\u003e\n\u003cp\u003eGenomic alteration frequencies of FBXO2, including amplification, deletion, and mutation rates, were analyzed across cancers using the cBioPortal database (version 6.0.24). Tumor mutation burden (TMB), representing the number of non-synonymous mutations per megabase of exon sequence, was calculated accordingly. Microsatellite instability (MSI) scores were assessed using tools such as MSIsensor (version 0.5). The relationship between FBXO2 expression and five mismatch repair (MMR) genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) was evaluated using Pearson correlation analysis (R package stats, version 4.4.1), with results visualized via the ggnewscale (v0.5.0) and jjPlot (v0.0.3) packages. To explore the prognostic significance of FBXO2 copy number variations (CNVs), Kaplan-Meier survival curves were generated using the Copy Number module of the TIDE (Tumor Immune Dysfunction and Exclusion) platform. Additionally, cancer stemness indices across tumor types were quantified using the TCGA analyze_Stemness function in the TCGA biolinks R package (version 2.28.4), and their correlations with FBXO2 mRNA expression were assessed.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration Assessment\u003c/h2\u003e \u003cp\u003eThe association between FBXO2 mRNA expression and immune cell infiltration was evaluated using CIBERSORT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdc.cancer.gov/about-data/publications/\u003c/span\u003e\u003cspan address=\"https://gdc.cancer.gov/about-data/publications/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e panimmune). Additionally, correlations between FBXO2 expression and the abundance of various immune cell subpopulations were assessed with the ESTIMATE algorithm (R package \u003cem\u003eestimate\u003c/em\u003e, version 1.0.4). Data from the ImmPort database were further analyzed to explore the relationships between FBXO2 expression and genes involved in major histocompatibility complex (MHC) molecules, chemokines, and their receptors. Pearson correlation analysis was employed for all correlation calculations (R package \u003cem\u003estats\u003c/em\u003e), and heatmaps visualizing these associations were generated using the pheatmap package (version 1.0.12).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA methylation analysis\u003c/h3\u003e\n\u003cp\u003eMethylation levels across pan-cancer samples from the TCGA database were compared using the scCancer Explorer tool (version 1.0), with group differences assessed via the Wilcoxon rank-sum test. Subsequently, the relationship between FBXO2 promoter methylation, cytotoxic T lymphocyte (CTL) infiltration, and patient survival was investigated using the Methylation module of the Tumor Immune Dysfunction and Exclusion (TIDE) platform. Correlations between FBXO2 expression and four key methyltransferases were visualized using a circos plot generated with the circlize R package (version 0.4.16).\u003c/p\u003e\n\u003ch3\u003eClinically relevant alternative splicing analyses of FBXO2\u003c/h3\u003e\n\u003cp\u003eClinically relevant AS events of FBXO2 were identified using the ClinicalAS tool available on the OncoSplicing server, leveraging data from the SpliceSeq and SplAdder projects. Visualization of splicing percentages (PSI values) across TCGA cancer types and GTEx normal tissues was performed using the PanPlot function. The PanDiff plot was employed to compare PSI differences between tumor samples and matched adjacent normal tissues or GTEx controls. Additionally, Kaplan-Meier survival analyses were conducted to assess the prognostic significance of specific AS events across pan-cancer cohorts.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePPI and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe STRING database (version 12.0) was utilized to construct the protein-protein interaction (PPI) network for FBXO2. A minimum interaction score threshold of 0.4 was applied to ensure high confidence in the identified interactions. To simplify the network, the maximum number of interactors displayed was limited to 20. The top 20 interacting proteins were then selected for subsequent functional enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler R package (version 4.8.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence\u003c/h2\u003e \u003cp\u003eImmunofluorescence staining was performed on paraffin-embedded pan-cancer tissue sections to validate the potential of FBXO2 as an NK cell biomarker, following a previously reported protocol with modifications (Su et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Tissue sections from five cancer types were fixed in 4% paraformaldehyde-buffered saline, embedded in paraffin, sectioned, and stored at 4\u0026deg;C until use. Sections were blocked with 5% goat serum blocking buffer and then incubated overnight with primary antibodies: anti-FBXO2 (rabbit, 1:200, A8579) and anti-CD56 (mouse, 1:100, 60238-1-Ig). After washing with PBS, sections were incubated at room temperature with Alexa Fluor 488-conjugated secondary antibody (1:200, SA100013-2) and Alexa Fluor 594-conjugated secondary antibody (1:200, SA100013-3). Following three washes with PBS, nuclei were counterstained with DAPI. Finally, stained sections were prepared for scanning and image acquisition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and culture\u003c/h2\u003e \u003cp\u003eThe human colon cancer cell line HCT116 and Caco-2 was obtained from the American Type Culture Collection (ATCC, catalog number CCL-247 for HCT116 and HTB-37 for Caco-2). Cell line HCT116 and Caco-2 were cultured in Minimum Essential Medium (MEM; Gibco, C12571500BT) supplemented with 10% fetal bovine serum (FBS; Gibco, C0235) and 1% penicillin/streptomycin (Gibco, 15140148). Cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO₂.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell transfection\u003c/h2\u003e \u003cp\u003eHCT116 and Caco-2 cells were seeded in 6-well plates and transfected with lentiviral particles (Genechem) at a multiplicity of infection (MOI) of 20 for 24 hours. Following transfection, cells were selected with 3 \u0026micro;g/mL puromycin to establish stable knockdown or overexpression lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and qRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from transfected cells using TRIzol reagent (Sparkjade, AC0101-B) according to the manufacturer\u0026rsquo;s instructions. FBXO2 gene expression was quantified by quantitative real-time PCR (qRT-PCR) using SYBR Green Master Mix (Yeasen, 11201ES50) on a Bio-Rad Real-Time PCR Detection System. Relative expression levels were calculated using the 2^\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eTransfected cells were washed twice with PBS and lysed in appropriate lysis buffer to extract total protein. Protein samples were separated by SDS-PAGE and transferred onto membranes, which were blocked with 5% non-fat milk for 1 hour at room temperature. Membranes were then incubated overnight at 4\u0026deg;C with primary antibody against FBXO2 (rabbit, 1:200, A8579). Following three washes with PBST, membranes were incubated with secondary antibodies. Protein bands were detected using an enhanced chemiluminescence (ECL) system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation and trans-well assays\u003c/h2\u003e \u003cp\u003eHCT116 and Caco-2 cells transfected with shRNA targeting FBXO2 were harvested at approximately 90% confluence and seeded into 96-well plates at a density of 3 \u0026times; 10\u0026sup3; cells per well (n\u0026thinsp;=\u0026thinsp;5 replicates per group). Cells were maintained under standard culture conditions (37\u0026deg;C, 5% CO₂). Proliferation was assessed at 0, 24-, 48-, 72- and 96-hours post-seeding using the CCK-8 assay kit (Beyotime, C0038) following the manufacturer\u0026rsquo;s instructions. Briefly, 10 \u0026micro;L of CCK-8 reagent was added to each well and incubated for 2 hours, after which absorbance was measured at 450 nm using a microplate reader. Cell migration and invasion were evaluated using Transwell chambers. Migration assays were performed in uncoated chambers, whereas invasion assays used chambers coated with Matrigel. For both assays, 1 \u0026times; 10⁵ cells were seeded per chamber. After incubation, cells that migrated or invaded through the membrane were fixed, stained with crystal violet, and quantified by counting five random fields per replicate (n\u0026thinsp;=\u0026thinsp;3 independent experiments).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePlate clone formation assay\u003c/h2\u003e \u003cp\u003eTransfected cells were seeded into 6-well plates at clonal densities ranging from 200 to 1,000 cells per well. Cells were cultured at 37\u0026deg;C with 5% CO₂ for 10\u0026ndash;14 days, with media refreshed every 72 hours. Colonies were washed with PBS, fixed in 4% paraformaldehyde for 20 minutes at room temperature, and stained with 0.5% crystal violet solution (prepared in 20% methanol) for 30 minutes. After rinsing and air drying, colonies containing\u0026thinsp;\u0026ge;\u0026thinsp;50 cells were manually counted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eData were analyzed using two-tailed Student\u0026rsquo;s t-tests, one-way ANOVA with Bonferroni post hoc correction, or Mann-Whitney U tests, as appropriate, using GraphPad Prism 7. Statistical significance was defined as *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePan-cancer analyses of FBXO2 expression and prognosis\u003c/h2\u003e \u003cp\u003eIn this study, a comprehensive pan-cancer analysis using TCGA data revealed significant tissue-specific variation in FBXO2 mRNA expression across malignant tumors. FBXO2 was markedly upregulated (fold change\u0026thinsp;\u0026gt;\u0026thinsp;2.0, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in ten cancer types, including bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), lung adenocarcinoma (LUAD), rectal adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Conversely, downregulation trends were observed in breast carcinoma (BRCA), glioblastoma multiforme (GBM), and prostate adenocarcinoma (PRAD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Paired-sample analyses within TCGA further confirmed significantly higher FBXO2 expression in tumor tissues compared to adjacent normal tissues in COAD, ESCA, HNSC, and THCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), highlighting its potential functional relevance in these cancers. Analysis through the UALCAN database demonstrated a positive correlation between FBXO2 expression and clinical stage progression in multiple malignancies. Specifically, cancers with elevated FBXO2 expression, such as ESCA, LIHC, and THCA, exhibited increasing mRNA levels with advancing tumor stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Significant associations between FBXO2 expression and advanced clinical stages were also noted in UCEC, READ, BLCA, and cervical squamous cell carcinoma (CESC) (Supplementary Fig.\u0026nbsp;1). Multidimensional survival analyses based on TCGA data (Supplementary Figures S2A-D) and multivariate Cox proportional hazards models, adjusting for age, gender, and pathological stage, revealed that high FBXO2 expression correlated with poorer DFI in cholangiocarcinoma (CHOL), COAD, and stomach adenocarcinoma (STAD), but with improved survival in sarcoma (SARC). DSS was reduced in CHOL, HNSC, and lung squamous cell carcinoma (LUSC), while mesothelioma (MESO) patients with high FBXO2 expression exhibited better survival outcomes. OS and PFI were significantly decreased in ESCA and HNSC but improved in MESO (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-G, and Supplementary Figures S2E-H). Collectively, these findings suggest that FBXO2 may act as a tumor promoter in CHOL, COAD, ESCA, and HNSC, yet function as a tumor suppressor in MESO, underscoring its context-dependent gene function across cancer types. This duality likely reflects complex interactions involving distinct signaling pathways, regulatory networks modulated by FBXO2, and tumor heterogeneity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have reported significantly elevated \u003cem\u003eFBXO2\u003c/em\u003e mRNA levels in various cancers compared to normal tissues. To validate these findings at the protein level, we performed immunofluorescence staining on clinical samples from COAD, ESCA, LIHC, and THCA, comparing FBXO2 protein expression between tumor and adjacent normal tissues. The results showed significantly higher FBXO2 protein levels in tumor tissues across all four cancer types (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D), corroborating the mRNA expression data and underscoring the potential importance of FBXO2 in the pathogenesis of COAD, ESCA, LIHC, and THCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFBXO2 is associated with genomic alterations and instability\u003c/h2\u003e \u003cp\u003eTo investigate the genomic features of FBXO2 in tumorigenesis, we conducted a comprehensive analysis of genomic variations. Data from cBioPortal revealed significant copy number amplification of FBXO2 in ESCA and pronounced deletions in CHOL. Notably, the frequency of FBXO2 single nucleotide variants (SNVs) was generally low (\u0026lt;\u0026thinsp;1%) across most cancer types (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Further evaluation using the TIDE platform indicated that FBXO2 copy number amplification was significantly associated with poorer prognosis in carcinosarcoma (CARC) and lower grade glioma (LGG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Genomic instability analyses showed a positive correlation between FBXO2 expression and TMB in acute myeloid leukemia (LAML), while negative correlations were observed in BLCA and three other cancers. Regarding MSI, FBXO2 expression correlated positively with testicular germ cell tumors (TGCT) but negatively with COAD and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Homologous recombination deficiency (HRD) analysis revealed significant positive correlations between FBXO2 and UCEC, among other cancers, whereas negative correlations were found in uveal melanoma (UVM) and kidney chromophobe carcinoma (KICH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Aneuploidy scores demonstrated positive associations with FBXO2 expression in thymoma (THYM) and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Considering the predictive role of tumor-specific neoantigen load in immunotherapy response (Jumper et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we examined the relationship between FBXO2 and neoantigen burden based on somatic mutation profiles. By identifying potential MHC-binding neoantigens derived from SNVs and indels (N. Zhang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we found that FBXO2 expression was significantly negatively correlated with neoantigen load in UCEC, adrenocortical carcinoma (ACC), and HNSC (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-H). Collectively, these findings suggest that FBXO2 may contribute to tumor immune evasion through modulation of genomic stability and neoantigen generation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFBXO2 Is associated with DNA repair and methylation\u003c/h2\u003e \u003cp\u003eThe maintenance of genomic stability in tumors depends on complex DNA damage repair mechanisms, including MMR and homologous recombination repair (HRR), which also influence tumor stemness (Gorodetska, Kozeretska, \u0026amp; Dubrovska, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our comprehensive analysis of TCGA pan-cancer data revealed that FBXO2 expression is significantly positively correlated with key MMR genes, such as EPCAM, MLH1, and MSH2, in HNSC, kidney renal papillary cell carcinoma (KIRP), and GBM, with the strongest correlation observed in LGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, FBXO2 expression showed a strong positive correlation with tumor stemness indices in LAML but a negative correlation in kidney renal clear cell carcinoma (KIRC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To investigate the role of FBXO2 methylation in tumor progression, we conducted systematic epigenetic analyses. Paired-sample comparisons across multiple cancers demonstrated significantly elevated FBXO2 promoter methylation in tumor tissues relative to adjacent normal tissues, notably in KIRC, breast invasive carcinoma (BRCA), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Survival analyses indicated that high FBXO2 promoter methylation was significantly associated with improved OS in patients with LGG, pancreatic adenocarcinoma (PAAD), ovarian cancer (OV), and sarcoma (SARC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Notably, FBXO2 expression exhibited broad positive correlations with RNA modification enzymes involved in m^1A, m^5C, and m^6A modifications across diverse cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Integrated DNA methylation pseudotime (DMPsi) analysis revealed significant negative correlations between FBXO2 expression and DNA methyltransferase activity in THYM, skin cutaneous melanoma (SKCM), MESO, and LGG, whereas positive correlations were observed in COAD, TGCT, STAD, and OV (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These multi-omics findings highlight the multifaceted role of \u003cem\u003eFBXO2\u003c/em\u003e in tumorigenesis, linking its expression to DNA repair, epigenetic regulation, and tumor stemness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAlternative FBXO2 splicing predicted survival outcomes\u003c/h2\u003e \u003cp\u003eAS is a pivotal post-transcriptional regulatory mechanism that generates diverse mRNA isoforms, thereby modulating protein function and non-coding RNA expression, with a well-established role in tumorigenesis (Pradella, Naro, Sette, \u0026amp; Ghigna, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our analysis focused on FBXO2 AS events identified an aberrantly regulated intron retention event, Intron_Retention_8113, across multiple malignancies. Notably, the percent spliced in (PSI) value of this event was significantly elevated in tumor tissues compared to adjacent normal tissues in LIHC and READ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Comparative analyses further revealed pronounced PSI differences in PAAD and GBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Clinical correlation analyses demonstrated significant associations between FBXO2_Intron_Retention_8113 and patient prognosis in MESO, HNSC, and BRCA (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Survival analyses indicated that high PSI values of this splicing event correlated with prolonged OS in KICH but with reduced OS in LUSC (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). These findings underscore the importance of FBXO2 AS in tumorigenesis and suggest that distinct transcript isoforms may contribute to tumor heterogeneity. The contrasting prognostic implications across cancer types likely reflect tissue-specific regulatory mechanisms, highlighting the potential for splicing-based precision therapies tailored to cancer subtype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eFBXO2 is involved in metabolism and immune pathways\u003c/h2\u003e \u003cp\u003eTo elucidate the functional roles and interaction networks of FBXO2 in cancer, we first identified FBXO2-interacting proteins using the STRING database. Following stringent filtering, 20 core interactors were identified, including E3 ubiquitin ligases such as STUB1 and BTRC, the tumor suppressor FBXW7, and the cullin-associated and neddylation-dissociated protein CAND1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). KEGG and GO enrichment analyses were conducted based on these interactors. KEGG pathway analysis highlighted FBXO2\u0026rsquo;s involvement in five key pathways: ubiquitin-mediated proteolysis, protein processing in the endoplasmic reticulum, Shigellosis infection, circadian rhythm, and Hedgehog signaling (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). GO analysis further revealed significant enrichment in processes related to protein ubiquitination and proteasome-dependent degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Importantly, Gene Set Enrichment Analysis (GSEA) uncovered strong associations between FBXO2 and metabolic pathways, including oxidative phosphorylation and bile acid metabolism (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F), suggesting that FBXO2 may modulate the tumor microenvironment through regulation of metabolic reprogramming. Collectively, these systematic bioinformatics analyses offer valuable insights into the molecular mechanisms by which FBXO2 contributes to tumorigenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eFBXO2 correlated with immune infiltration and cytokine-mediated immune modulations\u003c/h2\u003e \u003cp\u003eTo further investigate FBXO2\u0026rsquo;s role in regulating the tumor immune microenvironment, we conducted a pan-cancer analysis of ESTIMATE scores. FBXO2 expression demonstrated cancer type-specific correlations with ESTIMATE scores, showing positive associations in BLCA, CESC, LAML, LUSC, PRAD, THCA, and UVM, but negative correlations in KICH, KIRC, KIRP, OV, PCPG, STAD, and TGCT. Similar tissue-specific patterns emerged in stromal cell infiltration analyses: FBXO2 was positively correlated with stromal scores in BLCA and LAML, while negative correlations were observed in KICH and KIRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Further analyses revealed significant associations between FBXO2 expression and regulation of the chemokine network. In KICH and THYM, FBXO2 showed significant negative correlations with inflammation-related chemokines (CXCL2, CXCL5) and chemokine receptors (CCR1, CCR2) (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C). Notably, FBXO2\u0026rsquo;s relationship with MHC molecule expression exhibited cancer-specific patterns: positive correlations with MHC class I molecules (HLA-A, HLA-B) were detected in THCA, UVM, and PRAD, whereas negative correlations were found in OV, TGCT, and LGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Immune regulatory network analysis further showed that in cancers with negative FBXO2-ESTIMATE correlations (e.g., KICH, OV, STAD), FBXO2 expression negatively correlated with immune checkpoint molecules (PD-L1, CTLA-4) and diverse immune activation/suppression markers. Conversely, positive correlations were observed in LAML and UVM (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-G). Analysis of immune and molecular subtypes via the TISDB database revealed significant associations between FBXO2 expression and immune subtypes in UCEC and sarcoma (SARC), as well as molecular subtypes in STAD and UCEC (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-I). Collectively, these multi-dimensional findings highlight the pivotal role of FBXO2 in tumor immune evasion and suggest promising targets for novel immune microenvironment-based therapeutic strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eFBXO2 is a potential biomarker of infiltration by NK cell\u003c/h2\u003e \u003cp\u003ePrevious studies have established significant correlations between FBXO2 expression and various malignancies (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To validate these findings at higher resolution, we analyzed single-cell transcriptomic data from the Tumor Immune Single-cell Hub (TISCH) database. FBXO2 exhibited significantly elevated expression across single-cell profiles in multiple cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), further supporting its involvement in tumorigenesis at the single-cell level. To elucidate the role of FBXO2 in the tumor immune microenvironment, we examined its correlations with 22 immune cell infiltrates using CIBERSORT. FBXO2 expression was significantly negatively correlated with resting CD4\u003csup\u003e+\u003c/sup\u003e memory T cells and M2 macrophages, while showing a positive correlation with natural killer (NK) cell infiltration in 13 cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Single-cell subpopulation analysis confirmed that this positive association primarily involved activated NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Notably, MESO exhibited the strongest positive correlation between FBXO2 expression and NK cell infiltration, whereas negative correlations were observed in LIHC and COAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). This tumor type-specific pattern suggests differential functional roles of FBXO2 across cancers. Multiplex immunofluorescence staining further revealed co-localization of FBXO2 with the NK cell marker CD56 in both tumor and adjacent normal tissues of MESO (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Similar co-expression patterns were observed in KICH and ESCA (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE-F). Collectively, these multi-omics data spanning genomic, transcriptomic, and protein levels consistently demonstrate that FBXO2 may serve as a novel molecular marker of NK cell infiltration in specific cancers. This discovery highlights the complex and multifaceted functions of FBXO2 in tumor biology and offers new insights into the regulation of the tumor immune microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFBXO2 plays an important role in colorectal cancer cell proliferation, metastasis and tumorigenesis\u003c/h2\u003e \u003cp\u003eBuilding on preliminary analyses that highlighted the significance of FBXO2 in malignancies such as colorectal adenocarcinoma (COAD), LIHC, and THCA, yet lacking sufficient experimental validation, this study specifically focused on elucidating the functional role of FBXO2 in colorectal cancer progression. Before conducting functional experiments, we validated prior observations derived from the TCGA dataset by analyzing an independent external cohort, GSE39582. Consistent with TCGA findings, FBXO2 expression was significantly elevated in tumor tissues compared to normal tissues within the GSE39582 dataset (Supplementary Figure S3A, p\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 1e-4). Furthermore, Kaplan-Meier survival analysis revealed that patients with high FBXO2 expression experienced significantly shorter RFS than those with low expression (Supplementary Figure S3B, Log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.045). Multivariate Cox proportional hazards regression analysis confirmed that FBXO2 expression remained an independent prognostic factor for RFS (Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;1.57; 95% Confidence Interval [CI], 1.07\u0026ndash;2.30; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Figure S3C). Together, these results reinforce FBXO2\u0026rsquo;s potential as a robust prognostic biomarker and justify further experimental investigation into its mechanistic role in colorectal cancer development.\u003c/p\u003e \u003cp\u003eTo investigate the functional role of FBXO2, this study successfully established stable FBXO2-knockdown (shFBXO2) and FBXO2-overexpression (oeFBXO2) cell models in the colorectal cancer cell lines Caco2 and HCT116 (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B, and Supplementary Figures S4A-B). The CCK-8 cell proliferation assay and colony formation assay demonstrated that knockdown of FBXO2 significantly inhibited cell proliferation and reduced colony-forming ability, whereas overexpression of FBXO2 markedly enhanced both cell proliferation and colony formation (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-D, and Supplementary Figures S4C-D). Regarding metastatic phenotypes, the wound healing assay showed that FBXO2 overexpression significantly accelerated wound closure at 48 hours, while FBXO2 knockdown delayed the healing process (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE, and Supplementary Figure S4E). Transwell migration and invasion assays further confirmed this trend: FBXO2 overexpression increased the number of cells penetrating the membrane, whereas FBXO2 knockdown significantly suppressed cell migration and invasion (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF-G, Supplementary Figures S4F-G). Together, these results indicate that FBXO2 is a key regulator driving metastasis in colorectal cancer cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the function of FBXO2 in vivo, a subcutaneous xenograft tumor model was established in nude mice using HCT116 cells with different FBXO2 expression levels. The results showed that tumors formed by FBXO2-overexpressing cells exhibited significantly larger volumes and greater weights compared to the control group. In contrast, tumors derived from FBXO2-knockdown cells were smaller and lighter (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH-J). These in vivo findings further confirm the tumor-promoting role of FBXO2 in colorectal carcinogenesis.\u003c/p\u003e \u003cp\u003eIn summary, this study comprehensively demonstrates through in vitro and in vivo experiments that FBXO2 plays a crucial role in colorectal cancer progression by promoting cell proliferation, enhancing migratory and invasive capabilities, and increasing tumorigenicity in vivo.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecent studies have indicated that FBXO2 influences tumor cell processes; however, existing evidence remains limited and sometimes contradictory (13\u0026ndash;19). To clarify the relationship between FBXO2 and various cancers, this study conducted a comprehensive multi-omics analysis across 33 cancer types from TCGA. We systematically examined FBXO2 expression, mutation status, epigenetic modifications, and its association with immune infiltration. Our findings reveal that FBXO2 expression is significantly associated with patient prognosis, tumor stage, and the tumor microenvironment in multiple malignancies. Notably, FBXO2 was found to be highly expressed in COAD, LIHC, (THCA), and ESCA. Experimental validation confirmed that FBXO2 protein levels were significantly elevated in tumor tissues compared to adjacent normal tissues across these four cancer types, underscoring its potential regulatory role in tumor development.\u003c/p\u003e \u003cp\u003eTumorigenesis and progression are often accompanied by genomic alterations (Lopez-Otin, Pietrocola, Roiz-Valle, Galluzzi, \u0026amp; Kroemer, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this context, FBXO2 frequently exhibits copy number amplification in certain cancers, such as ESCA, with higher FBXO2 CNV levels correlating with poorer prognosis in malignancies including CARC and LGG. Analyses of TMB and MSI further demonstrated significant positive correlations between FBXO2 expression and several cancer types. Additionally, HRD and aneuploidy scores showed strong positive associations with FBXO2 expression in UCEC and other cancers. Collectively, these findings suggest that FBXO2 may contribute to tumor immune evasion by modulating genomic stability and neoantigen production.\u003c/p\u003e \u003cp\u003eThe maintenance of genomic stability in malignant tumors depends on complex, multi-layered DNA damage repair systems (Bradley \u0026amp; Anczukow, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, we found that FBXO2 potentially contributes to genomic stability across various cancers by modulating key DNA repair pathways, including MMR and HRR. Specifically, FBXO2 expression demonstrated significant positive correlations with critical MMR genes, such as EPCAM, MLH1, and MSH2, in several malignancies, including HNSC, KIRP, and GBM. Concurrently, epigenetic regulation of FBXO2 via promoter methylation appears to be closely associated with cancer development (Davalos \u0026amp; Esteller, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Significant differences in FBXO2 promoter methylation levels were observed among normal tissues, primary tumors, and metastatic lesions. For example, FBXO2 promoter methylation was markedly elevated in tumor tissues relative to adjacent normal tissues in diverse cancers, including GBM and BRCA. These findings suggest that FBXO2-related genomic instability may play a regulatory role in cancer initiation and progression, offering novel insights into its pivotal function in tumorigenesis.\u003c/p\u003e \u003cp\u003eFunctional enrichment analyses revealed that FBXO2 is involved in various metabolic pathways and disease processes, highlighting its potential to influence the tumor microenvironment through metabolism-immune crosstalk (Karami Fath et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; S. Zhang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Building on this, we performed in-depth investigations into FBXO2\u0026rsquo;s role in tumor immunomodulation. Our study systematically uncovers, for the first time, that FBXO2 expression correlates positively with both immune-activating and immune-suppressive molecules across multiple cancer types. Moreover, GSEA identified significant associations between FBXO2 and metabolic pathways such as oxidative phosphorylation and bile acid metabolism, suggesting that FBXO2 may modulate the tumor microenvironment by regulating metabolic reprogramming.\u003c/p\u003e \u003cp\u003eThe prognosis of cancer patients is often influenced by tumor immune evasion and the tumor microenvironment(Lawal et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To gain deeper insight into the role of FBXO2 within the tumor immune microenvironment, this study comprehensively assessed its correlation with the infiltration of 22 immune cell types. FBXO2 exhibited a significant positive correlation with NK cell infiltration in 13 cancer types, predominantly associated with activated NK cell subsets. NK cells are critical effectors in anti-tumor immunity, capable not only of directly recognizing and lysing tumor cells through cytolytic granule release but also modulating immune responses via secretion of specific chemokines(Shimasaki, Jain, \u0026amp; Campana, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To further evaluate whether FBXO2 expression is closely associated with NK cell infiltration in specific cancers, co-expression of FBXO2 and the NK cell marker CD56 was examined in selected malignancies using fluorescence staining. The experimental results confirmed co-localization of FBXO2 and CD56 in multiple cancer types. Given that NK cell infiltration generally correlates with tumor suppression, these findings suggest that, in contrast to its tumor-promoting role in cancers such as COAD and LIHC, FBXO2 may exert tumor-suppressive functions in malignancies like MESO. This observation aligns with the prognostic analyses (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-G, and Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-H) and highlights the tumor-type-specific roles of FBXO2. However, since the study included a limited range of cancers, whether FBXO2 co-localizes with CD56 in other cancer types remains to be determined. Moreover, defining FBXO2 as a novel biomarker for NK cell infiltration requires further functional validation.\u003c/p\u003e \u003cp\u003eGiven the overexpression of FBXO2 and its association with poor prognosis in COAD, LIHC, and THCA, coupled with its negative correlation with NK cell infiltration in COAD and LIHC, this study focused on elucidating FBXO2\u0026rsquo;s functional role in these cancers, particularly COAD. Experimental evidence confirmed that FBXO2 plays a critical role in colorectal cancer cell proliferation, migration and tumorigenesis, consistent with previous studies indicating that FBXO2 may regulate tumor growth via ubiquitin-proteasome system (UPS)-mediated degradation of N-cadherin (18). Since aberrant N-cadherin expression promotes colon cancer progression through β-catenin dysregulation(Zhao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), these findings collectively suggest that FBXO2 upregulation is closely associated with colorectal cancer proliferation and metastasis. FBXO2 thus emerges as a potential biomarker for colorectal cancer metastasis, although further functional experiments are necessary to confirm this role. Given the similar association between FBXO2 overexpression and poor prognosis in hepatocellular carcinoma and thyroid cancer, it is plausible that FBXO2 performs comparable functions in these malignancies, warranting experimental verification.\u003c/p\u003e \u003cp\u003eThis study represents the first systematic multi-omics investigation of FBXO2 across 33 TCGA cancer types, thoroughly characterizing its expression patterns, mutational landscape, epigenetic modifications, and immune infiltration relationships. Through pan-cancer analysis, we establish FBXO2 as a potential prognostic biomarker and therapeutic target. Our findings elucidate FBXO2\u0026rsquo;s dual role in maintaining tumor genomic stability and modulating the tumor immune microenvironment, while also demonstrating its contribution to tumor proliferation, migration and tumorigenesis in colorectal cancer. These insights provide a foundation for future mechanistic studies and the development of novel therapeutic strategies aimed at improving clinical outcomes for cancer patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis pan-cancer study delineates the multifaceted role of FBXO2 in tumorigenesis. FBXO2 exhibits significant, context-dependent dysregulation across cancers, correlating with clinical stage and patient prognosis. It contributes to genomic stability through associations with DNA repair pathways, copy number alterations, and epigenetic regulation. FBXO2 is also implicated in shaping the tumor immune microenvironment, showing notable correlations with immune cell infiltration (particularly activated NK cells) and immune-modulatory molecules. Functional validation in colorectal cancer confirms that FBXO2 promotes tumor cell proliferation, migration, invasion, and in vivo tumorigenesis. Collectively, these findings position FBXO2 as a promising prognostic biomarker and therapeutic target across multiple cancers, highlighting its dual functions in genomic integrity and immune modulation.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003eThe human tumor tissue sections used in this study were obtained from the First Hospital of Qinhuangdao. This study was reviewed and approved by the Institutional Review Boards (IRB) of the First Hospital of Qinhuangdao (Approval No.: 2025K-093-01) and Nankai University (Approval No.: NKUIRB2025134). Written informed consent was waived by the ethics committees due to the retrospective nature of the study using anonymized archival specimens. This study strictly adheres to the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eAll mice were bred and kept in the Animal Center of Nankai University under specific pathogen-free (SPF) conditions. Every procedure was conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Model Animal Research Center. All mouse experiments were approved by the Animal Ethics Committee of Nankai University. The maximal permitted tumor burden approved by the Animal Ethics Committee of Nankai University was a tumor volume of 2000 mm\u0026sup3;.This limit was not exceeded in any of the animals used in this study. Throughout the experiments, variables such as environmental influences, parental genotypes, and husbandry conditions were strictly controlled.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \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 \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by NSFC grants, 82271779, 81901677, 81802372; Tianjin science and technology commission (23JCYBJC01760); Hebei Natural Science Foundation (Grant No. C2025107013).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWM: Conceptualization, Investigation, Writing\u0026ndash;original draft, Software, Supervision, Data curation, Project administration, Methodology. ZH: Visualization, Writing\u0026ndash;original draft, Conceptualization, Methodology, Supervision, Project administration, Investigation. GQ: Formal analysis, Methodology, Software, Validation, Writing\u0026ndash;review \u0026amp; editing. ZY: Formal analysis, Methodology, Software, Validation, Writing\u0026ndash;review \u0026amp; editing. WJ: Funding acquisition, Investigation, Project administration, Supervision, Writing\u0026ndash;review \u0026amp; editing. GY: Funding acquisition, Conceptualization, Project administration, Writing\u0026ndash;review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was implemented based on the platform of Nankai University and the First Hospital of Qinhuangdao.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used in this study are publicly available from TCGA, CPTAC, UALCAN, cBioPortal (v6.0.24), ImmPort, TIDE, and OncoSplicing. The following is a detailed description of the data sources utilized in this study.TCGA Transcriptomic \u0026amp; Clinical Data: Gene expression quantification data (workflow: STAR - Counts) for 33 cancer types were retrieved from the GDC Data Portal (https://portal.gdc.cancer.gov/) via the TCGAbiolinks R package. Clinical phenotype and survival data were obtained from the UCSC Xena platform (https://xenabrowser.net/datapages/), specifically utilizing the \u0026lsquo;Phenotype\u0026rsquo; (PANCAN\\_clinicalMatrix) and \u0026lsquo;Survival Data\u0026rsquo; (Survival\\_Supplemental Table\\_S1\\_20171025\\_xena\\_sp) datasets from the TCGA Pan-Cancer (PANCAN) cohort. Proteomic Data: Proteomic expression analysis was conducted using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, which was accessed and analyzed via the UALCAN web portal (specifically the \u0026lsquo;CPTAC Analysis\u0026rsquo; module, available at [https://ualcan.path.uab.edu/analysis-prot.html).](https:/ualcan.path.uab.edu/analysis-prot.html).) cBioPortal (v6.0.24): The genetic alteration frequency (mutations, amplifications, deep deletions) and mutation landscape of FBXO2 were analyzed using the \u0026lsquo;TCGA PanCancer Atlas Studies\u0026rsquo; dataset (https://www.cbioportal.org/). TIDE: The prognostic value of FBXO2 copy number variations (CNVs) was analyzed using the \u0026lsquo;Copy Number\u0026rsquo; module of the Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/). Immune Cell Proportions: Pre-calculated CIBERSORT immune cell fractions were retrieved from the GDC \u0026lsquo;The Immune Landscape of Cancer\u0026rsquo; publication page (https://gdc.cancer.gov/about-data/publications/panimmune), specifically using the file TCGA.Kallisto.fullIDs.cibersort.relative.tsv (Thorsson et al., Immunity 2018) OncoSplicing: Alternative splicing events were analyzed using the OncoSplicing Pan-Cancer module (http://www.oncosplicing.com/indexPanCancer), incorporating data from the SpliceSeq and SplAdder projects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtkin G, Moore S, Lu Y, Nelson RF, Tipper N, Rajpal G, Paulson H. 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Mol Cancer. 2022;21(1):144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-022-01616-7\u003c/span\u003e\u003cspan address=\"10.1186/s12943-022-01616-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"FBXO2, Colorectal cancer, Cancer progression, Multi-omics analysis, Genomic instability, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-8578038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8578038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo comprehensively characterize the immunological functions and prognostic significance of FBXO2 across human cancers, and elucidate the functional role of FBXO2 in colorectal cancer progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study represents the first systematic multi-omics investigation of FBXO2 across 33 TCGA cancer types, thoroughly characterizing its expression patterns, mutational landscape, epigenetic modifications, and immune infiltration relationships. To investigate the functional role of FBXO2 in COAD, this study successfully established stable FBXO2-knockdown (shFBXO2) and FBXO2-overexpression (oeFBXO2) cell models in the colorectal cancer cell lines Caco2 and HCT116. CCK-8 cell proliferation assay and colony formation assay were conducted to evaluate the role of FBXO2 in the proliferation of colorectal cancer cells. While, regarding metastatic phenotypes, transwell migration and invasion assays were carried out.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThrough an integrated multi-omics analysis, we identified pronounced tumor-type-specific expression patterns of FBXO2 and demonstrated its strong diagnostic and prognostic potential in multiple malignancies. Functional assays further revealed that FBXO2 knockdown markedly inhibited the proliferation, migration, and invasion of colorectal cancer cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe established FBXO2 as a potential prognostic biomarker and therapeutic target and showed that FBXO2 expression may serve as a meaningful indicator of tumor initiation and progression in colorectal cancer.\u003c/p\u003e","manuscriptTitle":"Comprehensive pan-cancer analysis reveals that FBXO2 as a potential therapeutic target associated with immune infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 14:07:25","doi":"10.21203/rs.3.rs-8578038/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9647e112-a8e8-42be-9d2f-ea9df06722d6","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T15:10:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 14:07:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8578038","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8578038","identity":"rs-8578038","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}