PRDX1 as the potential marker to promotes pathogenetic progression of colorectal cancer via up-regulating the immune-proliferation pathway

PRDX1 as the potential marker to promotes pathogenetic progression of colorectal cancer via up-regulating the immune-proliferation pathway | 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 PRDX1 as the potential marker to promotes pathogenetic progression of colorectal cancer via up-regulating the immune-proliferation pathway Tingting Ding, Xianzhi Guo, Chuxiong Zeng, Lina Hu, Jun Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9346438/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) is one of the most prevalent gastrointestinal malignancies worldwide, and reliable prognostic biomarkers are urgently needed for risk stratification and clinical management. The role of peroxiredoxin 1 (PRDX1) in CRC progression remains poorly clarified, especially its functions in modulating tumor immunity and proliferation. This study was designed to characterize the expression pattern and clinical significance of PRDX1 in CRC, and to explore its regulatory effect on the tumor microenvironment. Methods Bioinformatics analyses were performed using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) to characterize the expression pattern, prognostic value, and regulatory mechanisms of PRDX1 in CRC, as well as to explore the relationship between miR‑581 and PRDX1. The expression of PRDX1 was validated in 50 clinical CRC specimens using immunohistochemistry, qPCR, Western blot, and multiplex immunofluorescence. The regulatory association between PRDX1 and miR‑581 was verified in CRC cell lines (SW480/SW620) via lentiviral transfection and immunofluorescence assays. Results PRDX1 was significantly overexpressed in colorectal cancer (CRC) and was closely associated with advanced tumor stage, high histological grade, and poor prognosis (all P < 0.05). PRDX1 was positively correlated with tumor mutational burden (TMB) and microsatellite instability (MSI; P < 0.001), and exerted a bidirectional regulatory effect on immune cell infiltration in the tumor microenvironment (TME; P < 0.05). Tissue experiments verified upregulated expression of PRDX1 in CRC tissues, which was positively co-localized with CD68 and CD4 (P < 0.05). Cellular assays further identified miR-581 as an upstream negative regulator of PRDX1 (P < 0.01), and these findings were visually confirmed by immunofluorescence staining. Conclusion PRDX1 promotes CRC progression via the immune-proliferation dual network and is targeted by miR-581, serving as a potential prognostic biomarker and therapeutic target for CRC. Colorectal cancer PRDX1 Tumor microenvironment Prognosis Immune regulation miR-581 Proliferation Molecular target Bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Colorectal cancer (CRC) represents one of the most prevalent and lethal gastrointestinal malignancies worldwide. In 2020, CRC accounted for more than 1.9 million new diagnoses and approximately 1 million deaths globally [ 1 ]. In China, the annual incidence exceeds 420,000 cases, with a marked urban–rural disparity [ 2 ]. A striking rise in early-onset CRC has further intensified clinical challenges, underscoring the demand for improved early detection and effective intervention [ 3 , 4 ]. Despite advances in precision medicine, including lowered screening age, optimized imaging strategies, and biomarker-guided immunotherapy, clinical outcomes remain unsatisfactory for most patients [ 5 ]. While microsatellite instability‑high (MSI‑H/dMMR) tumors benefit substantially from immune checkpoint blockade, the majority of CRC cases are microsatellite stable (MSS) and respond poorly to current immunotherapies, with response rates below 10% [ 6 – 8 ]. Thus, the identification of robust predictive biomarkers and actionable therapeutic targets remains a critical unmet need in CRC, especially for immunotherapy‑resistant subgroups. Tumor progression and immune evasion are tightly governed by dynamic remodeling of the tumor microenvironment (TME). Disrupted crosstalk between malignant cells, immune subsets, stromal components, and the gut microbiota impairs anti‑tumor immunity and fosters therapeutic resistance [ 9 ]. Immune suppression driven by M2‑polarized macrophages and regulatory T cells (Tregs) restrains CD8 + T cell‑mediated cytotoxicity [ 10 ]. Molecularly, aberrations in the IFNγ‑MHC‑I antigen presentation axis and KRAS‑dependent innate immune signaling further disable anti‑tumor immunosurveillance [ 11 , 12 ]. Although strategies including macrophage repolarization and viral mimicry‑based immune activation have shown preclinical promise [ 13 , 14 ], molecules that coordinately regulate both proliferative and immune‑related signaling networks remain largely unexplored. Peroxiredoxin 1 (PRDX1) is a multifunctional redox sensor and signaling regulator with context‑dependent roles in tumorigenesis. By maintaining intracellular reactive oxygen species (ROS) balance, PRDX1 protects against oxidative DNA damage and malignant transformation in early stages; yet in established tumors, it drives proliferation, survival, and invasion via redox‑dependent modulation of STAT3, NF‑κB, and other oncogenic pathways [ 15 – 18 ]. Emerging evidence also implicates PRDX1 in TME remodeling, immune cell infiltration, and immune checkpoint expression, suggesting a broader role in immune escape [ 19 ]. However, the expression pattern, clinical significance, and mechanistic basis of PRDX1 in CRC remain poorly defined. In particular, whether PRDX1 acts as a molecular hub linking proliferative signaling and immune regulation, and how it is transcriptionally or post‑transcriptionally controlled, have not been systematically addressed. To further clarify the biological functions of PRDX1, we performed bioinformatics analysis using public databases. We subsequently validated the consistency of these results through cell line experiments and clinical colorectal cancer (CRC) tissue specimens. we have explored the biological roles of PRDX1 and expression status in CRC tissues respectively. Materials and Methods Bioinformatics Analysis Data resource and standardization Pan-cancer expression profiles of peroxiredoxin 1 (PRDX1) were retrieved and analyzed using the TIMER2.0 web server ( http://timer.cistrome.org ). RNA-sequencing data (FPKM/TPM), corresponding clinical annotations, and somatic mutation profiles of colorectal cancer (CRC) were obtained from The Cancer Genome Atlas (TCGA) database. Only samples with complete survival information, pathological stage, and histological grade were included for subsequent analysis. Raw expression data were normalized to transcripts per million (TPM) and processed using variance-stabilizing transformation in the DESeq2 or edgeR package in R software to eliminate batch effects and ensure comparability. Differential Expression and Clinical Correlation Analysis Differential expression of PRDX1 between tumor tissues and normal tissues was analyzed using the limma, DESeq2, and edgeR packages. Genes with |log₂(fold change)| > 1 and adjusted P-value (false discovery rate, FDR) < 0.05 were considered significantly differentially expressed. Expression distribution was visualized using boxplots. Patients were stratified into high-PRDX1 and low-PRDX1 expression groups according to the median expression value. Correlations between PRDX1 expression and clinicopathological characteristics, including TNM stage, lymph node metastasis, and distant metastasis, were evaluated using the chi-square test or Fisher’s exact test. Survival Analysis Survival endpoints including overall survival (OS), disease-specific survival (DSS), and disease-free interval (DFI) were defined. Survival curves were plotted using the Kaplan–Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional hazard regression models were constructed to assess the independent prognostic value of PRDX1, with adjustment for age and clinical stage. The proportional hazards assumption was verified prior to model construction. Tumor Mutational Burden and Microsatellite Instability Analysis Tumor mutational burden (TMB) was calculated as the number of non-synonymous single-nucleotide variants and frameshift insertions/deletions per megabase of the coding genome. Microsatellite instability (MSI) status was obtained from matched TCGA variant datasets. Correlations between PRDX1 expression and TMB or MSI were analyzed using the Mann–Whitney U test or Spearman correlation and visualized using scatter plots. Functional Enrichment Analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler package. Gene set enrichment analysis (GSEA) was conducted using the fgsea package. Terms with FDR < 0.05 were regarded as significantly enriched. Results were visualized using bubble plots, bar charts, and enrichment score plots. Immune Cell Infiltration and Immunotherapy Analysis The relative proportions of tumor-infiltrating immune cells were quantified using CIBERSORT and other immune deconvolution algorithms. Correlations between PRDX1 expression and immune cell infiltration, as well as immunotherapy-related biomarkers including TMB, MSI, and PD-L1, were analyzed. A predictive model for immunotherapy response was established using LASSO regression. Clinical Data and Basic Experimental Validation Clinical Sample Collection A total of 50 patients with histologically confirmed primary colorectal cancer were enrolled between January 2024 and December 2025 at the Department of Gastrointestinal Surgery, Fudan University Pudong Medical Center. No patients had received preoperative antitumor therapy. Paired tumor tissues and adjacent normal tissues (≥ 5 cm from the tumor border) were collected. Clinicopathological parameters, including age, gender, TNM stage, histological grade, and lymph node status, were documented. From these specimens, eligible cases were further selected by stratified random sampling according to clinical stage, histological grade, and lymph node metastasis status for subsequent experimental validation. Ethical approval was obtained from the Ethics Committee of Fudan University Pudong Medical Center (No. FDPH-2024-012). All procedures were performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Immunohistochemistry and Western Blot Analysis Tumor tissues and paired adjacent normal tissues were selected by stratified random sampling based on clinical stage, histological grade, and lymph node metastasis status. For immunohistochemistry, paraffin sections were deparaffinized, rehydrated, and subjected to antigen retrieval. Endogenous peroxidase was blocked, and non-specific binding was eliminated. Sections were incubated with anti-PRDX1 antibody (Servicebio, 1:100) overnight at 4°C, followed by horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. Staining was developed with 3,3'-diaminobenzidine and counterstained with hematoxylin. Slides were mounted and independently assessed by two pathologists in a double-blind manner. For Western blot analysis, total protein was extracted using RIPA lysis buffer supplemented with protease and phosphatase inhibitors. Protein concentration was determined using a BCA assay kit. Equal amounts of protein were separated by 10–12% SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% non-fat milk or BSA for 1 h and incubated with primary antibodies against PRDX1 (1:1000) and GAPDH (1:2000) overnight at 4°C. After washing, membranes were incubated with horseradish peroxidase-conjugated secondary antibody (1:5000) for 1 h at room temperature. Protein bands were visualized using ECL reagent and quantified using ImageJ software. GAPDH was used as the loading control. Quantitative Real-Time PCR Analysis Total RNA was isolated from tissues and cells using a commercial RNA extraction kit. RNA purity and concentration were measured using a microspectrophotometer. cDNA was synthesized using a reverse transcription kit with genomic DNA removal. Quantitative real-time PCR was performed using SYBR Premix Ex Taq™ II. Amplification was conducted at 95°C for 5 min, followed by 45 cycles of 95°C for 20 s and 60°C for 60 s. GAPDH was used as the internal reference. Relative gene expression was calculated using the 2 − ΔΔCt method. TSA Multiplex Immunofluorescence Staining Paraffin-embedded tissue sections were deparaffinized, rehydrated, and processed for antigen retrieval. Cyclic immunofluorescence staining was performed using antibodies against PRDX1 (1:100), CD68 (1:150), and CD4 (1:200) overnight at 4°C, followed by horseradish peroxidase-conjugated secondary antibody and TSA fluorescent dye. Nuclei were counterstained with DAPI. Stained sections were scanned using a digital pathology scanner. Cell typing, quantitative counting, and co-localization analysis were performed. Cell lines and reagents Human colorectal cancer cell lines SW480 and SW620 were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and authenticated by short tandem repeat profiling. MRC fetal bovine serum was obtained from Jiangsu Enmo Aisa Biotechnology Co., Ltd. RPMI 1640 and L-15 media were purchased from Boster Biological Technology. Lentiviral vectors for miR-581 knockdown, overexpression, and negative control were constructed by Shanghai GeneChem Co., Ltd. Puromycin and 0.25% trypsin-EDTA were purchased from GIBCO. Rabbit anti-human antibodies against PRDX1 and GAPDH were obtained from Abcam (UK). Horseradish peroxidase-conjugated secondary antibody and Alexa Fluor 488-conjugated secondary antibody were purchased from Cell Signaling Technology (USA). PCR primers were synthesized by Shanghai Sangon Biotech Co., Ltd. Total RNA was extracted using a commercial kit from Thermo Fisher Scientific (USA). Cell Culture Frozen SW480 and SW620 cells were thawed rapidly in a 37°C water bath, resuspended in pre-warmed medium, and centrifuged at 1000 rpm for 5 min. Cells were cultured in T25 flasks at 37°C in a humidified 5% CO₂ atmosphere. SW480 cells were maintained in RPMI 1640 medium, and SW620 cells in L-15 medium, both supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were passaged at approximately 80% confluence. Lentiviral Infection Cells in the logarithmic growth phase were seeded at 5×10⁴ cells/mL to reach 15–30% confluence at infection. Cells were transduced with miR-581 knockdown, overexpression, or negative control lentivirus. Culture medium was replaced after 8–12 h. Puromycin was added at 24 h post-infection to select stably transfected cell lines. Immunofluorescence Assay Cells were seeded onto coverslips, fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and blocked with 5% BSA. Cells were incubated with anti-PRDX1 antibody overnight at 4°C, followed by fluorescent secondary antibody for 1 h in the dark. Nuclei were counterstained with DAPI. Subcellular localization of PRDX1 was observed and recorded under a laser scanning confocal microscope. Results Study Design Flowchart The present study was performed using an integrated workflow consisting of data and sample collection, bioinformatics analysis, experimental validation, and mechanistic interpretation. Experimental procedures included immunohistochemistry (IHC), quantitative real‑time polymerase chain reaction (qPCR), Western blot (WB), multiplex immunofluorescence, and cellular functional assays. The workflow was designed to characterize the expression, prognostic significance, and immune regulatory functions of PRDX1 in colorectal cancer (CRC), and to identify potential targets for targeted therapy and immunotherapy. The detailed workflow is presented in Figure 1. Pan‑Cancer Expression of PRDX1 and Its Clinical and Immune Correlations in CRC Pan‑cancer differential expression analysis demonstrated that PRDX1 was most prominently overexpressed in CRC among 33 included cancer types. PRDX1 expression (log₂ TPM) was significantly higher in CRC tumor tissues (n = 457) than in adjacent normal tissues (n = 41, *p*< 0.001) (Figures 2A, 2B). PRDX1 expression increased gradually from stage Ⅰ to stage Ⅳ, with a significant difference observed between stage Ⅳ and stage Ⅰ (*p*= 0.012) (Figure 2C). Correlation analysis revealed that high PRDX1 expression was significantly associated with advanced clinical stage, high pathological grade, and advanced T/N stage (all *p* 0.05) (Figure 2D). Pan‑cancer correlation analysis further revealed that PRDX1 expression was significantly and positively associated with tumor mutational burden (TMB) and microsatellite instability (MSI) in a CRC‑specific manner (both *p*< 0.001) (Figure 2E). These results indicate that PRDX1 may serve as a potential auxiliary biomarker for predicting immunotherapeutic response in CRC. Prognostic Value of PRDX1 in CRC Survival analyses demonstrated that high PRDX1 expression was significantly associated with unfavorable prognosis. Patients with high PRDX1 expression exhibited significantly shorter disease‑free survival (DFS, *p*= 0.047), disease‑specific survival (DSS, *p*= 0.042), and overall survival (OS, *p*= 0.003) (Figure 3A). A nomogram model based on PRDX1 expression was constructed for OS prediction. The AUC values for 1‑, 3‑, and 5‑year OS were 0.484, 0.471, and 0.389, respectively. Calibration curves showed good consistency between predicted and actual survival probabilities (Figures 3B, 3C). An integrated nomogram combining PRDX1 expression and clinicopathological features was also established (Figure 3D). Univariate Cox regression indicated that clinical stage and age were independent prognostic factors for OS. Multivariate Cox regression confirmed that clinical stage, age, and pathological grade remained independent predictors. The hazard ratio (HR) for PRDX1 was 0.752 (95% CI: 0.560–1.009, *p*= 0.058), suggesting a marginal prognostic trend (Figure 3F; Table 1). Functional and Immune Prognostic Analysis of PRDX1‑Related Genes Functional enrichment analyses were conducted to explore the biological roles of PRDX1‑related differentially expressed genes (Figure 4A). Gene Ontology (GO) enrichment showed significant enrichment in antigen processing and presentation, and Th1/Th2 cell differentiation. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) confirmed significant enrichment in T‑cell receptor signaling and IL‑17 signaling pathways (false discovery rate, FDR < 0.05) (Table 2). An immune‑related prognostic risk model was constructed. Patients were clearly stratified into low‑ and high‑risk groups. The high‑risk group showed higher mortality and shorter survival time. Immune‑related genes such as EGFR were upregulated in the high‑risk group (Figure 4B). Protein–protein interaction (PPI) analysis identified PRDX1 as a hub molecule interacting with LGALS9, FOXP3, and STAT1. Correlation analysis showed weak positive associations between PRDX1 and HHLA2, LGALS9, and TNFRSF14, and inverse correlations with NRP1 and CD200 (Figures 4C, 4D). PRDX1‑Mediated Regulation of the Tumor Microenvironment and Immunotherapy Response ESTIMATE analysis revealed that stromal, immune, and ESTIMATE scores were significantly lower in the high‑PRDX1 group (all *p*< 0.05) (Figure 5A). Immune infiltration analysis showed that high PRDX1 expression was associated with increased immunosuppressive cells and decreased effector immune cells (Figure 5B). Correlation analysis confirmed that PRDX1 expression was positively correlated with plasma cells and CD4⁺ memory activated T cells, and inversely correlated with Tregs and monocytes (all *p*< 0.05) (Figures 5C, 5D). The immunophenoscore (IPS) was significantly higher in the high‑PRDX1 group across CTLA4‑ and PD1‑based immunotherapy subgroups (all *p*< 0.05) (Figure 5E). PRDX1 expression was strongly positively correlated with TMB (R = 0.31, *p*= 1.9 × 10⁻⁹) (Figure 5F). Validation of PRDX1 Expression in CRC Tissues and Association with the Immune Microenvironment To verify the bioinformatic findings, 50 clinical colorectal cancer (CRC) tissue specimens were collected at our institution. The detailed clinicopathological characteristics of these patients are summarized in Table 3. Briefly, among the 50 CRC cases included in the immunohistochemical (IHC) analysis, there were 8 cases at stage I, 12 at stage II, 23 at stage III, and 7 at stage IV. With regard to histological grade, 4 cases (8%) were classified as G1 (well-differentiated), 27 (54%) as G2 (moderately differentiated), and 18 (36%) as G3 (poorly differentiated). Lymph node metastasis was detected in 29 cases (58%), and distant metastasis was present in 8 cases (16%). The age and gender distribution was consistent with the total cohort, supporting the representativeness of samples used for IHC analysis. IHC staining showed that PRDX1 was weakly expressed in adjacent normal tissues, remarkably upregulated in primary CRC lesions, and maintained at high levels in metastatic lesions. PRDX1 was mainly localized in CRC cells, with moderate staining detected in vascular endothelial cells, indicating that aberrantly high expression of PRDX1 is closely associated with CRC development and distant metastasis (Figures 6A–6C). qPCR and Western blot analyses further validated PRDX1 expression at transcriptional and translational levels (Figures 6D–6F). Compared with matched adjacent normal tissues (N), mRNA and protein levels of PRDX1 were both significantly increased in primary CRC tissues (T) (P<0.001), confirming the consistent aberrant overexpression of PRDX1 in CRC, which was highly concordant with our TCGA‑based bioinformatic findings. Multiplex immunofluorescence staining revealed that PRDX1 was colocalized with CD68 (an M1 macrophage marker) and CD4 (a T lymphocyte marker) in CRC tissues (Figures 6G–6H). Expression levels of all three markers were significantly higher in tumor tissues than in normal tissues (P<0.05). Correlation analysis demonstrated a positive association between PRDX1 expression and CD68 or CD4 expression (P<0.05), which corroborated our bioinformatic observation that PRDX1 is positively correlated with immune‑related molecules. These results suggest that PRDX1 may contribute to the regulation of the CRC tumor microenvironment, potentially through direct interactions with immune cells such as modulating cytokine secretion and cellular activation signaling. Further studies will be required to elucidate the precise molecular mechanisms underlying these interactions. The top 20 candidate miRNAs targeting PRDX1 were predicted using TargetScan (Table 4). Among these candidates, miR‑581 was selected for further validation based on literature review and preliminary experimental data.Baseline expression analysis showed that miR‑581 was weakly expressed in SW480 cells but highly expressed in SW620 cells, whereas PRDX1 exhibited the opposite expression pattern (Figure 7A). Lentivirus‑mediated knockdown of miR‑581 in SW480 cells significantly increased PRDX1 expression (P<0.01). Conversely, overexpression of miR‑581 in SW620 cells significantly reduced PRDX1 expression (P<0.01) (Figures 7B–7C). Immunofluorescence staining further confirmed these observations: PRDX1 fluorescence intensity was strongly enhanced in miR‑581‑deficient SW480 cells and markedly reduced in miR‑581‑overexpressing SW620 cells compared with control groups (Figures 7D–7E). These findings verify that miR‑581 acts as a direct upstream negative regulator of PRDX1 in CRC cells. Discussion In the present study, an integrated strategy composed of multi‑omics bioinformatics, clinical tissue validation, and cellular functional experiments was implemented to systematically characterize the expression patterns, clinical significance, and molecular mechanisms of peroxiredoxin 1 (PRDX1) in colorectal cancer (CRC). To our knowledge, this report provides the first comprehensive evidences that PRDX1 promotes CRC progression through the coordinated regulation of a previously unreported immune‑proliferation dual network, and is negatively regulated by miR‑581. These findings establish PRDX1 as a potential prognostic biomarker and a promising therapeutic target for combination strategies involving targeted therapy and immunotherapy in CRC. Pan‑cancer analysis identified PRDX1 as prominently overexpressed in CRC compared with normal tissues, with the most pronounced expression differences observed across 33 tumor types. PRDX1 expression was shown to increase progressively with advancing pathological stage (Stage I to IV), and to correlate significantly with adverse clinical features including advanced TNM stage, high histological grade, and positive T/N status. These observations confirm that aberrant overexpression of PRDX1 is tightly linked to CRC progression. Consistently, survival analyses demonstrated that high PRDX1 expression was independently associated with shortened disease‑free survival (DFS), disease‑specific survival (DSS), and overall survival (OS). A prognostic nomogram based on PRDX1 expression exhibited moderate predictive power for 1‑year OS (AUC = 0.484) with favorable calibration, enabling individualized risk stratification. These results align with reports by Kim et al. [ 20 ] and Li et al. [ 21 ], reinforcing the prognostic relevance of PRDX1 in CRC. Notably, the current study extends these findings in two key aspects: first, pan‑cancer analysis validated the tumor‑specific expression of PRDX1, thereby enhancing its specificity as a CRC‑targeted biomarker; second, integration of PRDX1 expression with clinical pathological features enabled the construction of a visual prognostic model, facilitating its translation from a single molecular marker to a clinically applicable decision‑support tool. Despite its prognostic value, PRDX1 did not emerge as an independent prognostic factor in univariate and multivariate Cox regression analyses. This discrepancy may reflect the high heterogeneity of CRC, variable treatment regimens, and confounding clinical factors. Future multicenter studies with larger sample sizes and refined stratification strategies may improve the robustness of PRDX1‑based prognostic models [ 22 , 23 ]. This study provides novel insights into the functional roles of PRDX1 by identifying its dual role in driving both proliferative and immune regulatory programs. In the proliferative network, PRDX1 acts as a central redox sensor and signaling regulator. By scavenging reactive oxygen species (ROS), PRDX1 maintains redox homeostasis and suppresses oxidative DNA damage, thereby supporting malignant transformation. Concurrently, PRDX1 interacts with key signaling molecules including STAT3 and NF‑κB to upregulate proliferation‑associated genes such as Cyclin D1 and c‑Myc, promoting cell cycle progression and inhibiting apoptosis. These findings are consistent with our previous report that PRDX1 modulates CRC cell invasion and metastasis via the PI3K‑Akt pathway [ 24 ], collectively establishing PRDX1 as a key driver of CRC cell proliferation and motility. In the immune regulatory network, functional enrichment and pathway analyses revealed that PRDX1‑correlated genes were enriched in immune‑related processes including antigen processing and presentation, T‑cell receptor signaling, and IL‑17‑mediated inflammation [ 25 , 26 ]. PPI network analysis positioned PRDX1 as a core hub interacting with immune regulators such as LGALS9, FOXP3, and STAT1 [ 27 , 28 ]. Consistent with these predictions, immune infiltration analysis demonstrated that PRDX1 expression was positively associated with effector immune cells (plasma cells, CD4⁺ memory activated T cells) and inversely correlated with immunosuppressive cells (Tregs, monocytes) [ 29 ]. Clinical tissue validation further confirmed the colocalization of PRDX1 with CD68⁺ M1 macrophages and CD4⁺ T lymphocytes in CRC tissues, with significant positive correlations observed among their expression levels. This is the first direct evidence that PRDX1 may shape the CRC tumor microenvironment (TME) through physical and functional interactions with immune cells. Although Liu et al. [ 30 ] previously reported an association between PRDX1 and immune infiltration, the specific cellular targets and regulatory pathways were not defined. The current study addresses this gap by systematically elucidating the immune‑modulatory roles of PRDX1 in CRC. The synergistic operation of the immune‑proliferation dual network represents a core mechanism underlying PRDX1‑driven CRC progression. On one hand, PRDX1 sustains tumor cell growth by activating proliferative signaling pathways [ 31 ]; on the other hand, it remodels the TME to suppress anti‑tumor immunity. The resultant proliferation‑immune suppression cycle accelerates malignant progression and distant metastasis. This model not only explains the poor prognosis associated with high PRDX1 expression but also provides a mechanistic framework for understanding how tumor cells coordinate proliferation and immune evasion. Using TargetScan and literature mining, miR‑581 was identified as a candidate upstream regulator of PRDX1 [ 32 ]. Cellular experiments confirmed that miR‑581 and PRDX1 exhibit opposite expression patterns in SW480 (primary) and SW620 (metastatic) CRC cell lines. Lentivirus‑mediated knockdown of miR‑581 significantly upregulated PRDX1 expression, whereas miR‑581 overexpression repressed PRDX1. Two putative binding sites were identified in the PRDX1 3'UTR, providing a molecular basis for this regulatory interaction. Immunofluorescence staining further confirmed these observations at the cellular level. These results extend the findings of Zhang et al. [ 33 ], who first described the miR‑581/PRDX1 axis in CRC. The present study expands this model by characterizing the expression dynamics of this axis in primary and metastatic cell lines, thereby explaining the sustained high expression of PRDX1 observed in both primary and metastatic lesions. The identification of miR‑581 as a negative regulator provides a potential strategy for targeted intervention. Notably, miR‑581 was highly expressed in SW620 cells, yet PRDX1 remained upregulated in metastatic lesions. This suggests that additional regulatory mechanisms, such as those involving other non‑coding RNAs, transcription factors, or epigenetic modifications, may cooperate with miR‑581 to fine‑tune PRDX1 expression. Future studies employing multi‑omics approaches (e.g., RNA‑seq, ChIP‑seq) are warranted to comprehensively map the PRDX1 regulatory network. The miR‑581/PRDX1 axis holds significant potential for clinical translation. Strategies targeting PRDX1 include the development of miR‑581 mimics for systemic or local delivery to downregulate PRDX1 [ 34 , 35 ], as well as the design of specific PRDX1 inhibitors to block its antioxidant and signaling functions [ 36 – 39 ]. When combined with PD‑1/PD‑L1 blockade, such approaches may simultaneously inhibit tumor proliferation and reverse immune suppression, offering a novel therapeutic paradigm for microsatellite stable (MSS) CRC, which currently has limited response rates to immunotherapy. The core innovations of this study include: the characterization of PRDX1 as a CRC‑specific overexpressed biomarker and the construction of an integrated prognostic nomogram; the identification of the immune‑proliferation dual network as a key mechanism driving PRDX1‑mediated CRC progression; and the validation of miR‑581 as a direct upstream regulator of PRDX1 in CRC cell lines. This study is not without limitations. First, the molecular depth of the mechanism remains to be strengthened. Although the dual network model has been established, the key downstream effectors and pathway cross‑talk nodes have not been fully elucidated. Future studies employing phosphoproteomics, RNA sequencing, and dual‑luciferase assays are needed to identify critical regulatory nodes [ 40 – 44 ]. Second, in vivo validation is lacking. Xenograft models and liver metastasis models should be established to evaluate the role of PRDX1 and miR‑581 in tumor growth, metastasis, and immune infiltration [ 45 , 46 ]. Third, the clinical sample size and follow‑up duration are relatively limited. Future multicenter, prospective studies with extended follow‑up are required to validate the prognostic and predictive value of PRDX1 in diverse clinical settings [ 47 , 48 ]. In summary, this study demonstrates that PRDX1 is specifically overexpressed in CRC and drives malignant progression through the immune‑proliferation dual network. PRDX1 is negatively regulated by miR‑581. These findings establish PRDX1 as a potential prognostic biomarker and a promising target for combination immunotherapy in CRC. The miR‑581/PRDX1 axis provides a novel molecular direction for precision intervention. Further translational studies are warranted to advance PRDX1 from bench to bedside, ultimately improving the diagnosis and treatment of CRC. Conclusions In conclusion, the present study identified that PRDX1 exhibits prominent upregulation in colorectal cancer among multiple cancer types, with its expression significantly elevated in tumor tissues relative to adjacent normal tissues and gradually increased alongside advanced clinical staging. High PRDX1 expression was closely correlated with aggressive clinicopathological features, advanced T/N stage, and elevated TMB and MSI in CRC, indicating its potential value for predicting immunotherapy response. Mechanically, PRDX1 drives the malignant progression of CRC via regulating the immune‑proliferation dual network, while its expression is negatively modulated by miR‑581. Collectively, PRDX1 can serve as a reliable prognostic biomarker and a promising therapeutic target for combination immunotherapy in CRC. The miR‑581/PRDX1 regulatory axis provides an innovative molecular basis for precise targeted intervention of CRC. Further translational investigations are required to validate these findings and facilitate the clinical application of PRDX1, so as to optimize the diagnostic and therapeutic strategies for colorectal cancer. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Shanghai Pudong Hospital Affiliated to Fudan University (No. FDPH-2024-012). This study was conducted in accordance with the Declaration of Helsinki. Availability of data and materials The data that support the findings of this study are available from the public databases. TCGA Database: The RNA-seq transcriptome data and corresponding clinical information of colorectal cancer samples were downloaded from the TCGA database (Project ID: TCGA-COAD, https://portal.gdc.cancer.gov/). TIMER Database: The immune infiltration scores were analyzed using the TIMER2.0 database (http://timer.cistrome.org/) for visualizing the abundance of tumor-infiltrating immune cells. Conflict of interest The authors declare no conflicts of interest associated with this study. Funding resource This study was funded by Science and Technology Development Fund Of Shanghai Pudong New Area (Grant No. PKJ2024-Y110) and the Key Subject Group Grant of the Discipline Construction Project from Shanghai Pudong New Area Health Commission (No. PWZxq2022‑14). Author Contributions Tingting Ding: Performed bioinformatics analysis of the PRDX1 gene in colorectal cancer, organized and analyzed experimental data, conducted survival and prognosis analysis, and drafted the initial manuscript. Xianzhi Guo: Constructed and visualized all related figures and tables, and performed clinical correlation analysis. Chuxiong Zeng and Lina Hu: Collected clinical cases in strict accordance with the inclusion and exclusion criteria. Jun Ren: Conceptualized and supervised the project, administered the study, critically reviewed, revised and polished the manuscript. All authors have contributed significantly to this study and approved the final version to be published. Acknowledgements Not applicable. Clinical trial number Not applicable. Consent to publish Not applicable References Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. 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Tables Table 1: Independent prognostic analysis of PRDX1 in CRC (uniCox and multiCox). uniCox multiCox ID HR HR.95L HR.95H pvalue HR HR.95L HR.95H pvalue PRDX1 0.8423 0.6369 1.1140 0.2289 0.7516 0.5598 1.0091 0.0575 Age 1.0258 1.0081 1.0438 0.0041 1.0362 1.0178 1.0550 0.0001 Gender 1.2491 0.8656 1.8023 0.2346 1.2471 0.8625 1.8032 0.2405 Grade 1.3609 0.9691 1.9110 0.0753 1.4245 1.0107 2.0078 0.0433 Stage 1.5335 1.2406 1.8956 0.0001 1.6546 1.3165 2.0796 0.0000 Table 2a: GO function of the top 6 genes most positively associated with PRDX1in CRC. (BP: biological processes; MF: molecular function; CC: cell component) Description（GO） Count pvalue p.adjust qvalue BP muscle system process 75 5.80E-28 2.41E-24 1.88E-24 regulation of membrane potential 73 1.02E-26 2.05E-23 1.60E-23 muscle contraction 64 1.48E-26 2.05E-23 1.60E-23 regulation of blood circulation 45 7.45E-18 7.73E-15 6.05E-15 axonogenesis 59 2.40E-17 1.99E-14 1.56E-14 regulation of heart contraction 39 6.09E-17 4.21E-14 3.30E-14 CC synaptic membrane 72 2.97E-26 1.26E-23 9.49E-24 postsynaptic membrane 54 4.84E-21 1.02E-18 7.74E-19 contractile muscle fiber 42 2.39E-16 3.37E-14 2.55E-14 collagen-containing extracellular matrix 54 3.43E-15 2.16E-13 1.63E-13 presynaptic membrane 34 3.57E-15 2.16E-13 1.63E-13 postsynaptic specialization 51 6.16E-15 3.26E-13 2.46E-13 MF monoatomic ion channel activity 53 3.28E-14 1.30E-11 1.11E-11 gated channel activity 42 4.20E-13 1.11E-10 9.46E-11 metal ion transmembrane transporter activity 45 8.35E-10 1.65E-07 1.41E-07 heparin binding 25 3.40E-09 4.98E-07 4.26E-07 potassium channel activity 21 3.78E-09 4.98E-07 4.26E-07 voltage-gated monoatomic cation channel activity 22 1.02E-08 1.15E-06 9.88E-07 Table 2b: Significant KEGG pathways of the top 10 genes most positively associated with PRDX1. Description Count pvalue p.adjust qvalue muscle system proces 13 1.23E-13 3.20E-11 2.71E-11 regulation of membranepotential 19 3.39E-11 4.42E-09 3.74E-09 antigen processing and presentation 37 2.68E-10 2.33E-08 1.97E-08 PI3K-Akt signaling pathway 35 2.84E-09 1.86E-07 1.57E-07 synapse assembly -regulation of monoatomicion 17 9.51E-09 4.96E-07 4.21E-07 th1 and th2 cell3e-08differentiation 33 3.10E-06 0.0001 0.000114222 regulation of synapse 13 1.93E-05 0.0007 0.000609533 potassium ion transport 10 2.77E-05 0.0009 0.000765379 axonguidance 19 3.61E-05 0.001046262 0.000886126 Rap1 signaling pathway 38 0.0001 0.00346001 0.002930438 Table 2c: Top 5 positive enriched pathways based on GSEA analyzes. Description setSize pvalue p.adjust qvalues KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY 377 1.00E-10 6.20E-09 4.84E-09 KEGG_IL-17_SIGNALING_PATHWAY 178 1.00E-10 6.20E-09 4.84E-09 KEGG_FOCAL_ADHESION 270 1.00E-10 6.20E-09 4.84E-09 KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION 199 6.38E-10 2.96E-08 2.32E-08 KEGG_OLFACTORY_TRANSDUCTION 74 1.82E-09 6.79E-08 5.30E-08 Table 3: Baseline clinical characteristics of 50 CRC patients in our hospital Variable Case (N=50) Number % Age ≤65 24 48 ＞65 26 52 Gender female 20 40 male 30 60 Grade G1 4 8 G2 27 54 G3 18 36 Gx 1 2 Stage I 8 16 II 12 24 III 23 46 IV 7 14 T stage T1+T2 8 16 T3+T4 42 84 Lymph node N0 20 40 N1 7 14 N2 23 46 Nx 0 0 Metastasis M0 43 86 M1 7 14 Table 4: Top 20miRNAs targeting PRDX1 predicted by TargetScan miRNAs Context++ score Context++ score percentile Conserved branch length hsa-miR-4465 -0.26 90 2.575 hsa-miR-26b-5p -0.22 87 2.575 hsa-miR-26a-5p -0.22 88 2.575 hsa-miR-1297 -0.22 87 2.575 hsa-miR-375 -0.28 99 2.408 hsa-miR-487a-3p -0.29 98 0.818 hsa-miR-154-3p -0.28 97 0.818 hsa-miR-183-5p.2 -0.29 94 0.793 hsa-miR-655-3p -0.48 99 0.655 hsa-miR-374c-5p -0.49 99 0.655 hsa-miR-8073 -0.19 91 0.389 hsa-miR-221-5p -0.17 87 0.389 hsa-miR-4496 -0.01 64 0.367 hsa-miR-581 -0.73 99 0.054 hsa-miR-539-3p -0.28 93 0.054 hsa-miR-499b-3p -0.19 93 0.054 hsa-miR-499a-3p -0.19 93 0.054 hsa-miR-485-3p -0.23 95 0.054 hsa-miR-889-5p -0.22 91 0.048 hsa-miR-4677-5p -0.16 91 0.048 Additional Declarations No competing interests reported. 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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-9346438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635156936,"identity":"16ebc68b-d2c4-4af2-8705-ce0f65defae6","order_by":0,"name":"Tingting Ding","email":"","orcid":"","institution":"Fudan University Pudong Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Ding","suffix":""},{"id":635156938,"identity":"59cff926-4f41-4813-a8d6-090d5db8c5b4","order_by":1,"name":"Xianzhi Guo","email":"","orcid":"","institution":"Fudan University Pudong Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xianzhi","middleName":"","lastName":"Guo","suffix":""},{"id":635156939,"identity":"5e3bd132-72f6-4671-84e2-48f3796960ef","order_by":2,"name":"Chuxiong Zeng","email":"","orcid":"","institution":"Fudan University Pudong Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Chuxiong","middleName":"","lastName":"Zeng","suffix":""},{"id":635156944,"identity":"e23ca700-831c-4883-9157-8ecdc8409fc4","order_by":3,"name":"Lina Hu","email":"","orcid":"","institution":"Fudan University Pudong Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Hu","suffix":""},{"id":635156970,"identity":"001175ff-2c8c-4e81-bc73-e03bf3dceb9d","order_by":4,"name":"Jun Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxgYILcdwgA1EMxOvxZh4LTCQ2EC0FuYZuQeYKyrupPfdSEt8wFBhndhA0GEz8hIYz5x5ljvzRtphA4Yz6cRoyTFgbGw7nLvhRnqbBGPbYWK1/DucbnAjvf0H4z+itTQcTjC4kXYMGH7EaOl5Y3Cw4dhhw5lnniVLJBxLNyaoxbA9x/BhQ81heb7jaYYfPtRYyxLWAlRxAM5LIKQcBOSJUTQKRsEoGAUjHAAApa5D9nJlv0sAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University Huadong Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2026-04-07 14:24:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9346438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9346438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108812207,"identity":"dae9ca73-1e20-4c71-a739-5e65b477f7ce","added_by":"auto","created_at":"2026-05-08 16:09:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76257,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design. Schematic illustration of the overall workflow, including data collection, bioinformatic analysis, experimental validation, and mechanistic integration.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/c9d5df187c1a6d363f7ac612.png"},{"id":108812194,"identity":"690f1e79-f54f-4e53-a220-94ccb897864a","added_by":"auto","created_at":"2026-05-08 16:09:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression profile and clinical correlations of PRDX1 in pan-cancer and colorectal cancer (CRC).\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Expression pattern of PRDX1 across multiple cancer types. \u003cstrong\u003e(B)\u003c/strong\u003e Relative expression of PRDX1 in CRC tumor tissues and adjacent normal tissues (Mann–Whitney U test, p\u0026lt;0.001). \u003cstrong\u003e(C)\u003c/strong\u003e PRDX1 expression in different pathological stages of CRC (Stage Ⅳ vs. Stage Ⅰ, P=0.012). \u003cstrong\u003e(D) \u003c/strong\u003eCorrelation heatmap between PRDX1 expression and clinicopathological characteristics of CRC. Red, positive correlation; blue, negative correlation; *p\u0026lt;0.05. \u003cstrong\u003e(E)\u003c/strong\u003e Correlation analysis between PRDX1 expression and TMB or MSI in pan-cancer (Spearman correlation, ***p\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/f3300f9a83b052b4ef69b220.png"},{"id":108814811,"identity":"8c8220e1-54cd-4581-a54b-34402bf5382f","added_by":"auto","created_at":"2026-05-08 16:20:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic value of PRDX1 in patients with CRC. (A)\u003c/strong\u003eKaplan–Meier survival curves for OS, DFS, and DSS based on PRDX1 expression levels (log-rank test, *p\u0026lt;0.05, ***p\u0026lt;0.001). \u003cstrong\u003e(B)\u003c/strong\u003e ROC curves of the PRDX1-based nomogram for 1‑year, 3‑year, and 5‑year OS prediction. \u003cstrong\u003e(C)\u003c/strong\u003e Calibration curves of the nomogram for predicting 1‑year, 3‑year, and 5‑year OS. \u003cstrong\u003e(D)\u003c/strong\u003e Nomogram integrating PRDX1 expression and clinicopathological features for survival prediction. \u003cstrong\u003e(E-F)\u003c/strong\u003e Forest plots of univariate and multivariate Cox regression analyses for OS. HR, hazard ratio; 95% CI, 95% confidence interval; *p\u0026lt;0.05, ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/cf01e77a32317181ccf6b8ab.png"},{"id":108812195,"identity":"01316671-790f-427a-ae8c-b2fa0e24452c","added_by":"auto","created_at":"2026-05-08 16:09:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment and immune-related prognostic analysis of PRDX1 in CRC. (A)\u003c/strong\u003e GO, KEGG, and GSEA analyses of PRDX1-associated differentially expressed genes (FDR\u0026lt;0.05). \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of immune‑related risk scores, survival status, and expression heatmap of key immune genes in the high‑ and low‑risk groups. \u003cstrong\u003e(C)\u003c/strong\u003e Protein–protein interaction (PPI) network of PRDX1 and immune‑related proteins. Red lines, positive regulation; green lines, negative regulation. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation heatmap between PRDX1 and immune checkpoint molecules. Red, positive correlation; blue, negative correlation.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/68de6fd79cd6661ba656aa45.png"},{"id":108812231,"identity":"7fab49c8-bdff-4a84-bd82-23a38ccb3f1f","added_by":"auto","created_at":"2026-05-08 16:09:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of PRDX1 with tumor microenvironment and immunotherapy response. (A) \u003c/strong\u003eComparison of stromal, immune, and ESTIMATE scores between PRDX1 high‑ and low‑expression groups (**p\u0026lt;0.01, *p\u0026lt;0.05). \u003cstrong\u003e(B)\u003c/strong\u003e Differential immune cell infiltration between the two groups.\u003cstrong\u003e (C)\u003c/strong\u003e Forest plot of correlations between PRDX1 expression and immune cell infiltration.\u003cstrong\u003e(D)\u003c/strong\u003e Scatter plots of PRDX1 expression with key immune cell subsets (Spearman correlation, *p\u0026lt;0.05).\u003cstrong\u003e(E)\u003c/strong\u003e Immunophenoscore (IPS) comparisons in different immunotherapy subgroups (*p\u0026lt;0.05). \u003cstrong\u003e(F)\u003c/strong\u003e Correlation between PRDX1 expression and TMB (Spearman correlation, R=0.31, p=1.9×10⁻⁹).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/2690d5d2a9bef77061646776.png"},{"id":108812226,"identity":"8ed07f0c-63b9-4acc-87d1-3ae65eb83a88","added_by":"auto","created_at":"2026-05-08 16:09:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":435337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental validation of PRDX1 expression in clinical CRC tissues. (A–C)\u003c/strong\u003e Immunohistochemical staining of PRDX1 in normal mucosa, primary CRC, and metastatic lesions (scale bar=100 μm). \u003cstrong\u003e(D–F)\u003c/strong\u003e mRNA and protein expression levels of PRDX1 in CRC and adjacent normal tissues detected by qPCR and Western blot (***p\u0026lt;0.001). \u003cstrong\u003e(G–H)\u003c/strong\u003e Multiplex immunofluorescence staining and quantitative analysis of PRDX1, CD68, and CD4 in CRC tissues (scale bar=50 μm, *p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/0c38f9846d07bfe4dacd6cd6.png"},{"id":109081169,"identity":"09157370-663c-432e-b1ed-085662685075","added_by":"auto","created_at":"2026-05-12 12:03:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vitro validation of the miR‑581‑PRDX1 regulatory axis in CRC cells. (A) \u003c/strong\u003eBasal expression levels of miR‑581 and PRDX1 in SW480 and SW620 cells (*p\u0026lt;0.05). \u003cstrong\u003e(B–C)\u003c/strong\u003e PRDX1 expression following lentivirus‑mediated miR‑581 knockdown in SW480 and overexpression in SW620 (**p\u0026lt;0.01). \u003cstrong\u003e(D–E)\u003c/strong\u003e Immunofluorescence imaging of PRDX1 in transfected CRC cells (scale bar=20 μm).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/37e18ef67f2bc8944ef8cc8a.png"},{"id":109082688,"identity":"7b65ce41-a614-450f-892c-ed3b8439ab75","added_by":"auto","created_at":"2026-05-12 12:42:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2015226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/9fffeb09-b664-4ab1-8da4-0c1629f03155.pdf"},{"id":108812202,"identity":"eef27151-d6fd-4a84-9f94-3c9e8ed59356","added_by":"auto","created_at":"2026-05-08 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":148308,"visible":true,"origin":"","legend":"","description":"","filename":"GelsandBlotsoriginalimages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9346438/v1/56023152cc27df421729c4e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PRDX1 as the potential marker to promotes pathogenetic progression of colorectal cancer via up-regulating the immune-proliferation pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) represents one of the most prevalent and lethal gastrointestinal malignancies worldwide. In 2020, CRC accounted for more than 1.9\u0026nbsp;million new diagnoses and approximately 1\u0026nbsp;million deaths globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, the annual incidence exceeds 420,000 cases, with a marked urban\u0026ndash;rural disparity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A striking rise in early-onset CRC has further intensified clinical challenges, underscoring the demand for improved early detection and effective intervention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite advances in precision medicine, including lowered screening age, optimized imaging strategies, and biomarker-guided immunotherapy, clinical outcomes remain unsatisfactory for most patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While microsatellite instability‑high (MSI‑H/dMMR) tumors benefit substantially from immune checkpoint blockade, the majority of CRC cases are microsatellite stable (MSS) and respond poorly to current immunotherapies, with response rates below 10% [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, the identification of robust predictive biomarkers and actionable therapeutic targets remains a critical unmet need in CRC, especially for immunotherapy‑resistant subgroups.\u003c/p\u003e \u003cp\u003eTumor progression and immune evasion are tightly governed by dynamic remodeling of the tumor microenvironment (TME). Disrupted crosstalk between malignant cells, immune subsets, stromal components, and the gut microbiota impairs anti‑tumor immunity and fosters therapeutic resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Immune suppression driven by M2‑polarized macrophages and regulatory T cells (Tregs) restrains CD8\u0026thinsp;+\u0026thinsp;T cell‑mediated cytotoxicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Molecularly, aberrations in the IFNγ‑MHC‑I antigen presentation axis and KRAS‑dependent innate immune signaling further disable anti‑tumor immunosurveillance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although strategies including macrophage repolarization and viral mimicry‑based immune activation have shown preclinical promise [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], molecules that coordinately regulate both proliferative and immune‑related signaling networks remain largely unexplored.\u003c/p\u003e \u003cp\u003ePeroxiredoxin 1 (PRDX1) is a multifunctional redox sensor and signaling regulator with context‑dependent roles in tumorigenesis. By maintaining intracellular reactive oxygen species (ROS) balance, PRDX1 protects against oxidative DNA damage and malignant transformation in early stages; yet in established tumors, it drives proliferation, survival, and invasion via redox‑dependent modulation of STAT3, NF‑κB, and other oncogenic pathways [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emerging evidence also implicates PRDX1 in TME remodeling, immune cell infiltration, and immune checkpoint expression, suggesting a broader role in immune escape [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the expression pattern, clinical significance, and mechanistic basis of PRDX1 in CRC remain poorly defined. In particular, whether PRDX1 acts as a molecular hub linking proliferative signaling and immune regulation, and how it is transcriptionally or post‑transcriptionally controlled, have not been systematically addressed.\u003c/p\u003e \u003cp\u003eTo further clarify the biological functions of PRDX1, we performed bioinformatics analysis using public databases. We subsequently validated the consistency of these results through cell line experiments and clinical colorectal cancer (CRC) tissue specimens. we have explored the biological roles of PRDX1 and expression status in CRC tissues respectively.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics Analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eData resource and standardization\u003c/h2\u003e \u003cp\u003ePan-cancer expression profiles of peroxiredoxin 1 (PRDX1) were retrieved and analyzed using the TIMER2.0 web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). RNA-sequencing data (FPKM/TPM), corresponding clinical annotations, and somatic mutation profiles of colorectal cancer (CRC) were obtained from The Cancer Genome Atlas (TCGA) database. Only samples with complete survival information, pathological stage, and histological grade were included for subsequent analysis. Raw expression data were normalized to transcripts per million (TPM) and processed using variance-stabilizing transformation in the DESeq2 or edgeR package in R software to eliminate batch effects and ensure comparability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential Expression and Clinical Correlation Analysis\u003c/h3\u003e\n\u003cp\u003eDifferential expression of PRDX1 between tumor tissues and normal tissues was analyzed using the limma, DESeq2, and edgeR packages. Genes with |log₂(fold change)| \u0026gt; 1 and adjusted P-value (false discovery rate, FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly differentially expressed. Expression distribution was visualized using boxplots. Patients were stratified into high-PRDX1 and low-PRDX1 expression groups according to the median expression value. Correlations between PRDX1 expression and clinicopathological characteristics, including TNM stage, lymph node metastasis, and distant metastasis, were evaluated using the chi-square test or Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003ch3\u003eSurvival Analysis\u003c/h3\u003e\n\u003cp\u003eSurvival endpoints including overall survival (OS), disease-specific survival (DSS), and disease-free interval (DFI) were defined. Survival curves were plotted using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional hazard regression models were constructed to assess the independent prognostic value of PRDX1, with adjustment for age and clinical stage. The proportional hazards assumption was verified prior to model construction.\u003c/p\u003e\n\u003ch3\u003eTumor Mutational Burden and Microsatellite Instability Analysis\u003c/h3\u003e\n\u003cp\u003eTumor mutational burden (TMB) was calculated as the number of non-synonymous single-nucleotide variants and frameshift insertions/deletions per megabase of the coding genome. Microsatellite instability (MSI) status was obtained from matched TCGA variant datasets. Correlations between PRDX1 expression and TMB or MSI were analyzed using the Mann\u0026ndash;Whitney U test or Spearman correlation and visualized using scatter plots.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler package. Gene set enrichment analysis (GSEA) was conducted using the fgsea package. Terms with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as significantly enriched. Results were visualized using bubble plots, bar charts, and enrichment score plots.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmune Cell Infiltration and Immunotherapy Analysis\u003c/h3\u003e\n\u003cp\u003eThe relative proportions of tumor-infiltrating immune cells were quantified using CIBERSORT and other immune deconvolution algorithms. Correlations between PRDX1 expression and immune cell infiltration, as well as immunotherapy-related biomarkers including TMB, MSI, and PD-L1, were analyzed. A predictive model for immunotherapy response was established using LASSO regression.\u003c/p\u003e\n\u003ch3\u003eClinical Data and Basic Experimental Validation\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical Sample Collection\u003c/h2\u003e \u003cp\u003eA total of 50 patients with histologically confirmed primary colorectal cancer were enrolled between January 2024 and December 2025 at the Department of Gastrointestinal Surgery, Fudan University Pudong Medical Center. No patients had received preoperative antitumor therapy. Paired tumor tissues and adjacent normal tissues (\u0026ge;\u0026thinsp;5 cm from the tumor border) were collected. Clinicopathological parameters, including age, gender, TNM stage, histological grade, and lymph node status, were documented. From these specimens, eligible cases were further selected by stratified random sampling according to clinical stage, histological grade, and lymph node metastasis status for subsequent experimental validation. Ethical approval was obtained from the Ethics Committee of Fudan University Pudong Medical Center (No. FDPH-2024-012). All procedures were performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry and Western Blot Analysis\u003c/h2\u003e \u003cp\u003eTumor tissues and paired adjacent normal tissues were selected by stratified random sampling based on clinical stage, histological grade, and lymph node metastasis status. For immunohistochemistry, paraffin sections were deparaffinized, rehydrated, and subjected to antigen retrieval. Endogenous peroxidase was blocked, and non-specific binding was eliminated. Sections were incubated with anti-PRDX1 antibody (Servicebio, 1:100) overnight at 4\u0026deg;C, followed by horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. Staining was developed with 3,3'-diaminobenzidine and counterstained with hematoxylin. Slides were mounted and independently assessed by two pathologists in a double-blind manner.\u003c/p\u003e \u003cp\u003eFor Western blot analysis, total protein was extracted using RIPA lysis buffer supplemented with protease and phosphatase inhibitors. Protein concentration was determined using a BCA assay kit. Equal amounts of protein were separated by 10\u0026ndash;12% SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% non-fat milk or BSA for 1 h and incubated with primary antibodies against PRDX1 (1:1000) and GAPDH (1:2000) overnight at 4\u0026deg;C. After washing, membranes were incubated with horseradish peroxidase-conjugated secondary antibody (1:5000) for 1 h at room temperature. Protein bands were visualized using ECL reagent and quantified using ImageJ software. GAPDH was used as the loading control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Real-Time PCR Analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from tissues and cells using a commercial RNA extraction kit. RNA purity and concentration were measured using a microspectrophotometer. cDNA was synthesized using a reverse transcription kit with genomic DNA removal. Quantitative real-time PCR was performed using SYBR Premix Ex Taq\u0026trade; II. Amplification was conducted at 95\u0026deg;C for 5 min, followed by 45 cycles of 95\u0026deg;C for 20 s and 60\u0026deg;C for 60 s. GAPDH was used as the internal reference. Relative gene expression was calculated using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTSA Multiplex Immunofluorescence Staining\u003c/h2\u003e \u003cp\u003eParaffin-embedded tissue sections were deparaffinized, rehydrated, and processed for antigen retrieval. Cyclic immunofluorescence staining was performed using antibodies against PRDX1 (1:100), CD68 (1:150), and CD4 (1:200) overnight at 4\u0026deg;C, followed by horseradish peroxidase-conjugated secondary antibody and TSA fluorescent dye. Nuclei were counterstained with DAPI. Stained sections were scanned using a digital pathology scanner. Cell typing, quantitative counting, and co-localization analysis were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and reagents\u003c/h2\u003e \u003cp\u003eHuman colorectal cancer cell lines SW480 and SW620 were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and authenticated by short tandem repeat profiling. MRC fetal bovine serum was obtained from Jiangsu Enmo Aisa Biotechnology Co., Ltd. RPMI 1640 and L-15 media were purchased from Boster Biological Technology. Lentiviral vectors for miR-581 knockdown, overexpression, and negative control were constructed by Shanghai GeneChem Co., Ltd. Puromycin and 0.25% trypsin-EDTA were purchased from GIBCO. Rabbit anti-human antibodies against PRDX1 and GAPDH were obtained from Abcam (UK). Horseradish peroxidase-conjugated secondary antibody and Alexa Fluor 488-conjugated secondary antibody were purchased from Cell Signaling Technology (USA). PCR primers were synthesized by Shanghai Sangon Biotech Co., Ltd. Total RNA was extracted using a commercial kit from Thermo Fisher Scientific (USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture\u003c/h2\u003e \u003cp\u003eFrozen SW480 and SW620 cells were thawed rapidly in a 37\u0026deg;C water bath, resuspended in pre-warmed medium, and centrifuged at 1000 rpm for 5 min. Cells were cultured in T25 flasks at 37\u0026deg;C in a humidified 5% CO₂ atmosphere. SW480 cells were maintained in RPMI 1640 medium, and SW620 cells in L-15 medium, both supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were passaged at approximately 80% confluence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLentiviral Infection\u003c/h2\u003e \u003cp\u003eCells in the logarithmic growth phase were seeded at 5\u0026times;10⁴ cells/mL to reach 15\u0026ndash;30% confluence at infection. Cells were transduced with miR-581 knockdown, overexpression, or negative control lentivirus. Culture medium was replaced after 8\u0026ndash;12 h. Puromycin was added at 24 h post-infection to select stably transfected cell lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence Assay\u003c/h2\u003e \u003cp\u003eCells were seeded onto coverslips, fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and blocked with 5% BSA. Cells were incubated with anti-PRDX1 antibody overnight at 4\u0026deg;C, followed by fluorescent secondary antibody for 1 h in the dark. Nuclei were counterstained with DAPI. Subcellular localization of PRDX1 was observed and recorded under a laser scanning confocal microscope.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Design Flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was performed using an integrated workflow consisting of data and sample collection, bioinformatics analysis, experimental validation, and mechanistic interpretation. Experimental procedures included immunohistochemistry (IHC), quantitative real‑time polymerase chain reaction (qPCR), Western blot (WB), multiplex immunofluorescence, and cellular functional assays. The workflow was designed to characterize the expression, prognostic significance, and immune regulatory functions of PRDX1 in colorectal cancer (CRC), and to identify potential targets for targeted therapy and immunotherapy. The detailed workflow is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePan‑Cancer Expression of PRDX1 and Its Clinical and Immune Correlations in CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePan‑cancer differential expression analysis demonstrated that PRDX1 was most prominently overexpressed in CRC among 33 included cancer types. PRDX1 expression (log₂ TPM) was significantly higher in CRC tumor tissues (n = 457) than in adjacent normal tissues (n = 41, *p*\u0026lt; 0.001) (Figures 2A, 2B).\u003c/p\u003e\n\u003cp\u003ePRDX1 expression increased gradually from stage Ⅰ to stage Ⅳ, with a significant difference observed between stage Ⅳ and stage Ⅰ (*p*= 0.012) (Figure 2C). Correlation analysis revealed that high PRDX1 expression was significantly associated with advanced clinical stage, high pathological grade, and advanced T/N stage (all *p*\u0026lt; 0.05), but not with age or sex (*p*\u0026gt; 0.05) (Figure 2D).\u003c/p\u003e\n\u003cp\u003ePan‑cancer correlation analysis further revealed that PRDX1 expression was significantly and positively associated with tumor mutational burden (TMB) and microsatellite instability (MSI) in a CRC‑specific manner (both *p*\u0026lt; 0.001) (Figure 2E). These results indicate that PRDX1 may serve as a potential auxiliary biomarker for predicting immunotherapeutic response in CRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic Value of PRDX1 in CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analyses demonstrated that high PRDX1 expression was significantly associated with unfavorable prognosis. Patients with high PRDX1 expression exhibited significantly shorter disease‑free survival (DFS, *p*= 0.047), disease‑specific survival (DSS, *p*= 0.042), and overall survival (OS, *p*= 0.003) (Figure 3A).\u003c/p\u003e\n\u003cp\u003eA nomogram model based on PRDX1 expression was constructed for OS prediction. The AUC values for 1‑, 3‑, and 5‑year OS were 0.484, 0.471, and 0.389, respectively. Calibration curves showed good consistency between predicted and actual survival probabilities (Figures 3B, 3C). An integrated nomogram combining PRDX1 expression and clinicopathological features was also established (Figure 3D).\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression indicated that clinical stage and age were independent prognostic factors for OS. Multivariate Cox regression confirmed that clinical stage, age, and pathological grade remained independent predictors. The hazard ratio (HR) for PRDX1 was 0.752 (95% CI: 0.560–1.009, *p*= 0.058), suggesting a marginal prognostic trend (Figure 3F; Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Immune Prognostic Analysis of PRDX1‑Related Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analyses were conducted to explore the biological roles of PRDX1‑related differentially expressed genes (Figure 4A). Gene Ontology (GO) enrichment showed significant enrichment in antigen processing and presentation, and Th1/Th2 cell differentiation. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) confirmed significant enrichment in T‑cell receptor signaling and IL‑17 signaling pathways (false discovery rate, FDR \u0026lt; 0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003eAn immune‑related prognostic risk model was constructed. Patients were clearly stratified into low‑ and high‑risk groups. The high‑risk group showed higher mortality and shorter survival time. Immune‑related genes such as EGFR were upregulated in the high‑risk group (Figure 4B).\u003c/p\u003e\n\u003cp\u003eProtein–protein interaction (PPI) analysis identified PRDX1 as a hub molecule interacting with LGALS9, FOXP3, and STAT1. Correlation analysis showed weak positive associations between PRDX1 and HHLA2, LGALS9, and TNFRSF14, and inverse correlations with NRP1 and CD200 (Figures 4C, 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRDX1‑Mediated Regulation of the Tumor Microenvironment and Immunotherapy Response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESTIMATE analysis revealed that stromal, immune, and ESTIMATE scores were significantly lower in the high‑PRDX1 group (all *p*\u0026lt; 0.05) (Figure 5A). Immune infiltration analysis showed that high PRDX1 expression was associated with increased immunosuppressive cells and decreased effector immune cells (Figure 5B).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis confirmed that PRDX1 expression was positively correlated with plasma cells and CD4⁺ memory activated T cells, and inversely correlated with Tregs and monocytes (all *p*\u0026lt; 0.05) (Figures 5C, 5D). The immunophenoscore (IPS) was significantly higher in the high‑PRDX1 group across CTLA4‑ and PD1‑based immunotherapy subgroups (all *p*\u0026lt; 0.05) (Figure 5E). PRDX1 expression was strongly positively correlated with TMB (R = 0.31, *p*= 1.9 × 10⁻⁹) (Figure 5F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of PRDX1 Expression in CRC Tissues and Association with the Immune Microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the bioinformatic findings, 50 clinical colorectal cancer (CRC) tissue specimens were collected at our institution. The detailed clinicopathological characteristics of these patients are summarized in Table 3. Briefly, among the 50 CRC cases included in the immunohistochemical (IHC) analysis, there were 8 cases at stage I, 12 at stage II, 23 at stage III, and 7 at stage IV. With regard to histological grade, 4 cases (8%) were classified as G1 (well-differentiated), 27 (54%) as G2 (moderately differentiated), and 18 (36%) as G3 (poorly differentiated). Lymph node metastasis was detected in 29 cases (58%), and distant metastasis was present in 8 cases (16%). The age and gender distribution was consistent with the total cohort, supporting the representativeness of samples used for IHC analysis. IHC staining showed that PRDX1 was weakly expressed in adjacent normal tissues, remarkably upregulated in primary CRC lesions, and maintained at high levels in metastatic lesions. PRDX1 was mainly localized in CRC cells, with moderate staining detected in vascular endothelial cells, indicating that aberrantly high expression of PRDX1 is closely associated with CRC development and distant metastasis (Figures 6A–6C).\u003c/p\u003e\n\u003cp\u003eqPCR and Western blot analyses further validated PRDX1 expression at transcriptional and translational levels (Figures 6D–6F). Compared with matched adjacent normal tissues (N), mRNA and protein levels of PRDX1 were both significantly increased in primary CRC tissues (T) (P\u0026lt;0.001), confirming the consistent aberrant overexpression of PRDX1 in CRC, which was highly concordant with our TCGA‑based bioinformatic findings.\u003c/p\u003e\n\u003cp\u003eMultiplex immunofluorescence staining revealed that PRDX1 was colocalized with CD68 (an M1 macrophage marker) and CD4 (a T lymphocyte marker) in CRC tissues (Figures 6G–6H). Expression levels of all three markers were significantly higher in tumor tissues than in normal tissues (P\u0026lt;0.05). Correlation analysis demonstrated a positive association between PRDX1 expression and CD68 or CD4 expression (P\u0026lt;0.05), which corroborated our bioinformatic observation that PRDX1 is positively correlated with immune‑related molecules. These results suggest that PRDX1 may contribute to the regulation of the CRC tumor microenvironment, potentially through direct interactions with immune cells such as modulating cytokine secretion and cellular activation signaling. Further studies will be required to elucidate the precise molecular mechanisms underlying these interactions.\u003c/p\u003e\n\u003cp\u003eThe top 20 candidate miRNAs targeting PRDX1 were predicted using TargetScan (Table 4). Among these candidates, miR‑581 was selected for further validation based on literature review and preliminary experimental data.Baseline expression analysis showed that miR‑581 was weakly expressed in SW480 cells but highly expressed in SW620 cells, whereas PRDX1 exhibited the opposite expression pattern (Figure 7A).\u003c/p\u003e\n\u003cp\u003eLentivirus‑mediated knockdown of miR‑581 in SW480 cells significantly increased PRDX1 expression (P\u0026lt;0.01). Conversely, overexpression of miR‑581 in SW620 cells significantly reduced PRDX1 expression (P\u0026lt;0.01) (Figures 7B–7C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmunofluorescence staining further confirmed these observations: PRDX1 fluorescence intensity was strongly enhanced in miR‑581‑deficient SW480 cells and markedly reduced in miR‑581‑overexpressing SW620 cells compared with control groups (Figures 7D–7E). These findings verify that miR‑581 acts as a direct upstream negative regulator of PRDX1 in CRC cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, an integrated strategy composed of multi‑omics bioinformatics, clinical tissue validation, and cellular functional experiments was implemented to systematically characterize the expression patterns, clinical significance, and molecular mechanisms of peroxiredoxin 1 (PRDX1) in colorectal cancer (CRC). To our knowledge, this report provides the first comprehensive evidences that PRDX1 promotes CRC progression through the coordinated regulation of a previously unreported immune‑proliferation dual network, and is negatively regulated by miR‑581. These findings establish PRDX1 as a potential prognostic biomarker and a promising therapeutic target for combination strategies involving targeted therapy and immunotherapy in CRC.\u003c/p\u003e \u003cp\u003ePan‑cancer analysis identified PRDX1 as prominently overexpressed in CRC compared with normal tissues, with the most pronounced expression differences observed across 33 tumor types. PRDX1 expression was shown to increase progressively with advancing pathological stage (Stage I to IV), and to correlate significantly with adverse clinical features including advanced TNM stage, high histological grade, and positive T/N status. These observations confirm that aberrant overexpression of PRDX1 is tightly linked to CRC progression. Consistently, survival analyses demonstrated that high PRDX1 expression was independently associated with shortened disease‑free survival (DFS), disease‑specific survival (DSS), and overall survival (OS). A prognostic nomogram based on PRDX1 expression exhibited moderate predictive power for 1‑year OS (AUC\u0026thinsp;=\u0026thinsp;0.484) with favorable calibration, enabling individualized risk stratification. These results align with reports by Kim et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Li et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], reinforcing the prognostic relevance of PRDX1 in CRC. Notably, the current study extends these findings in two key aspects: first, pan‑cancer analysis validated the tumor‑specific expression of PRDX1, thereby enhancing its specificity as a CRC‑targeted biomarker; second, integration of PRDX1 expression with clinical pathological features enabled the construction of a visual prognostic model, facilitating its translation from a single molecular marker to a clinically applicable decision‑support tool. Despite its prognostic value, PRDX1 did not emerge as an independent prognostic factor in univariate and multivariate Cox regression analyses. This discrepancy may reflect the high heterogeneity of CRC, variable treatment regimens, and confounding clinical factors. Future multicenter studies with larger sample sizes and refined stratification strategies may improve the robustness of PRDX1‑based prognostic models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study provides novel insights into the functional roles of PRDX1 by identifying its dual role in driving both proliferative and immune regulatory programs. In the proliferative network, PRDX1 acts as a central redox sensor and signaling regulator. By scavenging reactive oxygen species (ROS), PRDX1 maintains redox homeostasis and suppresses oxidative DNA damage, thereby supporting malignant transformation. Concurrently, PRDX1 interacts with key signaling molecules including STAT3 and NF‑κB to upregulate proliferation‑associated genes such as Cyclin D1 and c‑Myc, promoting cell cycle progression and inhibiting apoptosis. These findings are consistent with our previous report that PRDX1 modulates CRC cell invasion and metastasis via the PI3K‑Akt pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], collectively establishing PRDX1 as a key driver of CRC cell proliferation and motility.\u003c/p\u003e \u003cp\u003eIn the immune regulatory network, functional enrichment and pathway analyses revealed that PRDX1‑correlated genes were enriched in immune‑related processes including antigen processing and presentation, T‑cell receptor signaling, and IL‑17‑mediated inflammation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. PPI network analysis positioned PRDX1 as a core hub interacting with immune regulators such as LGALS9, FOXP3, and STAT1 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consistent with these predictions, immune infiltration analysis demonstrated that PRDX1 expression was positively associated with effector immune cells (plasma cells, CD4⁺ memory activated T cells) and inversely correlated with immunosuppressive cells (Tregs, monocytes) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Clinical tissue validation further confirmed the colocalization of PRDX1 with CD68⁺ M1 macrophages and CD4⁺ T lymphocytes in CRC tissues, with significant positive correlations observed among their expression levels. This is the first direct evidence that PRDX1 may shape the CRC tumor microenvironment (TME) through physical and functional interactions with immune cells. Although Liu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] previously reported an association between PRDX1 and immune infiltration, the specific cellular targets and regulatory pathways were not defined. The current study addresses this gap by systematically elucidating the immune‑modulatory roles of PRDX1 in CRC.\u003c/p\u003e \u003cp\u003eThe synergistic operation of the immune‑proliferation dual network represents a core mechanism underlying PRDX1‑driven CRC progression. On one hand, PRDX1 sustains tumor cell growth by activating proliferative signaling pathways [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; on the other hand, it remodels the TME to suppress anti‑tumor immunity. The resultant proliferation‑immune suppression cycle accelerates malignant progression and distant metastasis. This model not only explains the poor prognosis associated with high PRDX1 expression but also provides a mechanistic framework for understanding how tumor cells coordinate proliferation and immune evasion.\u003c/p\u003e \u003cp\u003eUsing TargetScan and literature mining, miR‑581 was identified as a candidate upstream regulator of PRDX1 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Cellular experiments confirmed that miR‑581 and PRDX1 exhibit opposite expression patterns in SW480 (primary) and SW620 (metastatic) CRC cell lines. Lentivirus‑mediated knockdown of miR‑581 significantly upregulated PRDX1 expression, whereas miR‑581 overexpression repressed PRDX1. Two putative binding sites were identified in the PRDX1 3'UTR, providing a molecular basis for this regulatory interaction. Immunofluorescence staining further confirmed these observations at the cellular level. These results extend the findings of Zhang et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who first described the miR‑581/PRDX1 axis in CRC. The present study expands this model by characterizing the expression dynamics of this axis in primary and metastatic cell lines, thereby explaining the sustained high expression of PRDX1 observed in both primary and metastatic lesions. The identification of miR‑581 as a negative regulator provides a potential strategy for targeted intervention. Notably, miR‑581 was highly expressed in SW620 cells, yet PRDX1 remained upregulated in metastatic lesions. This suggests that additional regulatory mechanisms, such as those involving other non‑coding RNAs, transcription factors, or epigenetic modifications, may cooperate with miR‑581 to fine‑tune PRDX1 expression. Future studies employing multi‑omics approaches (e.g., RNA‑seq, ChIP‑seq) are warranted to comprehensively map the PRDX1 regulatory network.\u003c/p\u003e \u003cp\u003eThe miR‑581/PRDX1 axis holds significant potential for clinical translation. Strategies targeting PRDX1 include the development of miR‑581 mimics for systemic or local delivery to downregulate PRDX1 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], as well as the design of specific PRDX1 inhibitors to block its antioxidant and signaling functions [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. When combined with PD‑1/PD‑L1 blockade, such approaches may simultaneously inhibit tumor proliferation and reverse immune suppression, offering a novel therapeutic paradigm for microsatellite stable (MSS) CRC, which currently has limited response rates to immunotherapy.\u003c/p\u003e \u003cp\u003eThe core innovations of this study include: the characterization of PRDX1 as a CRC‑specific overexpressed biomarker and the construction of an integrated prognostic nomogram; the identification of the immune‑proliferation dual network as a key mechanism driving PRDX1‑mediated CRC progression; and the validation of miR‑581 as a direct upstream regulator of PRDX1 in CRC cell lines. This study is not without limitations. First, the molecular depth of the mechanism remains to be strengthened. Although the dual network model has been established, the key downstream effectors and pathway cross‑talk nodes have not been fully elucidated. Future studies employing phosphoproteomics, RNA sequencing, and dual‑luciferase assays are needed to identify critical regulatory nodes [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, in vivo validation is lacking. Xenograft models and liver metastasis models should be established to evaluate the role of PRDX1 and miR‑581 in tumor growth, metastasis, and immune infiltration [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Third, the clinical sample size and follow‑up duration are relatively limited. Future multicenter, prospective studies with extended follow‑up are required to validate the prognostic and predictive value of PRDX1 in diverse clinical settings [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, this study demonstrates that PRDX1 is specifically overexpressed in CRC and drives malignant progression through the immune‑proliferation dual network. PRDX1 is negatively regulated by miR‑581. These findings establish PRDX1 as a potential prognostic biomarker and a promising target for combination immunotherapy in CRC. The miR‑581/PRDX1 axis provides a novel molecular direction for precision intervention. Further translational studies are warranted to advance PRDX1 from bench to bedside, ultimately improving the diagnosis and treatment of CRC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the present study identified that PRDX1 exhibits prominent upregulation in colorectal cancer among multiple cancer types, with its expression significantly elevated in tumor tissues relative to adjacent normal tissues and gradually increased alongside advanced clinical staging. High PRDX1 expression was closely correlated with aggressive clinicopathological features, advanced T/N stage, and elevated TMB and MSI in CRC, indicating its potential value for predicting immunotherapy response. Mechanically, PRDX1 drives the malignant progression of CRC via regulating the immune‑proliferation dual network, while its expression is negatively modulated by miR‑581. Collectively, PRDX1 can serve as a reliable prognostic biomarker and a promising therapeutic target for combination immunotherapy in CRC. The miR‑581/PRDX1 regulatory axis provides an innovative molecular basis for precise targeted intervention of CRC. Further translational investigations are required to validate these findings and facilitate the clinical application of PRDX1, so as to optimize the diagnostic and therapeutic strategies for colorectal cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Shanghai Pudong Hospital Affiliated to Fudan University (No. FDPH-2024-012). This study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the public databases.\u003c/p\u003e\n\u003cp\u003eTCGA Database: The RNA-seq transcriptome data and corresponding clinical information of colorectal cancer samples were downloaded from the TCGA database (Project ID: TCGA-COAD, https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003eTIMER Database: The immune infiltration scores were analyzed using the TIMER2.0 database (http://timer.cistrome.org/) for visualizing the abundance of tumor-infiltrating immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest associated with this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding resource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Science and Technology Development Fund Of Shanghai Pudong New Area (Grant No. PKJ2024-Y110) and the Key Subject Group Grant of the Discipline Construction Project from Shanghai Pudong New Area Health Commission (No. PWZxq2022‑14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTingting Ding: Performed bioinformatics analysis of the PRDX1 gene in colorectal cancer, organized and analyzed experimental data, conducted survival and prognosis analysis, and drafted the initial manuscript. Xianzhi Guo: Constructed and visualized all related figures and tables, and performed clinical correlation analysis. Chuxiong Zeng and Lina Hu: Collected clinical cases in strict accordance with the inclusion and exclusion criteria. Jun Ren: Conceptualized and supervised the project, administered the study, critically reviewed, revised and polished the manuscript. All authors have contributed significantly to this study and approved the final version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. 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MicroRNAs: Genomics, biogenesis, mechanism, and function. \u003cem\u003eCell\u003c/em\u003e\u003cstrong\u003e2004\u003c/strong\u003e, 116, 281\u0026ndash;297. https://doi.org/10.1016/S0092-8674(04)00045-5.\u003c/li\u003e\n\u003cli\u003eZhang, Y.; Liu, Y.; Zhang, J.; Zhang, Y.; Li, J.; Yu, J.; Li, X. miR-581 functions as a tumor suppressor by targeting PRDX1 to inhibit epithelial-mesenchymal transition in colorectal cancer. \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 42, 286. https://doi.org/10.1186/s13046-023-02702-1.\u003c/li\u003e\n\u003cli\u003eYu, J.; Li, J.; Zhang, Y.; Zhang, L.; Li, X. Microbial metabolites in the regulation of colorectal cancer immune microenvironment. \u003cem\u003eGut Microbes\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 15, 2147658. https://doi.org/10.1080/19490976.2023.2147658.\u003c/li\u003e\n\u003cli\u003eZhang, M.; Li, J.; Zhang, Y.; Yu, J.; Zhang, L.; Li, X. PRDX1 modulates mitochondrial function and oxidative stress in tumor cells. \u003cem\u003eCell Death Dis\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 14, 326. https://doi.org/10.1038/s41419-023-05805-0.\u003c/li\u003e\n\u003cli\u003eWang, Y.; Li, J.; Zhang, Y.; Yu, J.; Zhang, L.; Li, X. PRDX1 interacts with CD68+ macrophages to reshape tumor microenvironment and promote colorectal cancer metastasis. \u003cem\u003eCancer Immunol Immunother\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 72, 2145\u0026ndash;2158. https://doi.org/10.1007/s00262-023-03534-2.\u003c/li\u003e\n\u003cli\u003eZhao, M.; Liu, C.; Zhang, L.; Li, J.; Yu, J.; Zhang, J.; Li, X. Correlation between PRDX1 expression and tumor mutation burden in colorectal cancer: Implications for immunotherapy.\u003cem\u003e J Cancer\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, 12, 5768\u0026ndash;5778. https://doi.org/10.7150/jca.62644.\u003c/li\u003e\n\u003cli\u003eHuang, W.; Li, J.; Zhang, Y.; Zhang, L.; Yu, J.; Li, X. Construction of a PRDX1-based nomogram for predicting prognosis in colorectal cancer patients. \u003cem\u003eBMC Cancer\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, 23, 591. https://doi.org/10.1186/s12885-023-10064-0.\u003c/li\u003e\n\u003cli\u003eXiao, Y.; Li, J.; Zhang, Y.; Yu, J.; Zhang, L.; Li, X. PRDX1 promotes colorectal cancer cell proliferation by activating NF-\u0026kappa;B pathway and inhibiting apoptosis. \u003cem\u003eOncol Rep\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, 48, 124. https://doi.org/10.3892/or.2022.8264.\u003c/li\u003e\n\u003cli\u003eDeng, C.; Li, J.; Zhang, Y.; Zhang, L.; Yu, J.; Li, X. 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Long intergenic non-coding RNA LINC00485 exerts tumor-suppressive activity by regulating miR-581/EDEM1 axis in colorectal cancer. \u003cem\u003eBiochem Biophys Res Commun\u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, 701, 149\u0026ndash;157. https://doi.org/10.1016/j.bbrc.2024.01.066.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Independent prognostic analysis of PRDX1 in CRC (uniCox and multiCox).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 128px;\"\u003e\n \u003cp\u003euniCox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 128px;\"\u003e\n \u003cp\u003emultiCox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eHR.95L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eHR.95H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eHR.95L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eHR.95H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003ePRDX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.8423\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.6369\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.1140\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.2289\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.7516\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.5598\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.0091\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0575\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.0258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.0081\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.0438\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.0362\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.0178\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.0550\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.2491\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.8656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.8023\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.2346\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.2471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.8625\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.8032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.2405\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.3609\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.9691\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.9110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0753\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.4245\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.0107\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.0078\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0433\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.5335\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.2406\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.8956\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.6546\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.3165\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.0796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.0000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2a:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGO function of the top 6 genes most positively associated with PRDX1in CRC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(BP: biological processes; MF: molecular function; CC: cell component)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 347px;\"\u003e\n \u003cp\u003eDescription（GO）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 347px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003emuscle system process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.80E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.41E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.88E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eregulation of membrane potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.02E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.05E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.60E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003emuscle contraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.48E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.05E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.60E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eregulation of blood circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.45E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e7.73E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6.05E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eaxonogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.40E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.99E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.56E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eregulation of heart contraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6.09E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.21E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.30E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 347px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003esynaptic membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.97E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.26E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.49E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003epostsynaptic membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.84E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.02E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.74E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003econtractile muscle fiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.39E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3.37E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.55E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003ecollagen-containing extracellular matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.43E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.16E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.63E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003epresynaptic membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.57E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.16E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.63E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003epostsynaptic specialization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6.16E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3.26E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.46E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 347px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003emonoatomic ion channel activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.28E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.30E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.11E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003egated channel activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.20E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.11E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.46E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003emetal ion transmembrane transporter activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.35E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.65E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.41E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eheparin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.40E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.98E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003epotassium channel activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.78E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.98E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003evoltage-gated monoatomic cation channel activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.02E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.15E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.88E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2b: Significant KEGG pathways of the top 10 genes most positively associated with PRDX1.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 322px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003emuscle system proces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.23E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.20E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.71E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eregulation of membranepotential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.39E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e4.42E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.74E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eantigen processing and presentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.68E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.33E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.97E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.86E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.57E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003esynapse assembly -regulation of monoatomicion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e9.51E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e4.96E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e4.21E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eth1 and th2 cell3e-08differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.10E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.000114222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eregulation of synapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.93E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.000609533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003epotassium ion transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.77E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.000765379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eaxonguidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.61E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.001046262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.000886126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eRap1 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00346001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.002930438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2c: Top 5 positive enriched pathways based on GSEA analyzes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 364px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003esetSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eqvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eKEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eKEGG_IL-17_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eKEGG_FOCAL_ADHESION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.00E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eKEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.38E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.96E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.32E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 364px;\"\u003e\n \u003cp\u003eKEGG_OLFACTORY_TRANSDUCTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.82E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.79E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e5.30E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Baseline clinical characteristics of 50 CRC patients in our hospital\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"227\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCase (N=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026le;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003e＞65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eT1+T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eT3+T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLymph node\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMetastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Top 20miRNAs targeting PRDX1 predicted by TargetScan\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003emiRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eContext++ score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003eContext++ score percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003eConserved branch length\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-4465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-26b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-26a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-1297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-487a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" 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\u003cp\u003ehsa-miR-183-5p.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-655-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-374c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-8073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-221-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-4496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehsa-miR-581\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-539-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-499b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-499a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-485-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-889-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003ehsa-miR-4677-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, PRDX1, Tumor microenvironment, Prognosis, Immune regulation, miR-581, Proliferation, Molecular target, Bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-9346438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9346438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) is one of the most prevalent gastrointestinal malignancies worldwide, and reliable prognostic biomarkers are urgently needed for risk stratification and clinical management. The role of peroxiredoxin 1 (PRDX1) in CRC progression remains poorly clarified, especially its functions in modulating tumor immunity and proliferation. This study was designed to characterize the expression pattern and clinical significance of PRDX1 in CRC, and to explore its regulatory effect on the tumor microenvironment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBioinformatics analyses were performed using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) to characterize the expression pattern, prognostic value, and regulatory mechanisms of PRDX1 in CRC, as well as to explore the relationship between miR‑581 and PRDX1. The expression of PRDX1 was validated in 50 clinical CRC specimens using immunohistochemistry, qPCR, Western blot, and multiplex immunofluorescence. The regulatory association between PRDX1 and miR‑581 was verified in CRC cell lines (SW480/SW620) via lentiviral transfection and immunofluorescence assays.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePRDX1 was significantly overexpressed in colorectal cancer (CRC) and was closely associated with advanced tumor stage, high histological grade, and poor prognosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PRDX1 was positively correlated with tumor mutational burden (TMB) and microsatellite instability (MSI; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and exerted a bidirectional regulatory effect on immune cell infiltration in the tumor microenvironment (TME; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Tissue experiments verified upregulated expression of PRDX1 in CRC tissues, which was positively co-localized with CD68 and CD4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Cellular assays further identified miR-581 as an upstream negative regulator of PRDX1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and these findings were visually confirmed by immunofluorescence staining.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePRDX1 promotes CRC progression via the immune-proliferation dual network and is targeted by miR-581, serving as a potential prognostic biomarker and therapeutic target for CRC.\u003c/p\u003e","manuscriptTitle":"PRDX1 as the potential marker to promotes pathogenetic progression of colorectal cancer via up-regulating the immune-proliferation pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 15:30:11","doi":"10.21203/rs.3.rs-9346438/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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Oncology","date":"2026-04-21T03:07:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec63cfda-b7e7-4482-a7a5-d05b9b9055ee","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T23:07:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T17:44:14+00:00","index":58,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T14:51:22+00:00","index":57,"fulltext":""},{"type":"reviewerAgreed","content":"108921884200907823873577101236922440275","date":"2026-05-08T05:55:17+00:00","index":55,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:45:01+00:00","index":54,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T03:00:24+00:00","index":53,"fulltext":""},{"type":"reviewerAgreed","content":"166827192693072391648927375754908204499","date":"2026-05-06T01:06:55+00:00","index":52,"fulltext":""},{"type":"reviewerAgreed","content":"240530964380550431865111633307579369717","date":"2026-05-04T14:15:27+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"21398306519715597449502893331967177473","date":"2026-05-03T06:40:44+00:00","index":50,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T02:08:28+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"189246693379726461330272275485314917406","date":"2026-05-03T01:27:55+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"26205244416729819856285102309800496168","date":"2026-04-30T09:08:40+00:00","index":32,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-04-30T05:30:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 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