IFI30 remoulds immune microenvironment and macrophage function to promote colorectal cancer and atherosclerosis

IFI30 remoulds immune microenvironment and macrophage function to promote colorectal cancer and atherosclerosis | 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 IFI30 remoulds immune microenvironment and macrophage function to promote colorectal cancer and atherosclerosis Ming Gu, Dan Wu, Shaoyan Wang, Junfeng Yin, Weixiang Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7551279/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) and atherosclerosis (AS) are both chronic inflammatory diseases that may share immune-related genes (IRGs) and regulatory mechanisms. This study aimed to identify commonly dysregulated IRGs in CRC and AS, and to investigate their clinical significance and molecular functions. Methods Transcriptomic, single-cell RNA sequencing (scRNA-seq), and somatic mutation data from the TCGA-COAD and GEO databases were integrated to identify overlapping dysregulated IRGs in CRC and AS. Comprehensive analyses, including survival analysis, immune infiltration assessment (CIBERSORT, ssGSEA, ESTIMATE), functional enrichment, drug sensitivity prediction, and single-cell analysis, were conducted to evaluate the prognostic relevance and immunological role of the core gene IFI30 . The expression level of core genes was verified by staining pathological sections of stored files. Results A total of 102 IRGs were found to be commonly dysregulated in both CRC and AS. Among them, IFI30 was notably upregulated in both diseases, with its upregulation predicting poor prognosis in CRC (HR = 1.68, p < 0.05). Immune profiling revealed that elevated IFI30 expression was linked to higher immune scores and increased infiltration of macrophages and T cells. IFI30 expression also showed a positive linkage with genes in the interleukin, interferon, and TNF families. scRNA-seq indicated that IFI30 is predominantly expressed in macrophages, where it may promote M2 polarization by modulating oxidative phosphorylation and lipid metabolism pathways. Furthermore, high IFI30 expression was linked to reduced sensitivity to immunotherapy and certain chemotherapeutic agents, as well as increased mutation frequencies in genes such as KIF26A and TTN . Immunohistochemical experiments confirmed that the expression of IFI30 in colorectal cancer tissue and unstable carotid plaque increased significantly. Conclusion IFI30 is a critical immune regulatory gene commonly involved in both CRC and AS, with functional evidence pointing to its role in modulating macrophage-driven immune remodeling. These characteristics position IFI30 as a biomarker of clinical relevance and a candidate for future targeted therapies. These findings provide new insights into shared immunopathological mechanisms across chronic inflammatory diseases. Colorectal cancer Atherosclerosis Immune-related genes IFI30 Multiomic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Colorectal cancer (CRC) and atherosclerosis (AS) are widespread chronic conditions with significant impacts on global morbidity and mortality. Notably, CRC ranks third in incidence and second in cancer mortality worldwide [ 1 , 2 ]. AS, on the other hand, is a chronic and progressive vascular condition that often remains clinically silent for years before resulting in severe cardiovascular and cerebrovascular events, such as acute myocardial infarction and ischemic stroke—major causes of global disability-adjusted life years [ 3 ]. Recent evidence points to a potential link between CRC and AS. Subclinical AS, characterized by carotid intima-media thickness (CIMT ≥ 1 mm) or the presence of carotid plaques, has been linked to a markedly elevated occurrence of colorectal adenomas and high-risk adenomas, both of which are well-established precursors of CRC, when compared to individuals without atherosclerotic changes [ 4 ]. Moreover, a case-control study identified carotid AS as an independent risk determinant for CRC [ 5 ]. These findings suggest a possible convergence of pathogenic mechanisms underlying CRC and AS. A growing body of epidemiological evidence indicates that CRC and AS are influenced by overlapping risk profiles, notably including smoking, obesity, type 2 diabetes, and metabolic syndrome [ 6 ]. Smoking contributes to the altered immune microenvironment (IME) in both diseases by enhancing DNA mutagenesis and modulating macrophage activity [ 7 , 8 ]. Metabolic syndrome—particularly obesity-associated dyslipidemia—not only accelerates endothelial dysfunction but also facilitates CRC cell proliferation, invasion, and metastasis [ 9 , 10 ]. Notably, statins, widely used for lipid control, have demonstrated protective effects against both CRC and AS [ 11 ]. Growing evidence supports a bidirectional relationship between CRC and AS, potentially driven by common pathogenic mechanisms. Chronic inflammation serves as a pivotal link between the two diseases. In AS, inflammatory mediators such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) promote the development of atherosclerotic plaques. Similarly, these cytokines are implicated in colorectal adenoma progression and carcinogenesis [ 12 ]. The chemopreventive effects of nonsteroidal anti-inflammatory drugs on both conditions further underscore the therapeutic potential of targeting inflammation [ 13 ]. Immune cell infiltration also plays a central role in the pathophysiology of both CRC and AS. In CRC, the tumor microenvironment (TME) is characterized by diverse immune cell populations, with tumor-associated macrophages (TAMs) exerting significant influence on tumor progression and immune evasion [ 14 ]. TAMs display functional plasticity, adopting either a pro-inflammatory M1 or an immunosuppressive M2 phenotype in response to local cytokine cues. Numerous studies have reported that increased TAM infiltration correlates with poor prognosis and reduced survival in various cancers [ 15 ], although their specific roles in CRC remain under debate [ 16 ]. In metastatic CRC, the TME is often dominated by M2-like TAMs, which are activated by stromal gene programs and chemokine signaling—such as CXCL8—leading to enhanced angiogenesis, extracellular matrix (ECM) remodeling, and immunosuppression through the genration of TGF-β and IL-10 [ 17 , 18 ]. Macrophages are likewise key mediators in the initiation and progression of AS. Monocyte-derived macrophages migrate into the subendothelial space following endothelial dysfunction, where they engulf oxidized low-density lipoprotein via scavenger receptors, forming foam cells—a defining feature of early atherogenesis. The functional plasticity of macrophages further influences plaque stability: pro-inflammatory macrophages contribute to plaque vulnerability, whereas M2-like macrophages possess anti-inflammatory and reparative properties, though their protective effects are often diminished in advanced lesions [ 19 ]. IFI30, also known as GILT, is primarily localized in lysosomes and the cytoplasm, and is highly expressed in antigen-presenting cells, including bone marrow-derived dendritic cells (DCs), B cells (both progenitors and mature lineages), and monocytes/macrophages [ 20 ]. IFI30 is currently the only known enzyme that catalyzes disulfide bond reduction within the endocytic pathway, thereby facilitating both MHC class I-restricted cross-presentation and MHC class II-restricted antigen processing by reducing disulfide bonds in endocytosed proteins [ 21 ]. Additionally, IFI30 plays a pivotal role in modulating T cell autoreactivity, as it is capable of suppressing T cell proliferation and activation, thereby dampening immune responses and autoimmune potential [ 20 ]. Elevated expression of IFI30 has been observed in diverse pathological conditions, particularly in malignant tumors. Its overexpression is often associated with poor prognosis due to enhanced immune cell infiltration, and has been proposed as both a prognostic indicator and an immunotherapeutic target in multiple cancer types [ 22 , 23 ]. Emerging evidence has documented increased IFI30 expression in non-neoplastic diseases such as AS, where its upregulation correlates negatively with plaque stability [ 24 ]. These observations point to IFI30 as a possible molecular mediator connecting the pathophysiological processes of CRC and AS, warranting further investigation. Despite accumulating evidence indicating a clinical association between CRC and AS, the shared cellular and molecular mechanisms remain poorly defined. The rapid evolution of next-generation sequencing has positioned bioinformatics as an essential approach for dissecting molecular interactions and elucidating cross-disease mechanisms. In this study, we employed integrative bioinformatics approaches to identify shared immune-related genes (IRGs) involved in both CRC and AS. We then focused on the hub gene IFI30, assessing its relationship with the IME, chemotherapy sensitivity, somatic mutations, and response to immunotherapy in CRC. Our aim was to elucidate potential pathophysiological mechanisms linking these diseases and to identify promising therapeutic targets for both conditions. 2 Materials and methods 2.1 Data sources Transcriptomic profiles, somatic mutation data, and clinical information for 483 colorectal adenocarcinoma (COAD) samples and 41 adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA-COAD) project. For survival analysis and model construction, samples lacking complete survival data or with overall survival (OS) less than 30 days were excluded. Transcriptomic data for AS were retrieved from the Gene Expression Omnibus (GEO) database under accession number GSE43292, which includes 32 carotid atherosclerotic plaque samples and 32 matched normal intima/media samples from distal carotid arteries. A total of 2,498 IRGs were downloaded from the ImmPort database ( https://immport.org/shared/ ). 2.2 Single-Cell RNA Sequencing (scRNA-seq) Analysis Single-cell RNA-seq datasets were obtained from GEO under accession number GSE231559, including samples from 3 adjacent normal tissues and 6 CRC tissues, as well as 3 atherosclerotic plaque samples and 3 paired normal carotid tissues. Data preprocessing and analysis were carried out in R utilizing the Seurat workflow. Cells with low gene counts ( 20%) were excluded to ensure quality. To correct for batch-related variability, the Harmony algorithm was applied. Subsequently, the 2,000 most variable genes were selected via the FindVariableFeatures function. Principal component analysis (PCA) facilitated feature space reduction, and key marker genes for each cluster were determined utilizing the FindMarkers function under default configurations. Cell subpopulations were annotated with the SingleR package. 2.3 Survival analysis OS was selected as the primary clinical endpoint. To investigate the prognostic relevance of candidate genes in CRC, univariate Cox proportional hazards modeling was applied. Based on the median expression the gene of interest, patients were classified into two groups: high and low expression. Kaplan-Meier analysis was implemented to visualize survival distributions, and statistical differences between the groups were tested utilizing the log-rank test. 2.4 Functional Enrichment Analysis Gene Ontology (GO) enrichment analysis, including Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was conducted to explore the biological functions and pathways associated with gene sets. Enrichment analyses were completed utilizing the ClusterProfiler package in R. Terms with a p -value < 0.05 and an adjusted p -value < 0.1 were considered statistically significant. 2.5 Immune infiltration analysis To characterize immune cell infiltration in CRC samples, three computational methods (CIBERSORT, single-sample gene set enrichment analysis [ssGSEA], and ESTIMATE) were employed. For the CIBERSORT algorithm, normalized gene expression matrices were analyzed with the help of the R package CIBERSORT to estimate the relative proportions of 22 immune cell types. ssGSEA was implemented utilizing the GSVA package in R to assess the enrichment of immune cell signatures at the individual sample level. The ESTIMATE package was utilized to compute StromalScore, ImmuneScore, and ESTIMATEScore, thereby quantifying stromal and immune cell components in the TME. 2.6 Bulk RNA-Seq differential expression analysis Differential expression analysis was conducted utilizing the limma package (version 3.40.6) in R, which applies linear models suited for microarray and RNA-seq data. For CRC datasets, genes were deemed differentially expressed if they met the criteria of |log2 fold change [FC]| > 1 (i.e., fold change > 2) and adjusted p 1.5 and adjusted p < 0.05. Results were visualized employing the ggplot2 package in R, generating volcano plots and heatmaps to illustrate gene expression differences between groups. 2.7 Somatic mutation analysis Somatic mutation data were analyzed utilizing the maftools package in R. Mutation Annotation Format (MAF) files were imported via the read.maf function to construct MAF objects. Key mutation statistics, including tumor mutation burden, mutation types, and frequently mutated genes, were extracted using functions such as getSampleSummary and getGeneSummary. Mutation landscapes were visualized using oncoplots to display the frequency and types of gene alterations. Comparative analysis was implemented to test differences in mutation frequencies between groups. 2.8 Drug sensitivity analysis To assess immunotherapy response, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied. Higher TIDE scores indicate reduced sensitivity to immune checkpoint blockade therapy. For chemotherapy drug sensitivity, the oncoPredict package in R was used to predict responses based on TCGA-COAD gene expression data. The calcPhenotype function was employed to apply trained models from the GDSC database and compute drug sensitivity scores (Drug Sensitivity Score, DSS) for each tumor sample. 2.9 Hematoxylin and eosin (H&E) staining Tissue specimens were obtained from the hospital pathology archives, including 10 cases each of histologically confirmed stable and unstable carotid atherosclerotic plaques, as well as 10 CRC tissues and their matched adjacent normal tissues. Samples were fixed in 4% paraformaldehyde and paraffin-embedded. Sections of 4–5 µm thickness were deparaffinized, rehydrated, and washed prior to H&E staining according to standard protocols. 2.10 Immunohistochemistry (IHC) Tissue specimens were initially preserved in 10% neutral-buffered formalin for a minimum of 24 hours, followed by dehydration through a graded ethanol series (75, 95, 95, 100, 100%) and embedding in paraffin after triple xylene treatment. Paraffin blocks were prepared to sections of 5 µm thickness using a microtome, which were expanded on a 40°C water bath, transferred to glass slides, and baked at 60°C for 30 minutes. For antigen retrieval, slides underwent deparaffinization with xylene (three changes) and were sequentially rehydrated utilizing descending concentrations of ethanol (100%, 90%, 80%, 70%, and distilled water), then treated in antigen retrieval solution at 96°C. Endogenous peroxidase activity was quenched following treatment with hydrogen peroxide. Sections were incubated sequentially with primary antibody (anti-COX20) and corresponding secondary antibody. Streptavidin-peroxidase solution was applied, followed by development with DAB chromogen and rinsing with distilled water. Slides were counterstained with hematoxylin for 1–2 minutes, differentiated in 1% acid-alcohol for 3 seconds, and rinsed again. After dehydration, slides were mounted and imaged. 2.11 Statistical analysis For comparisons between two groups, either the unpaired t-test or the Mann-Whitney U test was used, depending on data distribution. For comparisons among more than two groups, the Kruskal-Wallis test was employed. Correlation analyses were implemented utilizing Spearman's rank correlation. Survival data were analyzed utilizing Kaplan-Meier survival curves with log-rank tests and univariate Cox regression. All statistical analyses were made by virtue of R software. A P value < 0.05 was deemed statistical significance ( P < 0.05, P < 0.005, P < 0.001, ns = not significant). 3 Results 3.1 Identification of commonly dysregulated IRGs in CRC and AS Figure 1 shows the flow chart of this study. Differential expression analysis revealed that, compared with adjacent normal tissues, a total of 14,622 genes (10,692 upregulated and 3,930 downregulated genes) were dysregulated in CRC tissues (Fig. 2 A). In AS tissues, 1,324 genes were identified as differentially expressed relative to control vascular tissues, with 734 upregulated and 590 downregulated (Fig. 2 B). By intersecting the dysregulated genes from CRC (14,622) and AS (1,324) with a list of 2,498 IRGs obtained from the ImmPort database, 102 overlapping genes were identified. These were considered commonly dysregulated IRGs in both CRC and AS. GO-BP enrichment analysis demonstrated that these 102 genes were mainly implicated in BP categories such as leukocyte migration, production of molecular mediators of the immune response, positive regulation of the MAPK cascade, ERK1/ERK2 cascade regulation, cell chemotaxis, myeloid leukocyte migration, and immunoglobulin production (Fig. 3 A). GO-MF analysis indicated enrichment in signaling receptor activator activity, receptor-ligand activity, antigen binding, cytokine activity, cytokine receptor binding, G protein-coupled receptor binding, immunoglobulin receptor binding, and chemokine receptor binding (Fig. 3 B). GO-CC analysis revealed that these genes were primarily located in the immunoglobulin complex, the external side of the plasma membrane, membrane microdomains and rafts, tertiary granules, circulating immunoglobulin complexes, blood microparticles, and specific granule membranes (Fig. 3 C). KEGG pathway enrichment further indicated involvement in cytokine-cytokine receptor interaction, MAPK signaling pathway, PI3K-Akt signaling, Ras signaling, lipid and AS pathway, Rap1 signaling, NF-κB signaling, HIF-1 signaling, Toll-like receptor (TLR) signaling, and EGFR tyrosine kinase inhibitor resistance (Fig. 3 D). 3.2 IFI30 as a core gene shared by CRC and AS and its association with prognosis in CRC Univariate Cox regression analysis identified 11 genes among the 102 shared IRGs that were distinctly linked to OS in CRC patients, including CCL19 , FABP4 , BDNF , IGF1 , IGLV6-57 , IFI30 , CD79A , IGKV1D-42 , IGKV1D-33 , SEMA3E , and LTBP4 . Elevated expression of these genes was correlated with poorer prognosis (Fig. 4 A, P < 0.05). Kaplan-Meier survival analysis further demonstrated that only IFI30 and FABP4 were distinctly linked to reduced OS when highly expressed (Fig. 4 B, P < 0.05), while the other genes did not show statistically significant survival differences (Fig. 4 B). Comparative expression analysis revealed that FABP4 was downregulated in CRC tissues but upregulated in AS samples compared to their respective controls (Figs. 4 C- 4 D, P < 0.05). In contrast, IFI30 was notably upregulated in both CRC and AS tissues (Figs. 4 C- 4 D, P < 0.05). The evidence points to IFI30 as a converging immunological factor potentially driving disease mechanisms in both CRC and AS. 3.3 Association between IFI30 expression and the IME in CRC ESTIMATE algorithm analysis demonstrated that higher IFI30 expression levels were positively correlated with increased ESTIMATE Score (Cor = 0.539), Stromal Score (Cor = 0.447), and Immune Score (Cor = 0.560), suggesting enhanced immune and stromal components in the TME (Fig. 5 A). ssGSEA analysis revealed a strong positive correlation between IFI30 expression and the enrichment levels of multiple immune cell populations, including macrophages, Th1 cells, cytotoxic cells, effector memory T cells (Tem), neutrophils, activated DCs (aDCs), total T cells, DCs, regulatory T cells (Tregs), immature DCs (iDCs), T helper cells, NK CD56 dim cells, B cells, T follicular helper cells (TFH), mast cells, eosinophils, CD8 + T cells, plasmacytoid DCs (pDCs), NK cells, and central memory T cells (Tcm). In contrast, a negative correlation was observed with Th17 cell infiltration (Fig. 5 B, P < 0.05). CIBERSORT analysis further revealed a positive relationship between IFI30 expression and the abundance of M1 and M2 macrophages, follicular helper T cells, neutrophils, resting mast cells, and activated memory CD4 + T cells. Conversely, negative associations were observed with activated mast cells, activated DCs, monocytes, and resting memory CD4 + T cells (Fig. 5 C, P < 0.05). Next, we evaluated the expression differences of key immune-modulatory genes in the interleukin, interferon, and TNF families between the two established groups in CRC samples. The high IFI30 group exhibited notably upregulated expression of the majority of interleukin family genes relative to the low expression group (Fig. 5 D, P < 0.05). Similarly, in the interferon family, IFNE and IFNG were markedly elevated in the IFI30-high group (Fig. 5 E, P < 0.05). For the TNF superfamily, most members were evidently upregulated in the high IFI30 expression group (Fig. 5 F, P < 0.05). Collectively, IFI30 is strongly associated with an activated IME in CRC. 3.4 IFI30 expression shows linkage with therapeutic sensitivity in CRC Analysis using the TIDE algorithm showed that CRC patients with high IFI30 expression had evidently higher TIDE scores relative to those with low one, suggesting a reduced likelihood of response to immune checkpoint blockade therapy (Fig. 6 A, P < 0.05). In terms of chemotherapy response, patients in the high IFI30 expression group demonstrated lower predicted sensitivity to multiple chemotherapeutic agents, including Sepantronium bromide, Luminespib, Dactolisib, Foretinib, Mirin, Alisertib, Dactinomycin, Vinorelbine, and Vincristine (Fig. 6 B, P < 0.05). 3.5 Association of IFI30 expression with somatic mutations in CRC Somatic mutation analysis based on data from 428 CRC samples revealed that APC mutations were the most prevalent, observed in 69% of cases, predominantly as multi-hit mutations. TP53 mutations were the second most frequent (53%), primarily missense variants, followed by TTN mutations (45%), also mainly missense (Fig. 7 A and 7 B). Comparative mutation profiling between IFI30 high- and low-expression groups showed that the high-expression group exhibited a higher frequency of mutations in genes including KIF26A , RDX , ZNF518A , ERBB4 , MTA1 , KMT2D , DDX27 , TNRC6C , SEMA6C , AKAP13 , FCGBP , TRRAP , ELP1 , GRIN3A , TTN , GGCX , IFT122 , TAS1R3 , TMEM104 , FAT1 , NPHP4 , PIK3C2B , ZNF236 , ROBO3 , SORBS2 , MYCBP2 , and KCNQ5 (Fig. 7 C and 7 D). 3.6 Single-cell transcriptomic analysis reveals IFI30 expression in CRC and AS Bulk RNA-seq analysis indicated that IFI30 expression showed a positive linkage with an active IME in CRC. However, its elevated expression also correlated with poor prognosis, prompting further investigation at the single-cell level. In CRC, scRNA-seq identified 19 distinct cell clusters (Fig. 8 A), which were annotated into 9 major cell types: monocytes, T cells, smooth muscle cells, epithelial cells, B cells, endothelial cells, macrophages, tissue stem cells, and NK cells (Fig. 8 B and 8 C). IFI30 expression was predominantly localized in monocytes and macrophages, with remarkably higher levels in tumor samples relative to controls (Figs. 8 D and 8 E). Given the well-documented role of TAMs in cancer progression, we focused on IFI30 ’s role within this cell population. Macrophages were stratified into IFI30-positive and IFI30-negative groups, and differential gene expression followed by functional enrichment analysis was performed. Enriched pathways in IFI30-positive macrophages included oxidative phosphorylation and cholesterol metabolism (Figs. 8 G- 8 J), processes closely associated with M2 polarization. Similarly, scRNA-seq of atherosclerotic tissues revealed 23 cellular clusters (Fig. 9 A), categorized into 7 cell types: chondrocytes, smooth muscle cells, T cells, monocytes, endothelial cells, NK cells, and macrophages (Fig. 9 B and 9 C). IFI30 was again highly expressed in monocytes and macrophages, with expression markedly elevated in atherosclerotic plaques compared to normal vascular tissues (Figs. 9 D and 9 E). As in CRC, macrophages were subdivided based on IFI30 expression status, followed by differential expression and pathway enrichment analyses. IFI30-positive macrophages showed enrichment in pathways related to cytokine signaling, phagosome formation, lipid metabolism and AS, MAPK and NF-κB signaling, fluid shear stress, IL-17, and PPAR pathways (Figs. 9 G- 9 J). These pathways are known to play critical roles in AS progression. 3.7 IFI30 is upregulated in CRC and atherosclerotic lesions To validate IFI30 expression at the tissue level, H&E staining was first used to localize pathological regions in CRC and paired adjacent normal tissues, as well as in stable versus unstable carotid plaques (Fig. 10 ). IHC staining was then performed to quantify IFI30 protein expression. Results showed notably higher IFI30 expression in unstable atherosclerotic plaques compared to stable plaques. Similarly, IFI30 was markedly overexpressed in CRC tissues relative to adjacent non-tumor tissues. These findings support the notion that IFI30 may promote disease progression in both CRC and AS (Fig. 11 ). 4 Discussion CRC has been increasingly reported to share pathophysiological links with AS, suggesting common risk factors and potentially overlapping molecular mechanisms. However, the key regulatory pathways connecting these two diseases remain largely undefined. In this study, we employed comprehensive bioinformatics analyses to uncover shared pathological immune mechanisms between CRC and AS. Functional enrichment of 102 co-dysregulated IRGs revealed a strong association with immune cell infiltration—a hallmark of both tumor progression and vascular inflammation. Tumor-infiltrating leukocytes (TILs) play essential immunomodulatory roles within the TME, contributing to immune evasion and ultimately facilitating tumor progression [ 25 ]. Similarly, in early atherosclerotic plaque development, the infiltration of monocytes into the vascular intima is a critical initiating event [ 26 ]. Chemokine-receptor interactions orchestrate immune cell recruitment and communication in both contexts. For example, the CXCL1–CXCR2 axis modulates the infiltration of myeloid-derived suppressor cells (MDSCs) and cytotoxic T lymphocytes in the CRC tumor IME [ 27 ]. In AS, the CCL2-CCR2 axis promotes monocyte recruitment into atherosclerotic lesions, where they differentiate into macrophages that contribute to foam cell formation and perpetuate local inflammation [ 28 ]. Signal transduction pathways also play pivotal roles in disease progression. The MAPK/ERK signaling pathway regulates T cell activation and macrophage polarization in the TME, thereby influencing tumor initiation, metastasis, treatment response, and resistance [ 29 ]. In AS, this pathway drives vascular inflammation and smooth muscle cell proliferation, contributing to plaque development and instability [ 30 ]. Likewise, the NF-κB/TLR axis—a key regulator of inflammatory signaling—has been implicated in both CRC and AS, highlighting its dual role in mediating immune responses and chronic inflammation [ 27 , 31 ]. To pinpoint core genes linking CRC and AS, we integrated survival modeling, differential expression profiling, and prognostic validation. Through this comprehensive approach, IFI30 was identified as the central hub gene common to both conditions, and subsequent analyses focused on its biological and clinical significance. IFI30 encodes a lysosomal thiol reductase essential for antigen processing and immune modulation. Accumulating evidence suggests that this gene contributes to tumor biology, with its expression markedly elevated across multiple malignancies, notably CRC. Pan-cancer bioinformatics analyses have shown elevated IFI30 transcript levels in CRC tissues, which correlate with poor patient outcomes. For example, data from the Pathology Atlas—which contains over five million IHC images across multiple tumor types—revealed that IFI30 exhibits strong cytoplasmic staining in 60–78% of CRC samples, with high expression levels associated with reduced 5-year survival [ 32 ]. In another study focusing on anoikis-related genes in CRC, IFI30 mRNA and protein expression were markedly increased in CRC cell lines such as HCT116 ( P < 0.01) and SW620 ( P < 0.001), compared to normal colonic epithelial cells (NCM460). IHC analysis of patient samples further confirmed that tumor tissues exhibited significantly higher IFI30 expression than adjacent normal mucosa [ 33 ]. Our IHC findings are consistent with these observations. Quantitative analysis showed that the mean optical density (MOD) of IFI30 was significantly higher in CRC and unstable atherosclerotic plaques than in their respective controls. Additionally, unstable plaques showed thinner fibrous caps and elevated IFI30 expression. Application of the ESTIMATE algorithm demonstrated a strong positive correlation between IFI30 expression and TME metrics, including ESTIMATE (Cor = 0.539), stromal (Cor = 0.447), and immune scores (Cor = 0.560). Transcriptomic profiling further indicated that high IFI30 expression aligned with the upregulation of numerous cytokine-related genes, particularly within the interleukin and TNF superfamilies. Within the interferon family, IFNE and IFNG levels were notably elevated in the high IFI30 group. These results collectively uncover that IFI30 expression is linked to heightened immune activity in CRC. However, immune checkpoint response prediction using the TIDE algorithm demonstrated that the IFI30 high-expression group had distinctly higher TIDE scores, implying reduced sensitivity to immunotherapy. This apparent contradiction prompted further investigation into the nature of immune cell infiltration. Multi-algorithm analysis, including CIBERSORT and ssGSEA, demonstrated that IFI30 expression was notably correlated with increased infiltration of macrophages—particularly the M2 subtype. These findings were corroborated by scRNA-seq data, which consistently showed that IFI30 is primarily expressed in monocytes and macrophages in both CRC and AS. In disease samples, IFI30 expression was markedly upregulated compared to normal controls. Stratification based on IFI30 expression revealed that macrophage-associated pathways were significantly enriched in both conditions. These results strongly suggest that IFI30 may drive disease progression in CRC and AS through macrophage-mediated immunomodulation. TAMs, comprising both M1-like and M2-like subsets, are a prominent component of the tumor-infiltrating immune cell population. TAMs and tumor cells engage in a mutually influential interplay that orchestrates changes in both immune regulation and tumor progression pathways. TAMs secrete various immunosuppressive molecules—such as TGF-β, IL-10, and nitric oxide (NO)—which suppress anti-tumor immune responses and facilitate tumor cell proliferation [ 34 ]. In addition to promoting tumor growth, TAMs also modulate the TME in ways that impair the effectiveness of chemotherapy, targeted therapy, and immunotherapy [ 35 , 36 ]. A key mechanism through which TAMs support tumor progression is the induction of immune evasion. A recent study investigating TAMs in CRC demonstrated that macrophage infiltration suppresses CD8 + T cell activity via CCL5-mediated upregulation of PD-L1 on tumor cells, thereby promoting CRC progression [ 37 ]. In this study, exposure of macrophages with poly(I:C) and LPS led to increased CCL5 expression. Treatment with an anti-CCL5 antibody blocked the macrophage-driven increase in PD-L1 levels and its binding to PD-1 on HT29 cells. Co-culture assays demonstrated that macrophage presence markedly reduced T cell-mediated cytotoxicity, an effect that was reversed by anti-CCL5 treatment. Notably, when PD-L1 expression was silenced in HT29 cells, neither macrophage presence nor CCL5 blockade influenced T cell killing. Moreover, PD-L1 blockade in tumor cells not only decreased TAM infiltration but also enhanced the recruitment of cytotoxic CD8 + T lymphocytes [ 38 ]. These findings suggest the existence of a positive feedback loop in which macrophage-derived CCL5 upregulates PD-L1 expression in tumor cells, which in turn triggers further macrophage infiltration and immune escape. This loop ultimately contributes to CRC progression and immune resistance [ 37 ]. Somatic mutation analysis revealed that the most frequently mutated genes in CRC were APC , TP53 , and TTN , consistent with previous genomic studies [ 39 ]. To further investigate the relationship between gene mutations and IRG expression, we stratified CRC samples based on IFI30 expression levels. Comparative analysis showed that 27 genes had significantly higher mutation frequencies in the IFI30 high-expression group relative to the low-expression group. A subset of these genes has been documented to contribute to CRC progression and prognosis. For instance, in a study aiming to identify clinicopathologically specific driver mutations in CRC, ERBB4 mutations were found to cluster predominantly in male patients. Among male patients with microsatellite stable (MSS) CRC, those harboring ERBB4 mutations exhibited significantly poorer overall survival than those without such mutations [ 40 ]. Similarly, mutations in KMT2D were shown to be associated with persistent polyps and early-stage CRC detection, suggesting its potential utility as a genetic biomarker for early diagnosis [ 41 ]. High-frequency mutations in FCGBP have also been reported in CRC tissues [ 42 ]. Analysis of clinical datasets revealed that FCGBP mRNA and protein levels were consistently downregulated across all CRC stages compared to normal tissues. Furthermore, low FCGBP expression was linked to unfavorable survival outcomes. Functionally, FCGBP has been proposed to act as a tumor suppressor and to influence immune cell infiltration within the CRC microenvironment [ 42 , 43 ]. Chemotherapy remains a cornerstone of CRC treatment, having significantly improved patient survival through its cytotoxic effects. However, the emergence of chemoresistance continues to pose a substantial barrier to effective therapy, ultimately limiting treatment outcomes and adversely impacting prognosis [ 44 ]. In our study, increased resistance to various chemotherapies was observed in CRC cases characterized by high IFI30 expression, in contrast to their low-expressing counterparts. Specifically, the high-expression group showed decreased sensitivity to nine distinct chemotherapy drugs. Emerging evidence suggests that TAMs, recognized as central players in the TME, are key mediators of chemoresistance in CRC [ 45 , 46 ]. One study demonstrated that 5-FU induced activation of the HIF1α-HMGB1 axis in CRC cells, which promotes TAM infiltration. In turn, these macrophages secrete GDF15, a cytokine widely associated with chemoresistance across malignancies [ 47 ], leading to a marked increase in the 5-FU IC50 and enhanced colony formation in CRC cell lines [ 46 ]. In addition to immune cells, stromal components—particularly cancer-associated fibroblasts (CAFs)—also contribute to chemoresistance. CAFs remodel the ECM and promote epithelial-mesenchymal transition (EMT), thereby supporting tumor survival and therapeutic evasion [ 48 , 49 ]. In our analysis, the stromal score calculated via the ESTIMATE algorithm was remarkably elevated in the IFI30 high-expression group (Cor = 0.447), suggesting that stromal enrichment may underlie the observed chemoresistance. These findings imply that IFI30 may influence treatment response by shaping a pro-resistance stromal and IME. 5 Conclusion To our knowledge, this work represents the first integrative bioinformatics investigation aimed at delineating the shared molecular mechanisms between CRC and AS through integrative bioinformatics analysis. Our findings identify IFI30 as a key IRG that is upregulated in both conditions and associated with immune cell infiltration, stromal activity, somatic mutations, poor prognosis, and reduced therapeutic sensitivity. These insights lay a conceptual groundwork for future diagnostic innovations and therapeutic interventions targeting overlapping mechanisms in CRC and AS. Nonetheless, several limitations should be mentioned. The majority of our findings stem from computational analyses, with the exception of IHC validation of IFI30 expression in tissue specimens. Additional cellular and animal experiments are essential to elucidate the mechanistic roles of IFI30 in disease progression. Furthermore, the CRC and AS datasets used were derived from separate patient cohorts. Future research should focus on establishing integrated disease models or collecting matched clinical samples to validate the biological and clinical relevance of the CRC-AS connection. Declarations Acknowledgements We would like to give our sincere gratitude to the reviewers for their constructive comments. Funding No funds, grants, or other support was received. Author contributions MG conceived and designed the study. DW and WY contributed to the writing of the manuscript. JY and SW supervised the study. All authors contributed to the article and approved the submitted version. Data availability statement The transcriptomics characteristics, somatic mutation data and clinical information of colorectal adenocarcinoma are from the TCGA-COAD data set of The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/projects/TCGA-COAD）. Transcriptome data of atherosclerosis (AS) were retrieved from the Gene Expression Database (GEO), and its accession number was GSE43292 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE43292）. A total of 2498 immune-related genes (IRGS) were downloaded from ImmPort database (https://immport.org/shared/). Ethics approval and consent to participate The protocol was approved by the Medical Research Ethics Committee and the Institutional Review Board of The First Affiliated Hospital of Harbin Medical University in accordance with the Declaration of Helsinki (as revised in 2013). The requirement for informed consent was waived by the Ethics Committee of First Affiliated Hospital of Harbin Medical University (IRB：IRB-AF/SQ-10/01.0).. Consent for publication Not applicable. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher ’ s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Wang C, et al. Exploring Social Isolation Among Patients With Colorectal Cancer and Their Spousal Caregivers in China: An Actor-Partner Interdependence Model. Nurs Health Sci. 2025;27(2):e70137. https://doi.org/10.1111/nhs.70137 . 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17:13:43","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74294,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/2ed63ba994aee1f6895f5a4c.png"},{"id":98620820,"identity":"dff09e88-d161-4d57-ba6b-2497452b6813","added_by":"auto","created_at":"2025-12-19 16:04:24","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18780,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/9460e4aaac316669003f31ff.png"},{"id":98620825,"identity":"a6448628-a32c-43ff-ae34-30dca038d06a","added_by":"auto","created_at":"2025-12-19 16:04:24","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33156,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/4928f171b32faeb1b8f2ff42.png"},{"id":98629634,"identity":"0839d661-a5a6-4865-98ee-f7f52053bf99","added_by":"auto","created_at":"2025-12-19 17:14:22","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187861,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/c2d65d4024e97728e978c193.png"},{"id":98620807,"identity":"92bcd1aa-38fa-4adc-98ad-5b6343c97f72","added_by":"auto","created_at":"2025-12-19 16:04:23","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203535,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/4bb165671d6402d80b1d17d2.png"},{"id":98629137,"identity":"07741f01-23d4-4933-b919-34b678f8bd81","added_by":"auto","created_at":"2025-12-19 17:13:17","extension":"xml","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121880,"visible":true,"origin":"","legend":"","description":"","filename":"60ff2a2443204b29bbe81e85465438e41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/d2711023c5478624d4b6db74.xml"},{"id":98629410,"identity":"d893974a-07e0-4851-9a73-dd0774a05f2f","added_by":"auto","created_at":"2025-12-19 17:13:52","extension":"html","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136563,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/507b52ac2b161843baf49698.html"},{"id":98629388,"identity":"69734735-2986-4930-8791-f4246a7ec5f6","added_by":"auto","created_at":"2025-12-19 17:13:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55559812,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the present study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/aae6dbfabf9f79fce1495b7f.png"},{"id":98620778,"identity":"46913dda-66ff-4e9c-bb26-21da3dc1997c","added_by":"auto","created_at":"2025-12-19 16:04:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":683008,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of commonly dysregulated immune-related genes (IRGs) in colorectal cancer (CRC) and atherosclerosis (AS). A. Volcano plot of differentially expressed genes between CRC and adjacent normal tissues. B. Volcano plot of differentially expressed genes between AS plaques and control vascular tissues. C. Venn diagram showing the intersection of dysregulated genes in CRC, AS, and immune-related genes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/500aa2da236ceb9dffb87dca.png"},{"id":98629376,"identity":"4a6b2afe-d28f-436d-8325-647d23dcf9b1","added_by":"auto","created_at":"2025-12-19 17:13:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":821358,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of the 102 overlapping immune-related genes. A. GO biological process (GO-BP) enrichment. B. GO molecular function (GO-MF) enrichment. C. GO cellular component (GO-CC) enrichment. D. KEGG pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/527e05a185bc2c2db68a8ead.png"},{"id":98629357,"identity":"65ea6cac-0f6d-4c63-a600-9d921869ee00","added_by":"auto","created_at":"2025-12-19 17:13:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1082944,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of \u003cem\u003eIFI30\u003c/em\u003e as a core gene shared between CRC and AS. A. Forest plot of univariate Cox regression for overall survival (OS). B. Kaplan-Meier survival curves of genes associated with OS. C. Expression levels of \u003cem\u003eFABP4\u003c/em\u003e and \u003cem\u003eIFI30\u003c/em\u003e in CRC vs. normal tissues. D. Expression levels of \u003cem\u003eFABP4\u003c/em\u003e and \u003cem\u003eIFI30\u003c/em\u003e in AS vs. control tissues.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/d8ebb0ca4fed66101a400c8e.png"},{"id":98620785,"identity":"88bf5bd2-d8f9-4577-b73e-0f60e5b94ef4","added_by":"auto","created_at":"2025-12-19 16:04:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2095271,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between \u003cem\u003eIFI30\u003c/em\u003e expression and the immune microenvironment in CRC. A. Correlation between \u003cem\u003eIFI30\u003c/em\u003e expression and ESTIMATE, stromal, and immune scores. B. ssGSEA-based analysis of \u003cem\u003eIFI30\u003c/em\u003eexpression and immune cell infiltration. C. CIBERSORT-based analysis of \u003cem\u003eIFI30\u003c/em\u003e expression and immune cell proportions. D. Differential expression of interleukin family genes between high and low IFI30 expression groups. E. Expression of interferon family genes in the IFI30 high vs. low expression groups. F. Expression of \u003cem\u003eTNF\u003c/em\u003e superfamily genes in the IFI30 high vs. low expression groups.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/6f5148b2fc291a0ad159220a.png"},{"id":98629598,"identity":"1c6025a3-44ac-40a6-9755-d4fc2185b9b1","added_by":"auto","created_at":"2025-12-19 17:14:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":346446,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between\u003cem\u003e IFI30\u003c/em\u003e expression and therapeutic sensitivity in CRC. A. Comparison of TIDE scores between IFI30 high and low expression groups. B. Differences in predicted sensitivity to chemotherapy agents between the two groups.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/3bb401045c3b71cd1180b346.png"},{"id":98629696,"identity":"5646d1eb-3e02-4feb-8d46-f150a122d768","added_by":"auto","created_at":"2025-12-19 17:14:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":962991,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between \u003cem\u003eIFI30 \u003c/em\u003eexpression and somatic mutations in CRC. A-B. Somatic mutation profiles of the TCGA-COAD cohort, showing frequently mutated genes. C-D. Comparison of mutation frequencies between IFI30 high and low expression groups in TCGA-COAD.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/7584fb40793b7603bacf5918.png"},{"id":98629382,"identity":"6aa51198-6990-4627-89e2-e0f973cfeb4c","added_by":"auto","created_at":"2025-12-19 17:13:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4092331,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell transcriptomic analysis of CRC tissues. A. Quality control criteria for single-cell RNA sequencing. B. t-SNE plot showing clustering of cell populations. C. Annotated t-SNE plot identifying 9 major cell subtypes. D. t-SNE plot showing IFI30 expression across cell types. E. Violin plot of IFI30 expression in different cell types. F. Identification of IFI30-positive and IFI30-negative macrophages. G-J. Functional enrichment of differentially expressed genes between IFI30-positive and IFI30-negative macrophages: GO-BP (G), GO-MF (H), GO-CC (I), and KEGG pathways (J).\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/867f49a973bf5f17d4c854e0.png"},{"id":98628953,"identity":"d866e470-c749-4a55-99da-e4f91feabe89","added_by":"auto","created_at":"2025-12-19 17:12:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4542172,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell transcriptomic analysis of AS tissues. A. Single-cell quality control metrics. B. t-SNE plot showing cell clustering in AS samples. C. Annotated t-SNE plot of 7 identified cell types. D. t-SNE visualization of IFI30 expression in AS cell types. E. Violin plot of IFI30 expression across AS cell populations. F. Stratification of macrophages into IFI30-positive and IFI30-negative subgroups. G-J. Enrichment analysis of differentially expressed genes in IFI30-positive vs. IFI30-negative macrophages: GO-BP (G), GO-MF (H), GO-CC (I), and KEGG pathways (J).\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/4f9b07fc16f9c013a4bff804.png"},{"id":98620805,"identity":"286558e9-a12e-4974-859d-60fe3cf8b357","added_by":"auto","created_at":"2025-12-19 16:04:23","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":21740099,"visible":true,"origin":"","legend":"\u003cp\u003eHE staining image of CRC and AS tissues\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/1f9a41b2b144c856e87c9cf6.png"},{"id":98620804,"identity":"e94794d3-fb3b-4dac-a2ca-60f820c44094","added_by":"auto","created_at":"2025-12-19 16:04:23","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":14143521,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of IFI30 in CRC and AS tissues, as determined by immunohistochemistry.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/9a5368963877f41b6e4b8048.png"},{"id":105034677,"identity":"06f091d8-eeec-4863-bb53-279efbf302e3","added_by":"auto","created_at":"2026-03-20 07:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":82655616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7551279/v1/5d8a1405-50e9-4bbf-a1e5-f8398829e26e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IFI30 remoulds immune microenvironment and macrophage function to promote colorectal cancer and atherosclerosis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) and atherosclerosis (AS) are widespread chronic conditions with significant impacts on global morbidity and mortality. Notably, CRC ranks third in incidence and second in cancer mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AS, on the other hand, is a chronic and progressive vascular condition that often remains clinically silent for years before resulting in severe cardiovascular and cerebrovascular events, such as acute myocardial infarction and ischemic stroke\u0026mdash;major causes of global disability-adjusted life years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent evidence points to a potential link between CRC and AS. Subclinical AS, characterized by carotid intima-media thickness (CIMT\u0026thinsp;\u0026ge;\u0026thinsp;1 mm) or the presence of carotid plaques, has been linked to a markedly elevated occurrence of colorectal adenomas and high-risk adenomas, both of which are well-established precursors of CRC, when compared to individuals without atherosclerotic changes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, a case-control study identified carotid AS as an independent risk determinant for CRC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings suggest a possible convergence of pathogenic mechanisms underlying CRC and AS.\u003c/p\u003e \u003cp\u003eA growing body of epidemiological evidence indicates that CRC and AS are influenced by overlapping risk profiles, notably including smoking, obesity, type 2 diabetes, and metabolic syndrome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Smoking contributes to the altered immune microenvironment (IME) in both diseases by enhancing DNA mutagenesis and modulating macrophage activity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Metabolic syndrome\u0026mdash;particularly obesity-associated dyslipidemia\u0026mdash;not only accelerates endothelial dysfunction but also facilitates CRC cell proliferation, invasion, and metastasis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, statins, widely used for lipid control, have demonstrated protective effects against both CRC and AS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Growing evidence supports a bidirectional relationship between CRC and AS, potentially driven by common pathogenic mechanisms. Chronic inflammation serves as a pivotal link between the two diseases. In AS, inflammatory mediators such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) promote the development of atherosclerotic plaques. Similarly, these cytokines are implicated in colorectal adenoma progression and carcinogenesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The chemopreventive effects of nonsteroidal anti-inflammatory drugs on both conditions further underscore the therapeutic potential of targeting inflammation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Immune cell infiltration also plays a central role in the pathophysiology of both CRC and AS. In CRC, the tumor microenvironment (TME) is characterized by diverse immune cell populations, with tumor-associated macrophages (TAMs) exerting significant influence on tumor progression and immune evasion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. TAMs display functional plasticity, adopting either a pro-inflammatory M1 or an immunosuppressive M2 phenotype in response to local cytokine cues. Numerous studies have reported that increased TAM infiltration correlates with poor prognosis and reduced survival in various cancers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], although their specific roles in CRC remain under debate [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In metastatic CRC, the TME is often dominated by M2-like TAMs, which are activated by stromal gene programs and chemokine signaling\u0026mdash;such as CXCL8\u0026mdash;leading to enhanced angiogenesis, extracellular matrix (ECM) remodeling, and immunosuppression through the genration of TGF-β and IL-10 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Macrophages are likewise key mediators in the initiation and progression of AS. Monocyte-derived macrophages migrate into the subendothelial space following endothelial dysfunction, where they engulf oxidized low-density lipoprotein via scavenger receptors, forming foam cells\u0026mdash;a defining feature of early atherogenesis. The functional plasticity of macrophages further influences plaque stability: pro-inflammatory macrophages contribute to plaque vulnerability, whereas M2-like macrophages possess anti-inflammatory and reparative properties, though their protective effects are often diminished in advanced lesions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIFI30, also known as GILT, is primarily localized in lysosomes and the cytoplasm, and is highly expressed in antigen-presenting cells, including bone marrow-derived dendritic cells (DCs), B cells (both progenitors and mature lineages), and monocytes/macrophages [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. IFI30 is currently the only known enzyme that catalyzes disulfide bond reduction within the endocytic pathway, thereby facilitating both MHC class I-restricted cross-presentation and MHC class II-restricted antigen processing by reducing disulfide bonds in endocytosed proteins [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, IFI30 plays a pivotal role in modulating T cell autoreactivity, as it is capable of suppressing T cell proliferation and activation, thereby dampening immune responses and autoimmune potential [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Elevated expression of IFI30 has been observed in diverse pathological conditions, particularly in malignant tumors. Its overexpression is often associated with poor prognosis due to enhanced immune cell infiltration, and has been proposed as both a prognostic indicator and an immunotherapeutic target in multiple cancer types [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Emerging evidence has documented increased IFI30 expression in non-neoplastic diseases such as AS, where its upregulation correlates negatively with plaque stability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These observations point to IFI30 as a possible molecular mediator connecting the pathophysiological processes of CRC and AS, warranting further investigation.\u003c/p\u003e \u003cp\u003eDespite accumulating evidence indicating a clinical association between CRC and AS, the shared cellular and molecular mechanisms remain poorly defined. The rapid evolution of next-generation sequencing has positioned bioinformatics as an essential approach for dissecting molecular interactions and elucidating cross-disease mechanisms. In this study, we employed integrative bioinformatics approaches to identify shared immune-related genes (IRGs) involved in both CRC and AS. We then focused on the hub gene IFI30, assessing its relationship with the IME, chemotherapy sensitivity, somatic mutations, and response to immunotherapy in CRC. Our aim was to elucidate potential pathophysiological mechanisms linking these diseases and to identify promising therapeutic targets for both conditions.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eTranscriptomic profiles, somatic mutation data, and clinical information for 483 colorectal adenocarcinoma (COAD) samples and 41 adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA-COAD) project. For survival analysis and model construction, samples lacking complete survival data or with overall survival (OS) less than 30 days were excluded. Transcriptomic data for AS were retrieved from the Gene Expression Omnibus (GEO) database under accession number GSE43292, which includes 32 carotid atherosclerotic plaque samples and 32 matched normal intima/media samples from distal carotid arteries. A total of 2,498 IRGs were downloaded from the ImmPort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://immport.org/shared/\u003c/span\u003e\u003cspan address=\"https://immport.org/shared/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Single-Cell RNA Sequencing (scRNA-seq) Analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA-seq datasets were obtained from GEO under accession number GSE231559, including samples from 3 adjacent normal tissues and 6 CRC tissues, as well as 3 atherosclerotic plaque samples and 3 paired normal carotid tissues. Data preprocessing and analysis were carried out in R utilizing the Seurat workflow. Cells with low gene counts (\u0026lt;\u0026thinsp;200) or high mitochondrial transcript proportions (\u0026gt;\u0026thinsp;20%) were excluded to ensure quality. To correct for batch-related variability, the Harmony algorithm was applied. Subsequently, the 2,000 most variable genes were selected via the FindVariableFeatures function. Principal component analysis (PCA) facilitated feature space reduction, and key marker genes for each cluster were determined utilizing the FindMarkers function under default configurations. Cell subpopulations were annotated with the SingleR package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Survival analysis\u003c/h2\u003e \u003cp\u003eOS was selected as the primary clinical endpoint. To investigate the prognostic relevance of candidate genes in CRC, univariate Cox proportional hazards modeling was applied. Based on the median expression the gene of interest, patients were classified into two groups: high and low expression. Kaplan-Meier analysis was implemented to visualize survival distributions, and statistical differences between the groups were tested utilizing the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) enrichment analysis, including Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was conducted to explore the biological functions and pathways associated with gene sets. Enrichment analyses were completed utilizing the ClusterProfiler package in R. Terms with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTo characterize immune cell infiltration in CRC samples, three computational methods (CIBERSORT, single-sample gene set enrichment analysis [ssGSEA], and ESTIMATE) were employed. For the CIBERSORT algorithm, normalized gene expression matrices were analyzed with the help of the R package CIBERSORT to estimate the relative proportions of 22 immune cell types. ssGSEA was implemented utilizing the GSVA package in R to assess the enrichment of immune cell signatures at the individual sample level. The ESTIMATE package was utilized to compute StromalScore, ImmuneScore, and ESTIMATEScore, thereby quantifying stromal and immune cell components in the TME.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Bulk RNA-Seq differential expression analysis\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was conducted utilizing the limma package (version 3.40.6) in R, which applies linear models suited for microarray and RNA-seq data. For CRC datasets, genes were deemed differentially expressed if they met the criteria of |log2 fold change [FC]| \u0026gt; 1 (i.e., fold change\u0026thinsp;\u0026gt;\u0026thinsp;2) and adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For atherosclerotic samples, significance was determined utilizing an FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Results were visualized employing the ggplot2 package in R, generating volcano plots and heatmaps to illustrate gene expression differences between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Somatic mutation analysis\u003c/h2\u003e \u003cp\u003eSomatic mutation data were analyzed utilizing the maftools package in R. Mutation Annotation Format (MAF) files were imported via the read.maf function to construct MAF objects. Key mutation statistics, including tumor mutation burden, mutation types, and frequently mutated genes, were extracted using functions such as getSampleSummary and getGeneSummary. Mutation landscapes were visualized using oncoplots to display the frequency and types of gene alterations. Comparative analysis was implemented to test differences in mutation frequencies between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess immunotherapy response, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied. Higher TIDE scores indicate reduced sensitivity to immune checkpoint blockade therapy. For chemotherapy drug sensitivity, the oncoPredict package in R was used to predict responses based on TCGA-COAD gene expression data. The calcPhenotype function was employed to apply trained models from the GDSC database and compute drug sensitivity scores (Drug Sensitivity Score, DSS) for each tumor sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Hematoxylin and eosin (H\u0026amp;E) staining\u003c/h2\u003e \u003cp\u003eTissue specimens were obtained from the hospital pathology archives, including 10 cases each of histologically confirmed stable and unstable carotid atherosclerotic plaques, as well as 10 CRC tissues and their matched adjacent normal tissues. Samples were fixed in 4% paraformaldehyde and paraffin-embedded. Sections of 4\u0026ndash;5 \u0026micro;m thickness were deparaffinized, rehydrated, and washed prior to H\u0026amp;E staining according to standard protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Immunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eTissue specimens were initially preserved in 10% neutral-buffered formalin for a minimum of 24 hours, followed by dehydration through a graded ethanol series (75, 95, 95, 100, 100%) and embedding in paraffin after triple xylene treatment. Paraffin blocks were prepared to sections of 5 \u0026micro;m thickness using a microtome, which were expanded on a 40\u0026deg;C water bath, transferred to glass slides, and baked at 60\u0026deg;C for 30 minutes. For antigen retrieval, slides underwent deparaffinization with xylene (three changes) and were sequentially rehydrated utilizing descending concentrations of ethanol (100%, 90%, 80%, 70%, and distilled water), then treated in antigen retrieval solution at 96\u0026deg;C. Endogenous peroxidase activity was quenched following treatment with hydrogen peroxide. Sections were incubated sequentially with primary antibody (anti-COX20) and corresponding secondary antibody. Streptavidin-peroxidase solution was applied, followed by development with DAB chromogen and rinsing with distilled water. Slides were counterstained with hematoxylin for 1\u0026ndash;2 minutes, differentiated in 1% acid-alcohol for 3 seconds, and rinsed again. After dehydration, slides were mounted and imaged.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistical analysis\u003c/h2\u003e \u003cp\u003eFor comparisons between two groups, either the unpaired t-test or the Mann-Whitney U test was used, depending on data distribution. For comparisons among more than two groups, the Kruskal-Wallis test was employed. Correlation analyses were implemented utilizing Spearman's rank correlation. Survival data were analyzed utilizing Kaplan-Meier survival curves with log-rank tests and univariate Cox regression. All statistical analyses were made by virtue of R software. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ns\u0026thinsp;=\u0026thinsp;not significant).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of commonly dysregulated IRGs in CRC and AS\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flow chart of this study. Differential expression analysis revealed that, compared with adjacent normal tissues, a total of 14,622 genes (10,692 upregulated and 3,930 downregulated genes) were dysregulated in CRC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In AS tissues, 1,324 genes were identified as differentially expressed relative to control vascular tissues, with 734 upregulated and 590 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). By intersecting the dysregulated genes from CRC (14,622) and AS (1,324) with a list of 2,498 IRGs obtained from the ImmPort database, 102 overlapping genes were identified. These were considered commonly dysregulated IRGs in both CRC and AS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGO-BP enrichment analysis demonstrated that these 102 genes were mainly implicated in BP categories such as leukocyte migration, production of molecular mediators of the immune response, positive regulation of the MAPK cascade, ERK1/ERK2 cascade regulation, cell chemotaxis, myeloid leukocyte migration, and immunoglobulin production (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). GO-MF analysis indicated enrichment in signaling receptor activator activity, receptor-ligand activity, antigen binding, cytokine activity, cytokine receptor binding, G protein-coupled receptor binding, immunoglobulin receptor binding, and chemokine receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). GO-CC analysis revealed that these genes were primarily located in the immunoglobulin complex, the external side of the plasma membrane, membrane microdomains and rafts, tertiary granules, circulating immunoglobulin complexes, blood microparticles, and specific granule membranes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). KEGG pathway enrichment further indicated involvement in cytokine-cytokine receptor interaction, MAPK signaling pathway, PI3K-Akt signaling, Ras signaling, lipid and AS pathway, Rap1 signaling, NF-κB signaling, HIF-1 signaling, Toll-like receptor (TLR) signaling, and EGFR tyrosine kinase inhibitor resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2\u003c/b\u003e \u003cb\u003eIFI30\u003c/b\u003e \u003cb\u003eas a core gene shared by CRC and AS and its association with prognosis in CRC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnivariate Cox regression analysis identified 11 genes among the 102 shared IRGs that were distinctly linked to OS in CRC patients, including \u003cem\u003eCCL19\u003c/em\u003e, \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eBDNF\u003c/em\u003e, \u003cem\u003eIGF1\u003c/em\u003e, \u003cem\u003eIGLV6-57\u003c/em\u003e, \u003cem\u003eIFI30\u003c/em\u003e, \u003cem\u003eCD79A\u003c/em\u003e, \u003cem\u003eIGKV1D-42\u003c/em\u003e, \u003cem\u003eIGKV1D-33\u003c/em\u003e, \u003cem\u003eSEMA3E\u003c/em\u003e, and \u003cem\u003eLTBP4\u003c/em\u003e. Elevated expression of these genes was correlated with poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Kaplan-Meier survival analysis further demonstrated that only \u003cem\u003eIFI30\u003c/em\u003e and FABP4 were distinctly linked to reduced OS when highly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the other genes did not show statistically significant survival differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Comparative expression analysis revealed that \u003cem\u003eFABP4\u003c/em\u003e was downregulated in CRC tissues but upregulated in AS samples compared to their respective controls (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, \u003cem\u003eIFI30\u003c/em\u003e was notably upregulated in both CRC and AS tissues (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The evidence points to \u003cem\u003eIFI30\u003c/em\u003e as a converging immunological factor potentially driving disease mechanisms in both CRC and AS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between \u003cem\u003eIFI30\u003c/em\u003e expression and the IME in CRC\u003c/h2\u003e \u003cp\u003eESTIMATE algorithm analysis demonstrated that higher \u003cem\u003eIFI30\u003c/em\u003e expression levels were positively correlated with increased ESTIMATE Score (Cor\u0026thinsp;=\u0026thinsp;0.539), Stromal Score (Cor\u0026thinsp;=\u0026thinsp;0.447), and Immune Score (Cor\u0026thinsp;=\u0026thinsp;0.560), suggesting enhanced immune and stromal components in the TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ssGSEA analysis revealed a strong positive correlation between IFI30 expression and the enrichment levels of multiple immune cell populations, including macrophages, Th1 cells, cytotoxic cells, effector memory T cells (Tem), neutrophils, activated DCs (aDCs), total T cells, DCs, regulatory T cells (Tregs), immature DCs (iDCs), T helper cells, NK CD56\u003csup\u003edim\u003c/sup\u003e cells, B cells, T follicular helper cells (TFH), mast cells, eosinophils, CD8\u003csup\u003e+\u003c/sup\u003e T cells, plasmacytoid DCs (pDCs), NK cells, and central memory T cells (Tcm). In contrast, a negative correlation was observed with Th17 cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CIBERSORT analysis further revealed a positive relationship between \u003cem\u003eIFI30\u003c/em\u003e expression and the abundance of M1 and M2 macrophages, follicular helper T cells, neutrophils, resting mast cells, and activated memory CD4\u003csup\u003e+\u003c/sup\u003e T cells. Conversely, negative associations were observed with activated mast cells, activated DCs, monocytes, and resting memory CD4\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we evaluated the expression differences of key immune-modulatory genes in the interleukin, interferon, and TNF families between the two established groups in CRC samples. The high IFI30 group exhibited notably upregulated expression of the majority of interleukin family genes relative to the low expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, in the interferon family, IFNE and IFNG were markedly elevated in the IFI30-high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the TNF superfamily, most members were evidently upregulated in the high \u003cem\u003eIFI30\u003c/em\u003e expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collectively, \u003cem\u003eIFI30\u003c/em\u003e is strongly associated with an activated IME in CRC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cem\u003eIFI30\u003c/em\u003e expression shows linkage with therapeutic sensitivity in CRC\u003c/h2\u003e \u003cp\u003eAnalysis using the TIDE algorithm showed that CRC patients with high IFI30 expression had evidently higher TIDE scores relative to those with low one, suggesting a reduced likelihood of response to immune checkpoint blockade therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of chemotherapy response, patients in the high IFI30 expression group demonstrated lower predicted sensitivity to multiple chemotherapeutic agents, including Sepantronium bromide, Luminespib, Dactolisib, Foretinib, Mirin, Alisertib, Dactinomycin, Vinorelbine, and Vincristine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Association of \u003cem\u003eIFI30\u003c/em\u003e expression with somatic mutations in CRC\u003c/h2\u003e \u003cp\u003eSomatic mutation analysis based on data from 428 CRC samples revealed that APC mutations were the most prevalent, observed in 69% of cases, predominantly as multi-hit mutations. TP53 mutations were the second most frequent (53%), primarily missense variants, followed by TTN mutations (45%), also mainly missense (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Comparative mutation profiling between IFI30 high- and low-expression groups showed that the high-expression group exhibited a higher frequency of mutations in genes including \u003cem\u003eKIF26A\u003c/em\u003e, \u003cem\u003eRDX\u003c/em\u003e, \u003cem\u003eZNF518A\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eMTA1\u003c/em\u003e, \u003cem\u003eKMT2D\u003c/em\u003e, \u003cem\u003eDDX27\u003c/em\u003e, \u003cem\u003eTNRC6C\u003c/em\u003e, \u003cem\u003eSEMA6C\u003c/em\u003e, \u003cem\u003eAKAP13\u003c/em\u003e, \u003cem\u003eFCGBP\u003c/em\u003e, \u003cem\u003eTRRAP\u003c/em\u003e, \u003cem\u003eELP1\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eGGCX\u003c/em\u003e, \u003cem\u003eIFT122\u003c/em\u003e, \u003cem\u003eTAS1R3\u003c/em\u003e, \u003cem\u003eTMEM104\u003c/em\u003e, \u003cem\u003eFAT1\u003c/em\u003e, \u003cem\u003eNPHP4\u003c/em\u003e, \u003cem\u003ePIK3C2B\u003c/em\u003e, \u003cem\u003eZNF236\u003c/em\u003e, \u003cem\u003eROBO3\u003c/em\u003e, \u003cem\u003eSORBS2\u003c/em\u003e, \u003cem\u003eMYCBP2\u003c/em\u003e, and \u003cem\u003eKCNQ5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Single-cell transcriptomic analysis reveals \u003cem\u003eIFI30\u003c/em\u003e expression in CRC and AS\u003c/h2\u003e \u003cp\u003eBulk RNA-seq analysis indicated that \u003cem\u003eIFI30\u003c/em\u003e expression showed a positive linkage with an active IME in CRC. However, its elevated expression also correlated with poor prognosis, prompting further investigation at the single-cell level.\u003c/p\u003e \u003cp\u003eIn CRC, scRNA-seq identified 19 distinct cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), which were annotated into 9 major cell types: monocytes, T cells, smooth muscle cells, epithelial cells, B cells, endothelial cells, macrophages, tissue stem cells, and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). \u003cem\u003eIFI30\u003c/em\u003e expression was predominantly localized in monocytes and macrophages, with remarkably higher levels in tumor samples relative to controls (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Given the well-documented role of TAMs in cancer progression, we focused on \u003cem\u003eIFI30\u003c/em\u003e\u0026rsquo;s role within this cell population. Macrophages were stratified into IFI30-positive and IFI30-negative groups, and differential gene expression followed by functional enrichment analysis was performed. Enriched pathways in IFI30-positive macrophages included oxidative phosphorylation and cholesterol metabolism (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ), processes closely associated with M2 polarization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, scRNA-seq of atherosclerotic tissues revealed 23 cellular clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), categorized into 7 cell types: chondrocytes, smooth muscle cells, T cells, monocytes, endothelial cells, NK cells, and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). \u003cem\u003eIFI30\u003c/em\u003e was again highly expressed in monocytes and macrophages, with expression markedly elevated in atherosclerotic plaques compared to normal vascular tissues (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). As in CRC, macrophages were subdivided based on IFI30 expression status, followed by differential expression and pathway enrichment analyses. IFI30-positive macrophages showed enrichment in pathways related to cytokine signaling, phagosome formation, lipid metabolism and AS, MAPK and NF-κB signaling, fluid shear stress, IL-17, and PPAR pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG-\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eJ). These pathways are known to play critical roles in AS progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 \u003cem\u003eIFI30\u003c/em\u003e is upregulated in CRC and atherosclerotic lesions\u003c/h2\u003e \u003cp\u003eTo validate \u003cem\u003eIFI30\u003c/em\u003e expression at the tissue level, H\u0026amp;E staining was first used to localize pathological regions in CRC and paired adjacent normal tissues, as well as in stable versus unstable carotid plaques (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). IHC staining was then performed to quantify IFI30 protein expression. Results showed notably higher IFI30 expression in unstable atherosclerotic plaques compared to stable plaques. Similarly, IFI30 was markedly overexpressed in CRC tissues relative to adjacent non-tumor tissues. These findings support the notion that IFI30 may promote disease progression in both CRC and AS (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eCRC has been increasingly reported to share pathophysiological links with AS, suggesting common risk factors and potentially overlapping molecular mechanisms. However, the key regulatory pathways connecting these two diseases remain largely undefined. In this study, we employed comprehensive bioinformatics analyses to uncover shared pathological immune mechanisms between CRC and AS. Functional enrichment of 102 co-dysregulated IRGs revealed a strong association with immune cell infiltration\u0026mdash;a hallmark of both tumor progression and vascular inflammation. Tumor-infiltrating leukocytes (TILs) play essential immunomodulatory roles within the TME, contributing to immune evasion and ultimately facilitating tumor progression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, in early atherosclerotic plaque development, the infiltration of monocytes into the vascular intima is a critical initiating event [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Chemokine-receptor interactions orchestrate immune cell recruitment and communication in both contexts. For example, the CXCL1\u0026ndash;CXCR2 axis modulates the infiltration of myeloid-derived suppressor cells (MDSCs) and cytotoxic T lymphocytes in the CRC tumor IME [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In AS, the CCL2-CCR2 axis promotes monocyte recruitment into atherosclerotic lesions, where they differentiate into macrophages that contribute to foam cell formation and perpetuate local inflammation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Signal transduction pathways also play pivotal roles in disease progression. The MAPK/ERK signaling pathway regulates T cell activation and macrophage polarization in the TME, thereby influencing tumor initiation, metastasis, treatment response, and resistance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In AS, this pathway drives vascular inflammation and smooth muscle cell proliferation, contributing to plaque development and instability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Likewise, the NF-κB/TLR axis\u0026mdash;a key regulator of inflammatory signaling\u0026mdash;has been implicated in both CRC and AS, highlighting its dual role in mediating immune responses and chronic inflammation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo pinpoint core genes linking CRC and AS, we integrated survival modeling, differential expression profiling, and prognostic validation. Through this comprehensive approach, IFI30 was identified as the central hub gene common to both conditions, and subsequent analyses focused on its biological and clinical significance. IFI30 encodes a lysosomal thiol reductase essential for antigen processing and immune modulation. Accumulating evidence suggests that this gene contributes to tumor biology, with its expression markedly elevated across multiple malignancies, notably CRC. Pan-cancer bioinformatics analyses have shown elevated IFI30 transcript levels in CRC tissues, which correlate with poor patient outcomes. For example, data from the Pathology Atlas\u0026mdash;which contains over five million IHC images across multiple tumor types\u0026mdash;revealed that IFI30 exhibits strong cytoplasmic staining in 60\u0026ndash;78% of CRC samples, with high expression levels associated with reduced 5-year survival [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In another study focusing on anoikis-related genes in CRC, IFI30 mRNA and protein expression were markedly increased in CRC cell lines such as HCT116 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and SW620 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to normal colonic epithelial cells (NCM460). IHC analysis of patient samples further confirmed that tumor tissues exhibited significantly higher IFI30 expression than adjacent normal mucosa [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our IHC findings are consistent with these observations. Quantitative analysis showed that the mean optical density (MOD) of IFI30 was significantly higher in CRC and unstable atherosclerotic plaques than in their respective controls. Additionally, unstable plaques showed thinner fibrous caps and elevated IFI30 expression.\u003c/p\u003e \u003cp\u003eApplication of the ESTIMATE algorithm demonstrated a strong positive correlation between \u003cem\u003eIFI30\u003c/em\u003e expression and TME metrics, including ESTIMATE (Cor\u0026thinsp;=\u0026thinsp;0.539), stromal (Cor\u0026thinsp;=\u0026thinsp;0.447), and immune scores (Cor\u0026thinsp;=\u0026thinsp;0.560). Transcriptomic profiling further indicated that high \u003cem\u003eIFI30\u003c/em\u003e expression aligned with the upregulation of numerous cytokine-related genes, particularly within the interleukin and TNF superfamilies. Within the interferon family, IFNE and IFNG levels were notably elevated in the high IFI30 group. These results collectively uncover that \u003cem\u003eIFI30\u003c/em\u003e expression is linked to heightened immune activity in CRC. However, immune checkpoint response prediction using the TIDE algorithm demonstrated that the IFI30 high-expression group had distinctly higher TIDE scores, implying reduced sensitivity to immunotherapy. This apparent contradiction prompted further investigation into the nature of immune cell infiltration. Multi-algorithm analysis, including CIBERSORT and ssGSEA, demonstrated that \u003cem\u003eIFI30\u003c/em\u003e expression was notably correlated with increased infiltration of macrophages\u0026mdash;particularly the M2 subtype. These findings were corroborated by scRNA-seq data, which consistently showed that \u003cem\u003eIFI30\u003c/em\u003e is primarily expressed in monocytes and macrophages in both CRC and AS. In disease samples, \u003cem\u003eIFI30\u003c/em\u003e expression was markedly upregulated compared to normal controls. Stratification based on \u003cem\u003eIFI30\u003c/em\u003e expression revealed that macrophage-associated pathways were significantly enriched in both conditions. These results strongly suggest that \u003cem\u003eIFI30\u003c/em\u003e may drive disease progression in CRC and AS through macrophage-mediated immunomodulation.\u003c/p\u003e \u003cp\u003eTAMs, comprising both M1-like and M2-like subsets, are a prominent component of the tumor-infiltrating immune cell population. TAMs and tumor cells engage in a mutually influential interplay that orchestrates changes in both immune regulation and tumor progression pathways. TAMs secrete various immunosuppressive molecules\u0026mdash;such as TGF-β, IL-10, and nitric oxide (NO)\u0026mdash;which suppress anti-tumor immune responses and facilitate tumor cell proliferation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition to promoting tumor growth, TAMs also modulate the TME in ways that impair the effectiveness of chemotherapy, targeted therapy, and immunotherapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A key mechanism through which TAMs support tumor progression is the induction of immune evasion. A recent study investigating TAMs in CRC demonstrated that macrophage infiltration suppresses CD8\u003csup\u003e+\u003c/sup\u003e T cell activity via CCL5-mediated upregulation of PD-L1 on tumor cells, thereby promoting CRC progression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, exposure of macrophages with poly(I:C) and LPS led to increased CCL5 expression. Treatment with an anti-CCL5 antibody blocked the macrophage-driven increase in PD-L1 levels and its binding to PD-1 on HT29 cells. Co-culture assays demonstrated that macrophage presence markedly reduced T cell-mediated cytotoxicity, an effect that was reversed by anti-CCL5 treatment. Notably, when PD-L1 expression was silenced in HT29 cells, neither macrophage presence nor CCL5 blockade influenced T cell killing. Moreover, PD-L1 blockade in tumor cells not only decreased TAM infiltration but also enhanced the recruitment of cytotoxic CD8\u003csup\u003e+\u003c/sup\u003e T lymphocytes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings suggest the existence of a positive feedback loop in which macrophage-derived CCL5 upregulates PD-L1 expression in tumor cells, which in turn triggers further macrophage infiltration and immune escape. This loop ultimately contributes to CRC progression and immune resistance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSomatic mutation analysis revealed that the most frequently mutated genes in CRC were \u003cem\u003eAPC\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003eTTN\u003c/em\u003e, consistent with previous genomic studies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To further investigate the relationship between gene mutations and IRG expression, we stratified CRC samples based on IFI30 expression levels. Comparative analysis showed that 27 genes had significantly higher mutation frequencies in the IFI30 high-expression group relative to the low-expression group. A subset of these genes has been documented to contribute to CRC progression and prognosis. For instance, in a study aiming to identify clinicopathologically specific driver mutations in CRC, \u003cem\u003eERBB4\u003c/em\u003e mutations were found to cluster predominantly in male patients. Among male patients with microsatellite stable (MSS) CRC, those harboring \u003cem\u003eERBB4\u003c/em\u003e mutations exhibited significantly poorer overall survival than those without such mutations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, mutations in \u003cem\u003eKMT2D\u003c/em\u003e were shown to be associated with persistent polyps and early-stage CRC detection, suggesting its potential utility as a genetic biomarker for early diagnosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. High-frequency mutations in \u003cem\u003eFCGBP\u003c/em\u003e have also been reported in CRC tissues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Analysis of clinical datasets revealed that FCGBP mRNA and protein levels were consistently downregulated across all CRC stages compared to normal tissues. Furthermore, low FCGBP expression was linked to unfavorable survival outcomes. Functionally, FCGBP has been proposed to act as a tumor suppressor and to influence immune cell infiltration within the CRC microenvironment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChemotherapy remains a cornerstone of CRC treatment, having significantly improved patient survival through its cytotoxic effects. However, the emergence of chemoresistance continues to pose a substantial barrier to effective therapy, ultimately limiting treatment outcomes and adversely impacting prognosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our study, increased resistance to various chemotherapies was observed in CRC cases characterized by high IFI30 expression, in contrast to their low-expressing counterparts. Specifically, the high-expression group showed decreased sensitivity to nine distinct chemotherapy drugs. Emerging evidence suggests that TAMs, recognized as central players in the TME, are key mediators of chemoresistance in CRC [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. One study demonstrated that 5-FU induced activation of the HIF1α-HMGB1 axis in CRC cells, which promotes TAM infiltration. In turn, these macrophages secrete GDF15, a cytokine widely associated with chemoresistance across malignancies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], leading to a marked increase in the 5-FU IC50 and enhanced colony formation in CRC cell lines [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition to immune cells, stromal components\u0026mdash;particularly cancer-associated fibroblasts (CAFs)\u0026mdash;also contribute to chemoresistance. CAFs remodel the ECM and promote epithelial-mesenchymal transition (EMT), thereby supporting tumor survival and therapeutic evasion [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In our analysis, the stromal score calculated via the ESTIMATE algorithm was remarkably elevated in the IFI30 high-expression group (Cor\u0026thinsp;=\u0026thinsp;0.447), suggesting that stromal enrichment may underlie the observed chemoresistance. These findings imply that \u003cem\u003eIFI30\u003c/em\u003e may influence treatment response by shaping a pro-resistance stromal and IME.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eTo our knowledge, this work represents the first integrative bioinformatics investigation aimed at delineating the shared molecular mechanisms between CRC and AS through integrative bioinformatics analysis. Our findings identify \u003cem\u003eIFI30\u003c/em\u003e as a key IRG that is upregulated in both conditions and associated with immune cell infiltration, stromal activity, somatic mutations, poor prognosis, and reduced therapeutic sensitivity. These insights lay a conceptual groundwork for future diagnostic innovations and therapeutic interventions targeting overlapping mechanisms in CRC and AS. Nonetheless, several limitations should be mentioned. The majority of our findings stem from computational analyses, with the exception of IHC validation of \u003cem\u003eIFI30\u003c/em\u003e expression in tissue specimens. Additional cellular and animal experiments are essential to elucidate the mechanistic roles of \u003cem\u003eIFI30\u003c/em\u003e in disease progression. Furthermore, the CRC and AS datasets used were derived from separate patient cohorts. Future research should focus on establishing integrated disease models or collecting matched clinical samples to validate the biological and clinical relevance of the CRC-AS connection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to give our sincere gratitude to the reviewers for their constructive comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMG conceived and designed the study. DW and WY contributed to the writing of the manuscript. JY and SW supervised the study. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptomics characteristics, somatic mutation data and clinical information of colorectal adenocarcinoma are from the TCGA-COAD data set of The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/projects/TCGA-COAD）. Transcriptome data of atherosclerosis (AS) were retrieved from the Gene Expression Database (GEO), and its accession number was GSE43292 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE43292）. A total of 2498 immune-related genes (IRGS) were downloaded from ImmPort database (https://immport.org/shared/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol was approved by the Medical Research Ethics Committee and the Institutional Review Board of The First Affiliated Hospital of Harbin Medical University in accordance with the Declaration of Helsinki (as revised in 2013). The requirement for informed consent was waived by the Ethics Committee of First Affiliated Hospital of Harbin Medical University (IRB：IRB-AF/SQ-10/01.0)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang C, et al. Exploring Social Isolation Among Patients With Colorectal Cancer and Their Spousal Caregivers in China: An Actor-Partner Interdependence Model. Nurs Health Sci. 2025;27(2):e70137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nhs.70137\u003c/span\u003e\u003cspan address=\"10.1111/nhs.70137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei X, et al. Investigating the role of the Pon1-rs854560 (L55M) SNP in colorectal Cancer susceptibility. 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Theranostics. 2018;8(14):3932\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.25541\u003c/span\u003e\u003cspan address=\"10.7150/thno.25541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Atherosclerosis, Immune-related genes, IFI30, Multiomic analysis","lastPublishedDoi":"10.21203/rs.3.rs-7551279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7551279/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) and atherosclerosis (AS) are both chronic inflammatory diseases that may share immune-related genes (IRGs) and regulatory mechanisms. This study aimed to identify commonly dysregulated IRGs in CRC and AS, and to investigate their clinical significance and molecular functions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTranscriptomic, single-cell RNA sequencing (scRNA-seq), and somatic mutation data from the TCGA-COAD and GEO databases were integrated to identify overlapping dysregulated IRGs in CRC and AS. Comprehensive analyses, including survival analysis, immune infiltration assessment (CIBERSORT, ssGSEA, ESTIMATE), functional enrichment, drug sensitivity prediction, and single-cell analysis, were conducted to evaluate the prognostic relevance and immunological role of the core gene \u003cem\u003eIFI30\u003c/em\u003e. The expression level of core genes was verified by staining pathological sections of stored files.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 102 IRGs were found to be commonly dysregulated in both CRC and AS. Among them, \u003cem\u003eIFI30\u003c/em\u003e was notably upregulated in both diseases, with its upregulation predicting poor prognosis in CRC (HR\u0026thinsp;=\u0026thinsp;1.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Immune profiling revealed that elevated \u003cem\u003eIFI30\u003c/em\u003e expression was linked to higher immune scores and increased infiltration of macrophages and T cells. \u003cem\u003eIFI30\u003c/em\u003e expression also showed a positive linkage with genes in the interleukin, interferon, and TNF families. scRNA-seq indicated that \u003cem\u003eIFI30\u003c/em\u003e is predominantly expressed in macrophages, where it may promote M2 polarization by modulating oxidative phosphorylation and lipid metabolism pathways. Furthermore, high \u003cem\u003eIFI30\u003c/em\u003e expression was linked to reduced sensitivity to immunotherapy and certain chemotherapeutic agents, as well as increased mutation frequencies in genes such as \u003cem\u003eKIF26A\u003c/em\u003e and \u003cem\u003eTTN\u003c/em\u003e. Immunohistochemical experiments confirmed that the expression of IFI30 in colorectal cancer tissue and unstable carotid plaque increased significantly.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e \u003cem\u003eIFI30\u003c/em\u003e is a critical immune regulatory gene commonly involved in both CRC and AS, with functional evidence pointing to its role in modulating macrophage-driven immune remodeling. These characteristics position IFI30 as a biomarker of clinical relevance and a candidate for future targeted therapies. These findings provide new insights into shared immunopathological mechanisms across chronic inflammatory diseases.\u003c/p\u003e","manuscriptTitle":"IFI30 remoulds immune microenvironment and macrophage function to promote colorectal cancer and atherosclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 16:04:18","doi":"10.21203/rs.3.rs-7551279/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b642d091-c838-429f-b29b-504071306ccf","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:44:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 16:04:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7551279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7551279","identity":"rs-7551279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}