Targeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression

Targeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression | 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 Targeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression Songguo Zheng, Qi Hu, Yang Lu, Xiaolan Zhong, Peng Huang, Lisheng Zheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7382643/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) exhibits limited responsiveness to immune-checkpoint blockade, necessitating further investigation. The intratumoral Treg/CD8⁺ T-cell ratio serves as a predictive biomarker for therapeutic efficacy. Here, we demonstrate that HSPB1 targeting reduces this ratio and confers therapeutic benefit in CRC. Methods Candidate genes were identified by integrative single-cell transcriptomics, TCGA and spatial transcriptomics, followed by survival analyses of TCGA cohorts. Functional interrogation was performed using CRISPR-Cas9 engineered knockout cell lines. Subcutaneous tumor models were established, and the immune microenvironment was characterized by multiparametric flow cytometry. Mechanistic validation was achieved through bulk RNA-seq and complementary functional assays. Results Single-cell profiling and TCGA WGCNA analyze identified HSPB1 as a putative determinant of the intratumoral Treg/CD8⁺ T-cell ratio, and survival analysis showed its prognostic relevance in CRC. Spatial transcriptomics revealed colocalization of HSPB1-expressing tumor cells with Tregs. Subcutaneous tumor models demonstrated that CRISPR-mediated HSPB1 deletion or pharmacologic inhibition markedly suppressed tumor growth and reprogrammed the Treg-dominated microenvironment. In vitro polarization assays confirmed that targeting HSPB1 selectively restrains Treg differentiation without affecting Th17. Integrated transcriptomic and functional studies further elucidated that HSPB1 orchestrates CCL20–CCR6 mediated Treg recruitment, thereby shaping the immunosuppressive milieu within colorectal tumors. Conclusions Targeting HSPB1 exerts dual anti-tumor effects: it directly suppresses neoplastic proliferation and simultaneously alleviates Treg-mediated immunosuppression within the tumor microenvironment. Colorectal cancer Tumor microenvironment Treg HSPB1 CCL20 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Colorectal cancer (CRC) ranks the third most prevalent malignancy worldwide and the second leading cause of cancer-related mortality[ 1 , 2 ]. Although immunotherapy has recently demonstrated somehow efficacy in CRC, the overall response rate remains limited. Consequently, a deeper comprehension of the regulatory mechanisms governing the CRC immune microenvironment is imperative for refining current therapeutic strategies. The advent of single cell RNA sequencing (scRNA-seq) and spatial transcriptomics has enabled unprecedented resolution of the intratumoral immune milieu and has begun to elucidate the intricate crosstalk between neoplastic and immune cells[ 3 – 5 ]. CD4 + regulatory T cells (Tregs) are indispensable for maintaining peripheral tolerance and preventing autoimmunity[ 6 , 7 ]. Within the CRC microenvironment, they constitute a dominant immunosuppressive population that curtails anti-tumor immunity and undermines responses to immunotherapy[ 8 – 10 ]. The Treg/CD8⁺ T-cell ratio not only quantifies the degree of local immunosuppression but also robustly predicts patient prognosis and responsiveness to immune checkpoint blockade[ 11 , 12 ]. These observations underscore the therapeutic necessity of disrupting the immunosuppressive niche established by tumors; nevertheless, the mechanisms through which tumors orchestrate a high Treg/CD8⁺ T-cell ratio remain incompletely understood. The heat shock protein 27 (HSP27), also known as HSPB1, belongs to the small molecular weight heat shock protein (HSP) family. HSPB1 has been proven to be overexpressed in colorectal cancer compared to normal intestinal tissue and is strongly associated with the TNM staging of CRC[ 13 , 14 ]. Meanwhile, HSPB1 can also promote the growth of CRC and chemotherapy resistance by inhibiting apoptosis[ 15 , 16 ]. However, it remains to be determined whether HSPB1 shapes the tumor microenvironment and influences Treg differentiation, as well as Treg/CD8 + T-cell ratio. In this study, we integrated single-cell RNA-seq and spatial transcriptomics to identify HSPB1 as a putative regulator of the intratumoral Treg:CD8⁺ T-cell ratio. Using complementary in vitro and in vivo models, we demonstrated that targeting HSPB1 not only directly curtails tumor growth but also suppresses Treg differentiation and intratumoral infiltration, markedly lowering the Treg:CD8⁺ T-cell ratio and thereby potentiating anti-tumor immunity. Methods Animal Female C57BL/6J (IL-17A GFP / Foxp3 RFP , Foxp3 GFP ) mice were purchased from the Jackson Laboratory[ 17 ]. All experimental mice were housed at the Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, maintained under specific pathogen-free (SPF) conditions with a 12-hour light/dark cycle. Mice at the age of 6–8 weeks were used for subcutaneous tumor modeling or for the extraction of naïve CD4 + T cells. The animal experimental ethics approval number is ACE-003-2025. Human Samples The collection of peripheral blood from healthy individuals was approved by the Ethics Committee of Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, with the approval number 2024-58. All participants provided written informed consent prior to participation in the study. Cell culture The human colorectal cancer cell lines (HCT116, SW480, SW620, DLD1, HT29, RKO), the murine colorectal cancer cell line MC38, and the human colorectal cell line NCM460 were obtained from the Cell Bank of the Chinese Academy of Sciences. Cells were cultured in a sterile incubator at 37°C with 5% CO₂, using DMEM or RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS) (FSD500, Excell) and 1% penicillin-streptomycin (15140122, Gibco). Cell passage and cryopreservation were performed in accordance with the protocols of the American Type Culture Collection (ATCC). Th17 and Treg Differentiation Lymphocytes were initially harvested from murine spleens and subjected to erythrocyte lysis. Subsequently, T cells were enriched via nylon wool columns. The collected T cells were further sorted and purified using an auto magnetic cell sorter (MACS) (Miltenyi Biotec, Germany) to obtain naïve CD4 + T cells. During negative selection, cells were incubated with biotin-conjugated antibodies against CD8, CD25, B220, CD11b, CD11c, and CD49b, along with anti-biotin microbeads. For positive selection, cells were incubated with anti-CD62L microbeads. The purity of the isolated naïve CD4 + T cells was confirmed by flow cytometry, with a purity exceeding 95%. These highly purified naïve CD4 + T cells were subsequently utilized for the induction of Tregs or Th17 cells as previously reported[ 18 , 19 ]. For the induction of Tregs, naïve CD4 + T cells were cultured in 96-well plates with anti-CD3/CD28–coated beads, recombinant human IL-2 (rhIL-2; 50 U/mL), and recombinant human TGF-β (rhTGF-β; 2 ng/mL) for 3 days. To induce Th17 cell differentiation, the culture was supplemented with anti-CD3/CD28 beads, recombinant murine IL-6 (rmIL-6; 20 ng/mL), recombinant human TGF-β (rhTGF-β; 2 ng/mL), anti-IL-4 (5 µg/mL), and anti-IFN-γ (5 µg/mL) for three days. Transwell experiment For the tumor cell migration assay, 50,000 serum-starved tumor cells were resuspended in 200 µL of serum-free medium and evenly seeded onto a Transwell insert (353097, Corning). The insert was then placed into a well containing 700 µL of complete medium with 10% FBS. After 3 days of incubation, non-migrated cells on the upper surface of the insert were gently removed with a cotton swab. The migrated cells were fixed with 4% paraformaldehyde, stained with crystal violet, and subsequently observed and counted under a microscope. For the Treg migration assay, 100,000 tumor cells were seeded into each well of a 24-well plate one day in advance. Subsequently, 200 µL of suspension containing 2 million Tregs was added to the Transwell insert. After 8 hours of incubation, the migrated cells in the lower chamber were collected and counted. CRISPR/Cas9 knockout The plasmids used for lentivirus packaging were the sgRNA plasmid (lentiCRISPRv2) and packaging plasmids (psPAX2 and pMD2.G). The target cells were infected with the virus suspension containing Polybrene (8 µg/mL) for 24 hours, after which the medium was replaced with fresh culture medium. At 48 hours post-infection, the cells were cultured in a selection medium containing 5 µg/mL puromycin for 3 to 7 days, until all uninfected control cells had died. Knockout efficiency was determined through Western blotting. homo hspb1 sgrna1: GUCCCCUUCUCGCUCCUGCG; homo hspb1 sgrna2: UGGUCGAAGAGGCGGCUAUG; Mus hspb1 sgrna: CGAGAAGGGCACGCGGCGCU. CCK-8 and plate cloning experiment In a 96-well plate, 1000 cells were seeded in 100 µL of complete culture medium per well. Each day at a fixed time, 10 µL of CCK-8 (C0038, Beyotime Biotechnology) reagent was added to each well, followed by a 2-hour incubation period. Absorbance was then measured at 450 nm. For the plate colony formation assay, 500 cells were seeded in 2 mL of complete culture medium in each well of a 6-well plate. The medium was refreshed every 3–4 days. After 15 days, the cells were fixed with 4% paraformaldehyde and stained with crystal violet. Quantitative real-time RT-PCR Total RNA extraction was performed using TRIzol reagent (Invitrogen). Reverse transcription was subsequently carried out using the HiScript II 1st Strand cDNA Synthesis Kit (R412-01, Vazyme). For quantitative real-time PCR (qRT-PCR), the ChamQ Universal SYBR qPCR Master Mix (Q711-02, Vazyme) was employed. The sequences of the primers used in the experiments are as follows: mCcl20 -F: GCCTCTCGTACATACAGACGC; mCcl20 -R: CCAGTTCTGCTTTGGATCAGC; m18s rRNA -F: GTAACCCGTTGAACCCCATT m18s rRNA -R: CCATCCAATCGGTAGTAGCG hCCL20-F: TGCTGTACCAAGAGTTTGCTC; hCCL20-R: CGCACACAGACAACTTTTTCTTT; hHSPB1-F: CGGAAATACACGCTGCCCC; hHSPB1-R: TCGAAGGTGACTGGGATGGT; hGAPDH-F: TGCACCACCAACTGCTTAGC; hGAPDH-R: GGCATGGACTGTGGTCATGAG. Western blot Cell Lysis Buffer for Western and IP (P0027, Beyotime Biotechnology) was used to lyse the protein samples. After lysis, the protein concentration was determined using the BeyoBCA Rapid Protein Assay Kit (P0398S, Beyotime Biotechnology) and normalized to ensure equal protein loading. The protein samples were subjected to SDS-PAGE electrophoresis and transferred onto a PVDF membrane. The membrane was blocked with 5% non-fat milk at room temperature for 1 hour and then incubated with the designated primary antibody at 4°C overnight. After incubation, the membrane was washed three times with TBST. Finally, the membrane was incubated with HRP-conjugated secondary antibody at room temperature for 1 hour. The antibodies employed in this study are detailed as follows: Beta Actin polyclonal antibody (20536-1-AP, ProteinTech), HSP27 monoclonal antibody (66767-1-Ig, ProteinTech), CCL20 recombinant antibody (84413-3-RR, ProteinTech), HRP-goat anti-mouse recombinant secondary antibody (RGAM001, ProteinTech), and HRP-goat anti-rabbit recombinant secondary antibody (RGAR001, ProteinTech). RNA sequencing Total RNA was extracted from 1–2 × 10⁶ cells with TRIzol, DNase-treated, quantified on a Qubit HS and quality-checked on a Bioanalyzer (RIN ≥ 7); 200 ng was used to prepare strand-specific, poly(A)-enriched libraries with the VAHTS Universal V6 RNA-seq Library Prep Kit for MGI® and sequenced on DNBSEQ-T7. Reads were adaptor- and quality-trimmed with fastp, aligned to GRCh38 via STAR, counted with featureCounts against GENCODE v38, and differential expression and GO/KEGG enrichment were computed with DESeq2 and clusterProfiler in R (FDR < 0.05). Flow cytometry For cell surface antigen staining, related antibodies for different antigens were diluted with phosphate buffered saline (PBS) at 1:200, and cells were resuspended in 100 µL dilution and stained for 30 min in the dark at 4°C. The cells were washed with PBS twice and then resuspended with 500 µL of PBS followed by detecting the expression of related markers by flow cytometry immediately. For intranuclear Foxp3 staining, the BioLegend Foxp3 Fix/Perm Kit (424401, BioLegend) was utilized, and the procedure was carried out according to the manufacturer's instructions. For intracellular cytokine staining, cells were stimulated with 25 ng/mL PMA (P8139, Sigma) and 5 µM ionomycin (50401ES03, YESEN). Single-cell RNA-seq data acquisition and analysis The single-cell RNA-seq dataset GSE132465 was retrieved from the GEO repository. It comprises 23 tumor samples and matched normal tissues from 10 patients. Data were processed and cell types were annotated with the Seurat package following the pipeline described by the original authors [ 20 ]. Tumor samples were ranked according to their intratumural Treg:CD8⁺ T-cell ratio. The six samples with the highest and the six with the lowest ratios were designated Treg:CD8⁺ T high and low groups, respectively. Differentially expressed genes (DEGs) in malignant cells between these two groups were identified using the FindMarkers function after pseudobulking. Cell–cell communication between malignant cells and Tregs was inferred with CellChat. Spatial transcriptomics data acquisition and analysis Spatial transcriptomics data (GSE267401) were downloaded from GEO. Raw UMI count matrices, high-resolution images, spatial coordinates, and scale factors were imported into R with the Seurat package. UMI counts were normalized by regularized negative binomial regression. Dimensionality reduction was performed by PCA on the top 3 000 highly variable genes, followed by non-linear embedding with UMAP. Clustering was carried out using the Louvain algorithm. Cell-type deconvolution of the Visium spots was performed with the RTCD method. TCGA data analysis We downloaded the mRNA expression data for CRC (COAD, READ) from the TCGA data portal. After rigorous quality control, immune cell fractions were estimated with CIBERSORT. A weighted gene co-expression network was constructed with the WGCNA R package. Module eigengenes were correlated with the intratumoral Treg/CD8⁺ T-cell ratio to identify modules most associated with immune-inhibitory phenotypes. Results Identification of HSPB1 as a potential regulator of the tumor immune microenvironment Although regulatory T cells (Tregs) are generally accepted to be enriched in colorectal cancer (CRC), the immune state varies markedly between patients, and Treg abundance alone fails to accurately gauge the extent of immunosuppression[ 11 , 12 ]. Recent studies have shown that the intratumoral Treg:CD8⁺ T-cell ratio more faithfully reflects the degree of immunosuppression and robustly predicts both patient prognosis and responsiveness to immunotherapy[ 11 , 12 ]. However, the mechanisms by which tumors actively shape this ratio remain poorly understood. To systematically identify potential regulators of the Treg:CD8⁺ T-cell ratio in tumor microenvironment, we integrated single-cell RNA sequencing (scRNA-seq) with large-scale bulk transcriptomic data from The Cancer Genome Atlas (TCGA). We re-analyzed a published CRC scRNA-seq dataset (GSE132465; Fig. 1 A, S1A) and confirmed the pronounced accumulation of Tregs within tumor regions (Fig. 1 B). Pairwise comparison further revealed that the Treg:CD8⁺ T-cell ratio was significantly higher in tumors than matched adjacent non-tumoral tissues (Fig. 1 C), suggesting the presence of tumor-intrinsic factors that modulate this balance. To uncover such factors, we stratified samples according to their Treg:CD8⁺ T-cell ratio and designated the six tumors with the highest and lowest ratios as “High ratio” and “Low ratio” two groups, respectively. Differential expression analysis of malignant cells between these two groups yielded a set of candidate genes that may govern the Treg:CD8⁺ T-cell ratio (Fig. 1 D). To augment the power of this discovery pipeline, we leveraged TCGA-COAD bulk RNA-seq data. After estimating immune-cell fractions with CIBERSORT, we performed weighted gene co-expression network analysis (WGCNA) and identified the module whose eigengene exhibited the strongest correlation with the Treg:CD8⁺ T-cell ratio (Fig. 1 E). Intersection of the module genes with the scRNA-seq-derived candidate list refined the pool to 1 high confidence regulators: HSPB1 (Fig. 1 F). Meanwhile, HSPB1 was significantly associated with overall survival in CRC patients (Fig. 1 G). Spatial transcriptomic profiling further revealed that HSPB1-expressing malignant cells were spatially juxtaposed to Tregs (Fig. 1 H), implying a possible direct role for HSPB1 in orchestrating Treg infiltration. Collectively, these data nominate HSPB1 as a putative tumor-intrinsic regulator of the intratumoral Treg:CD8⁺ T-cell ratio. Knocking out HSPB1 in tumor cells inhibits tumor growth and increases the Treg:CD8⁺ T-cell ratio To validate the aforementioned bioinformatics findings, We ablated HSPB1 via CRISPR/Cas9 in the human CRC cell line SW480 and the murine MC38 line (Fig. 2 A, S1D), both of which exhibit high endogenous HSPB1 expression (Fig. S1B, C).CCK-8 and colony-formation assays displayed a marked reduction in proliferative and clonogenic capacity in the knockout lines (Fig. S1E, F), while Transwell assays demonstrated impaired in vitro migratory ability (Fig. S1G), being consistent with previous reports[ 21 ]. To elucidate the in vivo modulation of the immune microenvironment by HSPB1, we established subcutaneous tumor models using parental and HSPB1-deficient MC38 cells (Fig. 3 B). HSPB1 deletion markedly curtailed in vivo tumor growth (Fig. 3 C), and harvested tumors exhibited significantly reduced volumes and weight (Fig. 3 D, E). Flow-cytometric profiling presented a pronounced decrease in the intratumoral Tregs/CD8⁺ T-cell ratio (Fig. 3 G) and a concomitant reduction in the PD-1⁺ Treg/PD-1⁺ CD8⁺ T-cell ratio (Fig. 3 F). These changes stemmed primarily from a marked decline in Tregs infiltration (Fig. 2 H) and an elevated Th1 cells fraction (Fig. 3 M), whereas the proportions of other immune subsets remained unaltered (Fig. S2A-D). Consistent alterations were also observed in draining lymph nodes (Fig. S2E-H). The proportion of exhausted CD8⁺ T cells was significantly reduced (Fig. 3 I-K), accompanied by enhanced cytokine-secretion capacity among CD8⁺ T cells, most notably a marked up-regulation of IFN-γ (Fig. 3 N). Concurrently, CD4⁺ T cells exhibited a pronounced increase in IL-2 production (Fig. 3 L). Collectively, these data demonstrate that genetic ablation of HSPB1 in tumor cells not only restrains tumor growth but also reduces the intratumoral Treg:CD8⁺ T-cell ratio, thereby dismantling Treg-mediated immunosuppression. Targeting HSPB1 effectively dismantles the Treg-orchestrated immunosuppressive microenvironment Furthermore, we tested whether small molecule inhibitor-J2 targeting HSPB1 could inhibit tumor growth in mouse models. (Fig. 3 A, B). After eight consecutive doses, tumors in the inhibitor-treated cohort were markedly smaller and lighter than controls (Fig. 3 C, D). Flow-cytometric analysis had a pronounced decrease in both the total Treg/CD8⁺ T-cell ratio and the PD-1⁺ Treg/PD-1⁺ CD8⁺ T-cell ratio (Fig. 3 E, F), driven primarily by a sharp reduction in intratumoral Tregs (Fig. 3 H). This is accompanied by significant increases in the frequencies of Th1 and Th17 populations (Fig. 3 N, O). Therefore, the function of anti-tumor CD8 + T cells has been significantly enhanced, displayed robust up-regulation of IFN-γ, granzyme B, TNF-α, and perforin expression (Fig. 3 H–K), while IL-2 secretion by CD4⁺ and CD8⁺ T cells was similarly heightened (Fig. 3 L–M). Collectively, these data demonstrate that HSPB1-targeted therapy substantially remodels the intratumoral immune milieu, effectively disrupting Treg-mediated immunosuppression. Targeting HSPB1 selectively suppresses Treg differentiation without impairing Th17 It is well established that intratumoral Tregs are not only recruited via chemokine gradients but also generated through microenvironmental induction[ 5 ]. We observed that pharmacological inhibition of HSPB1 yielded a more potent therapeutic effect than genetic deletion of HSPB1 in tumor cells alone, prompting us to investigate whether the inhibitor might additionally act on immune compartments to directly suppress Treg induction. To test this hypothesis, we added the HSPB1 inhibitor-J2 to an in vitro Treg-polarizing culture system as we previously established and monitored its impact on Treg differentiation efficiency[ 22 ]. As expected, HSPB1 inhibitor-J2 suppressed Treg induction in a dose-dependent manner (Fig. 4 A, B). Th17 cells, although also differentiated under TGF-β signaling, functionally and phenotypically antagonize Tregs[ 23 , 24 ]. To determine whether this inhibitory effect was selective for Tregs, we next examined cultures driven toward the Th17 lineage. Under these settings, J2 dose-dependently curtailed Tregs differentiation without altering Th17 commitment (Fig. 4 C-E). Collectively, these findings demonstrate that HSPB1 blockade exerts a dual mechanism of action: it simultaneously suppresses tumor-cell proliferation and also directly impairs Treg differentiation while leaving pro-inflammatory Th17 cells intact. HSPB1 up-regulates CCL20 to drive CCR6⁺ Treg migration and intratumoral infiltration To elucidate the mechanism by which HSPB1 orchestrates tumor cell–Tregs crosstalk, we performed transcriptomic sequencing on HSPB1-knockdown and control SW480 cells to delineate downstream oncogenic pathways. Subsequently, we observed that HSPB1-deficient (KO) cells exhibited down-regulated expression of immunosuppressive cytokines (e.g., IL-24, SAA2, and TSLP). Notably, CCL20 levels were also markedly diminished in HSPB1-KO cells. (Fig. 5 A). Consistent with this, KEGG pathway enrichment manifested a marked suppression of cytokine–cytokine receptor signaling (Fig. 5 B). CCL20 functions as a dual-effector in cancers, orchestrating both tumor-promoting and immunosuppressive programs through two converging mechanisms: autocrine stimulation of cancer cell proliferation and migration, and microenvironmental recruitment of Tregs. Given that CCL20 is upregulated in CRC [ 25 ] (Fig. 5 C), we hypothesized that HSPB1 may increase Treg infiltration in the tumor microenvironment by regulating CCL20 expression. Western blotting confirmed that HSPB1 knockout markedly reduced CCL20 expression at the protein level (Fig. 5 D). Subsequent Transwell assays further demonstrated that HSPB1 deletion significantly attenuated the tumor-derived chemotaxis of Tregs (Fig. 5 E). CCR6, the cognate receptor for CCL20, is widely documented to be highly expressed on Tregs[ 26 ]. In vivo , both HSPB1-knockout tumors and inhibitor-treated tumors exhibited a marked reduction in the proportion of CCR6⁺ Tregs (Fig. 5 F, G). Taken together, these data establish that HSPB1 up-regulates CCL20 to facilitate CCR6⁺ Treg migration and intratumoral infiltration. Targeting HSPB1 in CRC concurrently suppresses intrinsic tumor growth and CCL20-mediated Treg recruitment, while also attenuating intratumoral Treg induction, thereby exerting a dual antitumor effect (Fig. 6 ). Discussion Since immune-checkpoint blockade revolutionized melanoma treatment, immunotherapy has been translated to numerous solid malignancies; yet, objective response rates in CRC remain stubbornly low [ 27 – 29 ]. A systems-level deconstruction of the CRC immunophenotypic landscape especially the dynamic dialogue between neoplastic and immune cells is therefore essential for the rational design of combination strategies [ 27 , 30 ]. Although tumor-infiltrating Tregs are recognized as principal mediators of immunosuppression and predictors of poor outcome, their absolute abundance alone inadequately captures patient-specific immune status or the likelihood of response to checkpoint blockade. Instead, the Treg:CD8⁺ T-cell ratio emerges as a more precise barometer of intratumoral immunosuppression and a reliable biomarker of immunotherapeutic efficacy[ 11 , 12 ]. These observations implicate the molecular circuitry that governs the Treg/CD8⁺ T-cell balance as a pivotal, yet underexploited, determinant of immunotherapy responsiveness in CRC. By integrating single-cell RNA-seq, spatial transcriptomics and TCGA bulk data through a cross-platform pipeline, we identified HSPB1 as the first druggable regulator of the intratumoral Treg:CD8⁺ T-cell ratio (Fig. 1 ). Notably, HSPB1 is already targeted by inhibitors that have entered phase II trials for metastatic urothelial carcinoma, providing an immediate translational path to CRC therapy[ 31 – 33 ]. HSPB1 is an ATP-independent molecular chaperone that is normally expressed at low basal levels but is rapidly induced under stress to modulate cell-death pathways.[ 34 ]. However, it is aberrantly and ubiquitously overexpressed in tumors, especially in CRC [ 35 , 36 ]. Under oxidative stress, elevated HSPB1 promotes glutathione upregulation and intracellular iron depletion, reducing reactive oxygen species[ 37 ]. Indeed, iron overload affects the development of inflammatory diseases and cancers[ 38 , 39 ]. Moreover, in CRC, HSPB1 also drives epithelial-mesenchymal transition, enhancing tumor proliferation and metastasis[ 21 ]. This conclusion was corroborated in our HSPB1-knockout cell lines (Fig. S1E-G). Although the cell-autonomous functions of HSPB1 in neoplastic cells are well established, its contribution to the sculpting of the tumor microenvironment remains to be explored[ 40 ]. Here we demonstrate that pharmacologic or genetic blockade of HSPB1 concurrently suppresses tumor-cell proliferation, curtails Treg infiltration, and halts Treg differentiation collectively lowering the intratumoral Treg/CD8⁺ T-cell ratio and dismantling a pivotal axis of tumor-driven immunosuppression. Thus, unleash the antitumor immunity, the cytokine-secretion capacity of CD8⁺ T cells were markedly augmented and infiltration of Th1 and Th17 subsets was significantly increased. In the tumor microenvironment, cancer cells secrete various chemokines to recruit and reprogram immune cells, facilitating immune evasion [ 41 ]. Intratumoral Treg cell responses are enhanced by chemokines, such as the CCR6-CCL20 axis, which is significantly overexpressed in CRC[ 42 , 43 ]. Our findings reveal that tumor-derived HSPB1 transcriptionally up-regulates CCL20, thereby recruiting CCR6⁺ Tregs and orchestrating the immunosuppressive tumor microenvironment (Fig. 5 F, G). Nevertheless, several mechanistic gaps remain to be addressed. Specifically, the mechanisms by which HSPB1 suppresses Treg differentiation and modulates CCL20 expression in tumor cells require further investigation. Collectively, we have identified a previously unrecognized function of HSPB1 within the tumor microenvironment, highlighting its potential for bolstering antitumor immunity and informing future combinatorial immunotherapeutic strategies (Fig. 6 ). Conclusions Pharmacologic or genetic inhibition of HSPB1 simultaneously restrains tumor growth, curtails intratumoral Treg infiltration, and suppresses Treg induction. These findings establish HSPB1 as a promising novel immunotherapeutic target in colorectal cancer. Abbreviations CRC Colorectal cancer scRNA-seq single-cell RNA sequencing Tregs CD4 + regulatory T cells HSP27 The heat shock protein 27 HSP Heat shock protein SPF Specific pathogen free FBS Fetal bovine serum ATCC American Type Culture Collection MACS Auto magnetic cell sorter PBS Phosphate buffered saline DEGs Differentially expressed genes COAD Colon adenocarcinoma WGCNA weighted gene co-expression network analysis Declarations Ethics approval and consent to participate All animal procedures were approved by the Institutional Animal Care and Use Committee of Songjiang Hospital, Shanghai Jiao Tong University School of Medicine (protocol ACE-003-2025). Peripheral blood from healthy donors was collected at Songjiang Hospital, Shanghai Jiao Tong University School of Medicine, under Ethics Approval No. 2024-58. Consent for publication Not applicable. Availability of data and materials Single-cell RNA-seq data were obtained from GEO accession GSE132465, and spatial transcriptomic data from GSE267401. Bulk RNA-seq data generated in this study have been deposited in the GEO database under accession GSE305561. Competing interests All authors declare to have no conflicts of interest relevant to this study. Funding The funding sources supporting this study are listed below: Huadu District People’s Hospital of Guangzhou Institutional Research Fund Project, Expert research project ZJXM202501, ZJXM202502; Talent Development Foundation of The First Dongguan Affiliated Hospital of Guangdong Medical University& Foundation of State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia SKL-HIDCA-2024-GD3B; Key technological projects in Songjiang District, Shanghai. Authors' contributions Qi Hu and Yang Lu have equal contributions to this work and are recognized as co-first authors. Song Guo Zheng, Wanlin Li and Binghua Jiang conceived the study, supervised overall design, and critically revised the manuscript. Qi Hu and Yang Lu performed all in vitro and in vivo experiments, analyzed the data, and drafted the manuscript. Peng Huang and Xiaolan Zhong oversaw experimental reagents and financial management. Lisheng Zheng supervised and executed the statistical analysis of all experimental data. Acknowledgements Not applicable. References Kim JC, Bodmer WF. Genomic landscape of colorectal carcinogenesis. J Cancer Res Clin Oncol. 2022;148(3):533–45. Guinney J, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350–6. Zheng L, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science. 2021;374(6574):abe6474. Chu X, et al. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. Nat Cancer. 2024;5(9):1409–26. Lee HO, et al. 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Hsp27 consolidates intracellular redox homeostasis by upholding glutathione in its reduced form and by decreasing iron intracellular levels. Antioxid Redox Signal. 2005;7(3–4):414–22. Liu Y, et al. Heterogeneous ferroptosis susceptibility of macrophages caused by focal iron overload exacerbates rheumatoid arthritis. Redox Biol. 2024;69:103008. Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266–82. Hirano S, Shelden EA, Gilmont RR. HSP27 regulates fibroblast adhesion, motility, and matrix contraction. Cell Stress Chaperones. 2004;9(1):29–37. Ozga AJ, Chow MT, Luster AD. Chemokines and the immune response to cancer. Immunity. 2021;54(5):859–74. Liu Q, et al. CCL20-CCR6 signaling in tumor microenvironment: Functional roles, mechanisms, and immunotherapy targeting. Biochim Biophys Acta Rev Cancer. 2025;1880(3):189341. Frick VO, et al. Chemokine/chemokine receptor pair CCL20/CCR6 in human colorectal malignancy: An overview. World J Gastroenterol. 2016;22(2):833–41. Supplementary Files OnlineSUPfig1.png Sup Figure 1. HSPB1 knockout suppresses colorectal cancer growth and metastasis. (A) Dotplot shows the marker genes expression; (B) Western blot shows the HSPB1 protein level of several cell lines and (C) Quantitative analysis of HSPB1 and β-actin band intensities by densitometry; (D) Western blot shows the HSPB1 protein level of SW480.NC and SW480.sg HSPB1 ; (E-G) CCK-8 assay shows the proliferation rate(E), clone formation(F) and evasion ability(G) of SW480.NC and SW480.sg HSPB1 . Data are represented as mean with SEM, and (E) were analyzed using two-way ANOVA, while the remaining data were analyzed using one-way ANOVA, *: P<0.05, ****: P<0.0001. Onlinesupfig2.png Sup Figure 2. HSPB1 knockout does not alter immune cell profiles in the tumor or draining lymph nodes.(A-D) The proportions of tumor-infiltrating B cells (A), NK cells (B), NKT cells (C), and macrophages (D) in tumor-bearing mice; (E-H) The proportions of B cells (E), NK cells (F), T cells (G), and DC cells (H) in tumor-draining lymph nodes of tumor-bearing mice. Data are represented as mean with SEM, data were analyzed using unpaired t-tests, ns: no significance. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7382643","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506393175,"identity":"66e167dc-5f71-4cfe-87cb-7e733827bb91","order_by":0,"name":"Songguo Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC+QYgwfjnAJBkPsAMFTTAq8UApJiBB0SyJRCphQGuhceASC3svYdfMEjckTPnX/P5c8GfbYkN7M3bJBhq7uDUIt9zLs2CweCZseWMt9ukZ7bdTmzgOVYmwXDsGW5rbuSYGTAkHE7ccOPsNmbeBqAWiRwzCcaGwwS0HDhcv+HGmcefef4Atci/IajF+AFQQYLB+R4GaR42kC08+LUYnDljxpDYcNhwww02M2nettvGbTxpxRYJx3BrkW/vMf7w8c9heYPzh8EOk+1nP7zxxocaPA4DRqFEAoiCkEAuiEjApwGYUD6AKf4D+JWNglEwCkbByAUAuBRdti/fr4wAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5611-4774","institution":"Shanghai Songjiang District Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Songguo","middleName":"","lastName":"Zheng","suffix":""},{"id":506393176,"identity":"659fc457-61fd-41a3-ab70-b05965d32d9e","order_by":1,"name":"Qi Hu","email":"","orcid":"","institution":"Huadu District People's Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Hu","suffix":""},{"id":506393177,"identity":"b09d720f-bfa7-4642-85c1-6348393ef64a","order_by":2,"name":"Yang Lu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lu","suffix":""},{"id":506393178,"identity":"87a88148-4863-45bc-bc1c-f3446526a792","order_by":3,"name":"Xiaolan Zhong","email":"","orcid":"","institution":"Huadu District People's Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Xiaolan","middleName":"","lastName":"Zhong","suffix":""},{"id":506393179,"identity":"9adacf59-1c37-4e33-92b5-70ebd3824995","order_by":4,"name":"Peng Huang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Huang","suffix":""},{"id":506393180,"identity":"d786b78b-f9e6-46e3-93cf-729dd3aae991","order_by":5,"name":"Lisheng Zheng","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lisheng","middleName":"","lastName":"Zheng","suffix":""},{"id":506393181,"identity":"7a9de2d7-6cf1-44ae-a4f7-71cceddd95c0","order_by":6,"name":"Bing-Hua Jiang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Bing-Hua","middleName":"","lastName":"Jiang","suffix":""},{"id":506393182,"identity":"98a246d7-8786-4abe-a588-392e4a6c37e0","order_by":7,"name":"Wanlin Li","email":"","orcid":"","institution":"Huadu District People's Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Wanlin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-15 15:44:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7382643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7382643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90528837,"identity":"fa002fb3-8eee-462a-aeeb-f2ced62d746d","added_by":"auto","created_at":"2025-09-03 17:44:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2206614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated multi-omics analysis identifies HSPB1 as a potential regulator of the Treg:CD8 ratio in CRC. \u003c/strong\u003e(A) UMAP plot of CRC cells; (B) Proportion of cell subtypes between normal and tumor samples; (C) The ratio of Treg to CD8\u003csup\u003e+\u003c/sup\u003e T cells in paired normal and tumor samples, paired t test, ** : P \u0026lt;0.01; (D) Volcano plot of differentially expressed genes in epithelial cells from tumor samples with high versus low Treg:CD8⁺ T-cell ratios, genes with P value \u0026lt;0.05 and Fold change \u0026gt;2 are considered DEGs; (E) Pearson correlation analysis of merged modules and Treg:CD8\u003csup\u003e+\u003c/sup\u003eT ratio in TCGA-COAD; (F) Veen plot show the common genes of DEGs from (D) and modules genes from (E); (G) K-M plot show the overall survival of TCGA-COAD cohort based on the HSPB1 expression; (H) spatial transcriptomics show the colocation of HSPB1 and Treg.\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/1b4c57633474db4f3d0b2a6c.png"},{"id":90528277,"identity":"8bcda081-8c24-4e10-b5d4-c8653e29cc20","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2854594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnockout of HSP27 in Tumor Cells Abrogates the Treg-Mediated Immunosuppressive Tumor Microenvironment. \u003c/strong\u003e(A) Western blot shows the protein level of HSPB1 in MC38.NC and MC38.sg; (B) Experimental procedure flowchart；(C-E) Tumor volume curve (C), physical photographs (D), and tumor weight (E); (F) Treg:CD8\u003csup\u003e+\u003c/sup\u003eT cell ratio of NC and sg Hspb1 tumor; (G) PD-1\u003csup\u003e+\u003c/sup\u003e Treg: PD-1\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cell ratio of NC and sg Hspb1 tumor; (H) The proportion of Tregs in TILs; (I-K) The expression of exhaustion markers in CD8\u003csup\u003e+\u003c/sup\u003eT cells; (L-N) Cytokine production of TIL after PMA/ion stimulation. Data are represented as mean with SEM, and data in (C) were analyzed using two-way ANOVA, while the remaining data were analyzed using unpaired t-tests, *: P\u0026lt;0.05, **: P\u0026lt;0.01, ***: P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/65101195d1be5e167cc23c66.png"},{"id":90528268,"identity":"fe62c17b-0749-47cb-a21c-815e2b1560c1","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2769553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeted Inhibition of HSP27 Dismantles the Treg-Mediated Immunosuppressive Tumor Microenvironment. \u003c/strong\u003e(A) Experimental procedure flowchart；(B-D) Tumor volume curve (B), tumor weight (C), and physical photographs (D); (E) PD-1\u003csup\u003e+\u003c/sup\u003e Treg: PD-1\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cell ratio of NC and sg Hspb1 tumor; (F) Treg:CD8\u003csup\u003e+\u003c/sup\u003eT cell ratio of NC and sg Hspb1 tumor; (G) The proportion of Tregs in TILs; (H-O) Cytokine production of TIL after PMA/ion stimulation. Data are represented as mean with SEM, and data in (B) were analyzed using two-way ANOVA, while the remaining data were analyzed using unpaired t-tests, *: P\u0026lt;0.05, **: P\u0026lt;0.01, ***: P\u0026lt;0.001, ****: P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/381f08355c9f6ef5533aa7d4.png"},{"id":90528267,"identity":"47e356c3-1c6a-4590-832e-3813e61da47c","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1684655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of HSP27 Suppresses Treg Differentiation without Affecting Th17 Differentiation. \u003c/strong\u003e(A, B) Represent FACS graphs and statistic plots show Treg percent under HSPB1 inhibitor treatment; (C-E) Represent FACS graphs and statistic plots show Treg and Th17 percent under HSPB1 inhibitor treatment. Data are represented as mean with SEM, and data were analyzed using unpaired t-tests, ns: no significance, ***: P\u0026lt;0.001, ****: P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/05a2a92a5aaaa9857d8e270d.png"},{"id":90528270,"identity":"d4ea0243-24fc-424e-aab2-5ad7691e54f9","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2128847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHSP27 Increases Treg Infiltration in Tumors via CCL20\u003c/strong\u003e. (A) Heatmap shows the DEGs between SW480.nc and SW480.sg \u003cem\u003eHSPB1\u003c/em\u003e; (B) Dotplot shows KEGG enrichment of DEGs between SW480.nc and SW480.sg \u003cem\u003eHSPB1\u003c/em\u003e; (C) Differential ligand-receptor expression between Treg:CD8\u003csup\u003e+\u003c/sup\u003eT ratio high and low group; (D) Western bolt shows the HSPB1 and CCL20 protein level between NC and HSPB1 knockdown cells; (E) Schematic of the Transwell assay and statistic plots of Treg cells that migrated to the lower chamber. Data are represented as mean with SEM, and (E) were analyzed using one-way ANOVA, while the remaining data were analyzed using unpaired t-tests, ns: no significance, *: P\u0026lt;0.05, **: P\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/b001a9df3d5d2853ff6ae1cf.png"},{"id":90528273,"identity":"855b681f-8780-4a60-8cee-082236efd152","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":870616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression. \u003c/strong\u003eIn tumors with high HSPB1 expression, rapid tumor-cell proliferation is accompanied by abundant CCL20 secretion that recruits large numbers of Treg cells, elevating the intratumoral Treg: CD8⁺ T-cell ratio and establishing an immunosuppressive microenvironment. Targeted HSPB1 inhibition suppresses tumor growth, blocks Treg recruitment and differentiation, disrupts Treg mediated immunosuppression, and ultimately converts a “cold” tumor into a “hot” one.\u003c/p\u003e","description":"","filename":"Onlinefig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/43db2d25cc0c9a51dfa883d1.png"},{"id":93846866,"identity":"3474847b-4994-4532-9b0e-f6837bb414e4","added_by":"auto","created_at":"2025-10-18 15:40:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4380569,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/c3caa4c3-53b0-4524-8f43-398ff1f31510.pdf"},{"id":90528275,"identity":"cdb1a04b-9e44-4c7d-a9a5-fcd8d0ec9a69","added_by":"auto","created_at":"2025-09-03 17:36:47","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3895077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSup Figure 1. HSPB1 knockout suppresses colorectal cancer growth and metastasis. \u003c/strong\u003e(A) Dotplot shows the marker genes expression; (B) Western blot shows the HSPB1 protein level of several cell lines and (C) Quantitative analysis of HSPB1 and β-actin band intensities by densitometry; (D) Western blot shows the HSPB1 protein level of SW480.NC and SW480.sg \u003cem\u003eHSPB1\u003c/em\u003e; (E-G) CCK-8 assay shows the proliferation rate(E), clone formation(F) and evasion ability(G) of SW480.NC and SW480.sg \u003cem\u003eHSPB1\u003c/em\u003e. Data are represented as mean with SEM, and (E) were analyzed using two-way ANOVA, while the remaining data were analyzed using one-way ANOVA, *: P\u0026lt;0.05, ****: P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"OnlineSUPfig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/4f5c0704e58f174736b9f2e4.png"},{"id":90528836,"identity":"ba0e41a0-53a9-402b-88d8-34c8037eb2ba","added_by":"auto","created_at":"2025-09-03 17:44:47","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":365109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSup Figure 2. HSPB1 knockout does not alter immune cell profiles in the tumor or draining lymph nodes.\u003c/strong\u003e(A-D) The proportions of tumor-infiltrating B cells (A), NK cells (B), NKT cells (C), and macrophages (D) in tumor-bearing mice; (E-H) The proportions of B cells (E), NK cells (F), T cells (G), and DC cells (H) in tumor-draining lymph nodes of tumor-bearing mice. Data are represented as mean with SEM, data were analyzed using unpaired t-tests, ns: no significance.\u003c/p\u003e","description":"","filename":"Onlinesupfig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7382643/v1/f0306a544ae477037c9638fc.png"}],"financialInterests":"","formattedTitle":"Targeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression","fulltext":[{"header":"Background","content":"\u003cp\u003eColorectal cancer (CRC) ranks the third most prevalent malignancy worldwide and the second leading cause of cancer-related mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although immunotherapy has recently demonstrated somehow efficacy in CRC, the overall response rate remains limited. Consequently, a deeper comprehension of the regulatory mechanisms governing the CRC immune microenvironment is imperative for refining current therapeutic strategies. The advent of single cell RNA sequencing (scRNA-seq) and spatial transcriptomics has enabled unprecedented resolution of the intratumoral immune milieu and has begun to elucidate the intricate crosstalk between neoplastic and immune cells[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e regulatory T cells (Tregs) are indispensable for maintaining peripheral tolerance and preventing autoimmunity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Within the CRC microenvironment, they constitute a dominant immunosuppressive population that curtails anti-tumor immunity and undermines responses to immunotherapy[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Treg/CD8⁺ T-cell ratio not only quantifies the degree of local immunosuppression but also robustly predicts patient prognosis and responsiveness to immune checkpoint blockade[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These observations underscore the therapeutic necessity of disrupting the immunosuppressive niche established by tumors; nevertheless, the mechanisms through which tumors orchestrate a high Treg/CD8⁺ T-cell ratio remain incompletely understood.\u003c/p\u003e\u003cp\u003eThe heat shock protein 27 (HSP27), also known as HSPB1, belongs to the small molecular weight heat shock protein (HSP) family. HSPB1 has been proven to be overexpressed in colorectal cancer compared to normal intestinal tissue and is strongly associated with the TNM staging of CRC[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Meanwhile, HSPB1 can also promote the growth of CRC and chemotherapy resistance by inhibiting apoptosis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, it remains to be determined whether HSPB1 shapes the tumor microenvironment and influences Treg differentiation, as well as Treg/CD8\u0026thinsp;+\u0026thinsp;T-cell ratio.\u003c/p\u003e\u003cp\u003eIn this study, we integrated single-cell RNA-seq and spatial transcriptomics to identify HSPB1 as a putative regulator of the intratumoral Treg:CD8⁺ T-cell ratio. Using complementary \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e models, we demonstrated that targeting HSPB1 not only directly curtails tumor growth but also suppresses Treg differentiation and intratumoral infiltration, markedly lowering the Treg:CD8⁺ T-cell ratio and thereby potentiating anti-tumor immunity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimal\u003c/h2\u003e\u003cp\u003eFemale C57BL/6J (IL-17A\u003csup\u003eGFP\u003c/sup\u003e/ Foxp3\u003csup\u003eRFP\u003c/sup\u003e, Foxp3\u003csup\u003eGFP\u003c/sup\u003e) mice were purchased from the Jackson Laboratory[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. All experimental mice were housed at the Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, maintained under specific pathogen-free (SPF) conditions with a 12-hour light/dark cycle. Mice at the age of 6\u0026ndash;8 weeks were used for subcutaneous tumor modeling or for the extraction of na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells. The animal experimental ethics approval number is ACE-003-2025.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHuman Samples\u003c/h3\u003e\n\u003cp\u003e The collection of peripheral blood from healthy individuals was approved by the Ethics Committee of Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, with the approval number 2024-58. All participants provided written informed consent prior to participation in the study.\u003c/p\u003e\n\u003ch3\u003eCell culture\u003c/h3\u003e\n\u003cp\u003eThe human colorectal cancer cell lines (HCT116, SW480, SW620, DLD1, HT29, RKO), the murine colorectal cancer cell line MC38, and the human colorectal cell line NCM460 were obtained from the Cell Bank of the Chinese Academy of Sciences. Cells were cultured in a sterile incubator at 37\u0026deg;C with 5% CO₂, using DMEM or RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS) (FSD500, Excell) and 1% penicillin-streptomycin (15140122, Gibco). Cell passage and cryopreservation were performed in accordance with the protocols of the American Type Culture Collection (ATCC).\u003c/p\u003e\n\u003ch3\u003eTh17 and Treg Differentiation\u003c/h3\u003e\n\u003cp\u003eLymphocytes were initially harvested from murine spleens and subjected to erythrocyte lysis. Subsequently, T cells were enriched \u003cem\u003evia\u003c/em\u003e nylon wool columns. The collected T cells were further sorted and purified using an auto magnetic cell sorter (MACS) (Miltenyi Biotec, Germany) to obtain na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells. During negative selection, cells were incubated with biotin-conjugated antibodies against CD8, CD25, B220, CD11b, CD11c, and CD49b, along with anti-biotin microbeads. For positive selection, cells were incubated with anti-CD62L microbeads. The purity of the isolated na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells was confirmed by flow cytometry, with a purity exceeding 95%. These highly purified na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells were subsequently utilized for the induction of Tregs or Th17 cells as previously reported[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For the induction of Tregs, na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells were cultured in 96-well plates with anti-CD3/CD28\u0026ndash;coated beads, recombinant human IL-2 (rhIL-2; 50 U/mL), and recombinant human TGF-β (rhTGF-β; 2 ng/mL) for 3 days. To induce Th17 cell differentiation, the culture was supplemented with anti-CD3/CD28 beads, recombinant murine IL-6 (rmIL-6; 20 ng/mL), recombinant human TGF-β (rhTGF-β; 2 ng/mL), anti-IL-4 (5 \u0026micro;g/mL), and anti-IFN-γ (5 \u0026micro;g/mL) for three days.\u003c/p\u003e\n\u003ch3\u003eTranswell experiment\u003c/h3\u003e\n\u003cp\u003eFor the tumor cell migration assay, 50,000 serum-starved tumor cells were resuspended in 200 \u0026micro;L of serum-free medium and evenly seeded onto a Transwell insert (353097, Corning). The insert was then placed into a well containing 700 \u0026micro;L of complete medium with 10% FBS. After 3 days of incubation, non-migrated cells on the upper surface of the insert were gently removed with a cotton swab. The migrated cells were fixed with 4% paraformaldehyde, stained with crystal violet, and subsequently observed and counted under a microscope. For the Treg migration assay, 100,000 tumor cells were seeded into each well of a 24-well plate one day in advance. Subsequently, 200 \u0026micro;L of suspension containing 2\u0026nbsp;million Tregs was added to the Transwell insert. After 8 hours of incubation, the migrated cells in the lower chamber were collected and counted.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCRISPR/Cas9 knockout\u003c/h2\u003e\u003cp\u003eThe plasmids used for lentivirus packaging were the sgRNA plasmid (lentiCRISPRv2) and packaging plasmids (psPAX2 and pMD2.G). The target cells were infected with the virus suspension containing Polybrene (8 \u0026micro;g/mL) for 24 hours, after which the medium was replaced with fresh culture medium. At 48 hours post-infection, the cells were cultured in a selection medium containing 5 \u0026micro;g/mL puromycin for 3 to 7 days, until all uninfected control cells had died. Knockout efficiency was determined through Western blotting.\u003c/p\u003e\u003cp\u003ehomo hspb1 sgrna1: GUCCCCUUCUCGCUCCUGCG;\u003c/p\u003e\u003cp\u003ehomo hspb1 sgrna2: UGGUCGAAGAGGCGGCUAUG;\u003c/p\u003e\u003cp\u003eMus hspb1 sgrna: CGAGAAGGGCACGCGGCGCU.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCCK-8 and plate cloning experiment\u003c/h3\u003e\n\u003cp\u003eIn a 96-well plate, 1000 cells were seeded in 100 \u0026micro;L of complete culture medium per well. Each day at a fixed time, 10 \u0026micro;L of CCK-8 (C0038, Beyotime Biotechnology) reagent was added to each well, followed by a 2-hour incubation period. Absorbance was then measured at 450 nm. For the plate colony formation assay, 500 cells were seeded in 2 mL of complete culture medium in each well of a 6-well plate. The medium was refreshed every 3\u0026ndash;4 days. After 15 days, the cells were fixed with 4% paraformaldehyde and stained with crystal violet.\u003c/p\u003e\n\u003ch3\u003eQuantitative real-time RT-PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA extraction was performed using TRIzol reagent (Invitrogen). Reverse transcription was subsequently carried out using the HiScript II 1st Strand cDNA Synthesis Kit (R412-01, Vazyme). For quantitative real-time PCR (qRT-PCR), the ChamQ Universal SYBR qPCR Master Mix (Q711-02, Vazyme) was employed. The sequences of the primers used in the experiments are as follows:\u003c/p\u003e\u003cp\u003e\u003cem\u003emCcl20\u003c/em\u003e-F: GCCTCTCGTACATACAGACGC;\u003c/p\u003e\u003cp\u003e\u003cem\u003emCcl20\u003c/em\u003e-R: CCAGTTCTGCTTTGGATCAGC;\u003c/p\u003e\u003cp\u003e\u003cem\u003em18s rRNA\u003c/em\u003e-F: GTAACCCGTTGAACCCCATT\u003c/p\u003e\u003cp\u003e\u003cem\u003em18s rRNA\u003c/em\u003e-R: CCATCCAATCGGTAGTAGCG\u003c/p\u003e\u003cp\u003ehCCL20-F: TGCTGTACCAAGAGTTTGCTC;\u003c/p\u003e\u003cp\u003ehCCL20-R: CGCACACAGACAACTTTTTCTTT;\u003c/p\u003e\u003cp\u003ehHSPB1-F: CGGAAATACACGCTGCCCC;\u003c/p\u003e\u003cp\u003ehHSPB1-R: TCGAAGGTGACTGGGATGGT;\u003c/p\u003e\u003cp\u003ehGAPDH-F: TGCACCACCAACTGCTTAGC;\u003c/p\u003e\u003cp\u003ehGAPDH-R: GGCATGGACTGTGGTCATGAG.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eWestern blot\u003c/h2\u003e\u003cp\u003eCell Lysis Buffer for Western and IP (P0027, Beyotime Biotechnology) was used to lyse the protein samples. After lysis, the protein concentration was determined using the BeyoBCA Rapid Protein Assay Kit (P0398S, Beyotime Biotechnology) and normalized to ensure equal protein loading. The protein samples were subjected to SDS-PAGE electrophoresis and transferred onto a PVDF membrane. The membrane was blocked with 5% non-fat milk at room temperature for 1 hour and then incubated with the designated primary antibody at 4\u0026deg;C overnight. After incubation, the membrane was washed three times with TBST. Finally, the membrane was incubated with HRP-conjugated secondary antibody at room temperature for 1 hour. The antibodies employed in this study are detailed as follows: Beta Actin polyclonal antibody (20536-1-AP, ProteinTech), HSP27 monoclonal antibody (66767-1-Ig, ProteinTech), CCL20 recombinant antibody (84413-3-RR, ProteinTech), HRP-goat anti-mouse recombinant secondary antibody (RGAM001, ProteinTech), and HRP-goat anti-rabbit recombinant secondary antibody (RGAR001, ProteinTech).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRNA sequencing\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from 1\u0026ndash;2 \u0026times; 10⁶ cells with TRIzol, DNase-treated, quantified on a Qubit HS and quality-checked on a Bioanalyzer (RIN\u0026thinsp;\u0026ge;\u0026thinsp;7); 200 ng was used to prepare strand-specific, poly(A)-enriched libraries with the VAHTS Universal V6 RNA-seq Library Prep Kit for MGI\u0026reg; and sequenced on DNBSEQ-T7. Reads were adaptor- and quality-trimmed with fastp, aligned to GRCh38 via STAR, counted with featureCounts against GENCODE v38, and differential expression and GO/KEGG enrichment were computed with DESeq2 and clusterProfiler in R (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry\u003c/h2\u003e\u003cp\u003eFor cell surface antigen staining, related antibodies for different antigens were diluted with phosphate buffered saline (PBS) at 1:200, and cells were resuspended in 100 \u0026micro;L dilution and stained for 30 min in the dark at 4\u0026deg;C. The cells were washed with PBS twice and then resuspended with 500 \u0026micro;L of PBS followed by detecting the expression of related markers by flow cytometry immediately. For intranuclear Foxp3 staining, the BioLegend Foxp3 Fix/Perm Kit (424401, BioLegend) was utilized, and the procedure was carried out according to the manufacturer's instructions. For intracellular cytokine staining, cells were stimulated with 25 ng/mL PMA (P8139, Sigma) and 5 \u0026micro;M ionomycin (50401ES03, YESEN).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell RNA-seq data acquisition and analysis\u003c/h2\u003e\u003cp\u003eThe single-cell RNA-seq dataset GSE132465 was retrieved from the GEO repository. It comprises 23 tumor samples and matched normal tissues from 10 patients. Data were processed and cell types were annotated with the Seurat package following the pipeline described by the original authors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Tumor samples were ranked according to their intratumural Treg:CD8⁺ T-cell ratio. The six samples with the highest and the six with the lowest ratios were designated Treg:CD8⁺ T high and low groups, respectively. Differentially expressed genes (DEGs) in malignant cells between these two groups were identified using the FindMarkers function after pseudobulking. Cell\u0026ndash;cell communication between malignant cells and Tregs was inferred with CellChat.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSpatial transcriptomics data acquisition and analysis\u003c/h2\u003e\u003cp\u003eSpatial transcriptomics data (GSE267401) were downloaded from GEO. Raw UMI count matrices, high-resolution images, spatial coordinates, and scale factors were imported into R with the Seurat package. UMI counts were normalized by regularized negative binomial regression. Dimensionality reduction was performed by PCA on the top 3 000 highly variable genes, followed by non-linear embedding with UMAP. Clustering was carried out using the Louvain algorithm. Cell-type deconvolution of the Visium spots was performed with the RTCD method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTCGA data analysis\u003c/h2\u003e\u003cp\u003eWe downloaded the mRNA expression data for CRC (COAD, READ) from the TCGA data portal. After rigorous quality control, immune cell fractions were estimated with CIBERSORT. A weighted gene co-expression network was constructed with the WGCNA R package. Module eigengenes were correlated with the intratumoral Treg/CD8⁺ T-cell ratio to identify modules most associated with immune-inhibitory phenotypes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of HSPB1 as a potential regulator of the tumor immune microenvironment\u003c/h2\u003e\u003cp\u003eAlthough regulatory T cells (Tregs) are generally accepted to be enriched in colorectal cancer (CRC), the immune state varies markedly between patients, and Treg abundance alone fails to accurately gauge the extent of immunosuppression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have shown that the intratumoral Treg:CD8⁺ T-cell ratio more faithfully reflects the degree of immunosuppression and robustly predicts both patient prognosis and responsiveness to immunotherapy[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the mechanisms by which tumors actively shape this ratio remain poorly understood. To systematically identify potential regulators of the Treg:CD8⁺ T-cell ratio in tumor microenvironment, we integrated single-cell RNA sequencing (scRNA-seq) with large-scale bulk transcriptomic data from The Cancer Genome Atlas (TCGA).\u003c/p\u003e\u003cp\u003eWe re-analyzed a published CRC scRNA-seq dataset (GSE132465; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, S1A) and confirmed the pronounced accumulation of Tregs within tumor regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Pairwise comparison further revealed that the Treg:CD8⁺ T-cell ratio was significantly higher in tumors than matched adjacent non-tumoral tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), suggesting the presence of tumor-intrinsic factors that modulate this balance. To uncover such factors, we stratified samples according to their Treg:CD8⁺ T-cell ratio and designated the six tumors with the highest and lowest ratios as \u0026ldquo;High ratio\u0026rdquo; and \u0026ldquo;Low ratio\u0026rdquo; two groups, respectively. Differential expression analysis of malignant cells between these two groups yielded a set of candidate genes that may govern the Treg:CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo augment the power of this discovery pipeline, we leveraged TCGA-COAD bulk RNA-seq data. After estimating immune-cell fractions with CIBERSORT, we performed weighted gene co-expression network analysis (WGCNA) and identified the module whose eigengene exhibited the strongest correlation with the Treg:CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Intersection of the module genes with the scRNA-seq-derived candidate list refined the pool to 1 high confidence regulators: HSPB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Meanwhile, HSPB1 was significantly associated with overall survival in CRC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Spatial transcriptomic profiling further revealed that HSPB1-expressing malignant cells were spatially juxtaposed to Tregs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH), implying a possible direct role for HSPB1 in orchestrating Treg infiltration. Collectively, these data nominate HSPB1 as a putative tumor-intrinsic regulator of the intratumoral Treg:CD8⁺ T-cell ratio.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eKnocking out HSPB1 in tumor cells inhibits tumor growth and increases the Treg:CD8⁺ T-cell ratio\u003c/h2\u003e\u003cp\u003eTo validate the aforementioned bioinformatics findings, We ablated HSPB1 \u003cem\u003evia\u003c/em\u003e CRISPR/Cas9 in the human CRC cell line SW480 and the murine MC38 line (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, S1D), both of which exhibit high endogenous HSPB1 expression (Fig. S1B, C).CCK-8 and colony-formation assays displayed a marked reduction in proliferative and clonogenic capacity in the knockout lines (Fig. S1E, F), while Transwell assays demonstrated impaired \u003cem\u003ein vitro\u003c/em\u003e migratory ability (Fig. S1G), being consistent with previous reports[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the \u003cem\u003ein vivo\u003c/em\u003e modulation of the immune microenvironment by HSPB1, we established subcutaneous tumor models using parental and HSPB1-deficient MC38 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). HSPB1 deletion markedly curtailed \u003cem\u003ein vivo\u003c/em\u003e tumor growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and harvested tumors exhibited significantly reduced volumes and weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). Flow-cytometric profiling presented a pronounced decrease in the intratumoral Tregs/CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG) and a concomitant reduction in the PD-1⁺ Treg/PD-1⁺ CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These changes stemmed primarily from a marked decline in Tregs infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH) and an elevated Th1 cells fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eM), whereas the proportions of other immune subsets remained unaltered (Fig. S2A-D). Consistent alterations were also observed in draining lymph nodes (Fig. S2E-H). The proportion of exhausted CD8⁺ T cells was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI-K), accompanied by enhanced cytokine-secretion capacity among CD8⁺ T cells, most notably a marked up-regulation of IFN-γ (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eN). Concurrently, CD4⁺ T cells exhibited a pronounced increase in IL-2 production (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL). Collectively, these data demonstrate that genetic ablation of HSPB1 in tumor cells not only restrains tumor growth but also reduces the intratumoral Treg:CD8⁺ T-cell ratio, thereby dismantling Treg-mediated immunosuppression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eTargeting HSPB1 effectively dismantles the Treg-orchestrated immunosuppressive microenvironment\u003c/h2\u003e\u003cp\u003eFurthermore, we tested whether small molecule inhibitor-J2 targeting HSPB1 could inhibit tumor growth in mouse models. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). After eight consecutive doses, tumors in the inhibitor-treated cohort were markedly smaller and lighter than controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Flow-cytometric analysis had a pronounced decrease in both the total Treg/CD8⁺ T-cell ratio and the PD-1⁺ Treg/PD-1⁺ CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F), driven primarily by a sharp reduction in intratumoral Tregs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). This is accompanied by significant increases in the frequencies of Th1 and Th17 populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eN, O). Therefore, the function of anti-tumor CD8\u003csup\u003e+\u003c/sup\u003eT cells has been significantly enhanced, displayed robust up-regulation of IFN-γ, granzyme B, TNF-α, and perforin expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u0026ndash;K), while IL-2 secretion by CD4⁺ and CD8⁺ T cells was similarly heightened (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL\u0026ndash;M). Collectively, these data demonstrate that HSPB1-targeted therapy substantially remodels the intratumoral immune milieu, effectively disrupting Treg-mediated immunosuppression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eTargeting HSPB1 selectively suppresses Treg differentiation without impairing Th17\u003c/h2\u003e\u003cp\u003eIt is well established that intratumoral Tregs are not only recruited \u003cem\u003evia\u003c/em\u003e chemokine gradients but also generated through microenvironmental induction[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. We observed that pharmacological inhibition of HSPB1 yielded a more potent therapeutic effect than genetic deletion of HSPB1 in tumor cells alone, prompting us to investigate whether the inhibitor might additionally act on immune compartments to directly suppress Treg induction. To test this hypothesis, we added the HSPB1 inhibitor-J2 to an \u003cem\u003ein vitro\u003c/em\u003e Treg-polarizing culture system as we previously established and monitored its impact on Treg differentiation efficiency[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As expected, HSPB1 inhibitor-J2 suppressed Treg induction in a dose-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTh17 cells, although also differentiated under TGF-β signaling, functionally and phenotypically antagonize Tregs[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To determine whether this inhibitory effect was selective for Tregs, we next examined cultures driven toward the Th17 lineage. Under these settings, J2 dose-dependently curtailed Tregs differentiation without altering Th17 commitment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). Collectively, these findings demonstrate that HSPB1 blockade exerts a dual mechanism of action: it simultaneously suppresses tumor-cell proliferation and also directly impairs Treg differentiation while leaving pro-inflammatory Th17 cells intact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eHSPB1 up-regulates CCL20 to drive CCR6⁺ Treg migration and intratumoral infiltration\u003c/h2\u003e\u003cp\u003eTo elucidate the mechanism by which HSPB1 orchestrates tumor cell\u0026ndash;Tregs crosstalk, we performed transcriptomic sequencing on HSPB1-knockdown and control SW480 cells to delineate downstream oncogenic pathways. Subsequently, we observed that HSPB1-deficient (KO) cells exhibited down-regulated expression of immunosuppressive cytokines (e.g., IL-24, SAA2, and TSLP). Notably, CCL20 levels were also markedly diminished in HSPB1-KO cells. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Consistent with this, KEGG pathway enrichment manifested a marked suppression of cytokine\u0026ndash;cytokine receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). CCL20 functions as a dual-effector in cancers, orchestrating both tumor-promoting and immunosuppressive programs through two converging mechanisms: autocrine stimulation of cancer cell proliferation and migration, and microenvironmental recruitment of Tregs. Given that CCL20 is upregulated in CRC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), we hypothesized that HSPB1 may increase Treg infiltration in the tumor microenvironment by regulating CCL20 expression. Western blotting confirmed that HSPB1 knockout markedly reduced CCL20 expression at the protein level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Subsequent Transwell assays further demonstrated that HSPB1 deletion significantly attenuated the tumor-derived chemotaxis of Tregs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). CCR6, the cognate receptor for CCL20, is widely documented to be highly expressed on Tregs[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eIn vivo\u003c/em\u003e, both HSPB1-knockout tumors and inhibitor-treated tumors exhibited a marked reduction in the proportion of CCR6⁺ Tregs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, G). Taken together, these data establish that HSPB1 up-regulates CCL20 to facilitate CCR6⁺ Treg migration and intratumoral infiltration. Targeting HSPB1 in CRC concurrently suppresses intrinsic tumor growth and CCL20-mediated Treg recruitment, while also attenuating intratumoral Treg induction, thereby exerting a dual antitumor effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince immune-checkpoint blockade revolutionized melanoma treatment, immunotherapy has been translated to numerous solid malignancies; yet, objective response rates in CRC remain stubbornly low [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A systems-level deconstruction of the CRC immunophenotypic landscape especially the dynamic dialogue between neoplastic and immune cells is therefore essential for the rational design of combination strategies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although tumor-infiltrating Tregs are recognized as principal mediators of immunosuppression and predictors of poor outcome, their absolute abundance alone inadequately captures patient-specific immune status or the likelihood of response to checkpoint blockade. Instead, the Treg:CD8⁺ T-cell ratio emerges as a more precise barometer of intratumoral immunosuppression and a reliable biomarker of immunotherapeutic efficacy[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These observations implicate the molecular circuitry that governs the Treg/CD8⁺ T-cell balance as a pivotal, yet underexploited, determinant of immunotherapy responsiveness in CRC.\u003c/p\u003e\u003cp\u003eBy integrating single-cell RNA-seq, spatial transcriptomics and TCGA bulk data through a cross-platform pipeline, we identified HSPB1 as the first druggable regulator of the intratumoral Treg:CD8⁺ T-cell ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, HSPB1 is already targeted by inhibitors that have entered phase II trials for metastatic urothelial carcinoma, providing an immediate translational path to CRC therapy[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHSPB1 is an ATP-independent molecular chaperone that is normally expressed at low basal levels but is rapidly induced under stress to modulate cell-death pathways.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, it is aberrantly and ubiquitously overexpressed in tumors, especially in CRC [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Under oxidative stress, elevated HSPB1 promotes glutathione upregulation and intracellular iron depletion, reducing reactive oxygen species[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Indeed, iron overload affects the development of inflammatory diseases and cancers[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, in CRC, HSPB1 also drives epithelial-mesenchymal transition, enhancing tumor proliferation and metastasis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This conclusion was corroborated in our HSPB1-knockout cell lines (Fig. S1E-G).\u003c/p\u003e\u003cp\u003eAlthough the cell-autonomous functions of HSPB1 in neoplastic cells are well established, its contribution to the sculpting of the tumor microenvironment remains to be explored[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Here we demonstrate that pharmacologic or genetic blockade of HSPB1 concurrently suppresses tumor-cell proliferation, curtails Treg infiltration, and halts Treg differentiation collectively lowering the intratumoral Treg/CD8⁺ T-cell ratio and dismantling a pivotal axis of tumor-driven immunosuppression. Thus, unleash the antitumor immunity, the cytokine-secretion capacity of CD8⁺ T cells were markedly augmented and infiltration of Th1 and Th17 subsets was significantly increased.\u003c/p\u003e\u003cp\u003eIn the tumor microenvironment, cancer cells secrete various chemokines to recruit and reprogram immune cells, facilitating immune evasion [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Intratumoral Treg cell responses are enhanced by chemokines, such as the CCR6-CCL20 axis, which is significantly overexpressed in CRC[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our findings reveal that tumor-derived HSPB1 transcriptionally up-regulates CCL20, thereby recruiting CCR6⁺ Tregs and orchestrating the immunosuppressive tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, G).\u003c/p\u003e\u003cp\u003eNevertheless, several mechanistic gaps remain to be addressed. Specifically, the mechanisms by which HSPB1 suppresses Treg differentiation and modulates CCL20 expression in tumor cells require further investigation. Collectively, we have identified a previously unrecognized function of HSPB1 within the tumor microenvironment, highlighting its potential for bolstering antitumor immunity and informing future combinatorial immunotherapeutic strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePharmacologic or genetic inhibition of HSPB1 simultaneously restrains tumor growth, curtails intratumoral Treg infiltration, and suppresses Treg induction. These findings establish HSPB1 as a promising novel immunotherapeutic target in colorectal cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTregs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e regulatory T cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHSP27\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe heat shock protein 27\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHSP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHeat shock protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpecific pathogen free\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFetal bovine serum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eATCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Type Culture Collection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMACS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAuto magnetic cell sorter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphate buffered saline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColon adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eweighted gene co-expression network analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal procedures were approved by the Institutional Animal Care and Use Committee of Songjiang Hospital, Shanghai Jiao Tong University School of Medicine (protocol ACE-003-2025). Peripheral blood from healthy donors was collected at Songjiang Hospital, Shanghai Jiao Tong University School of Medicine, under Ethics Approval No. 2024-58.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA-seq data were obtained from GEO accession GSE132465, and spatial transcriptomic data from GSE267401. Bulk RNA-seq data generated in this study have been deposited in the GEO database under accession GSE305561.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare to have no conflicts of interest relevant to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding sources supporting this study are listed below:\u003c/p\u003e\n\u003cp\u003eHuadu District People\u0026rsquo;s Hospital of Guangzhou Institutional Research Fund Project, Expert research project ZJXM202501, ZJXM202502; Talent Development Foundation of The First Dongguan Affiliated Hospital of Guangdong Medical University\u0026amp; Foundation of State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia SKL-HIDCA-2024-GD3B; Key technological projects in Songjiang District, Shanghai.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQi Hu and Yang Lu have equal contributions to this work and are recognized as co-first authors.\u0026nbsp;Song Guo Zheng, Wanlin Li and Binghua Jiang conceived the study, supervised overall design, and critically revised the manuscript. Qi Hu and Yang Lu performed all \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments, analyzed the data, and drafted the manuscript.\u0026nbsp;Peng Huang and Xiaolan Zhong oversaw experimental reagents and financial management. Lisheng Zheng supervised and executed the statistical analysis of all experimental data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim JC, Bodmer WF. Genomic landscape of colorectal carcinogenesis. J Cancer Res Clin Oncol. 2022;148(3):533\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuinney J, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng L, et al. 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World J Gastroenterol. 2016;22(2):833\u0026ndash;41.\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, Tumor microenvironment, Treg, HSPB1, CCL20","lastPublishedDoi":"10.21203/rs.3.rs-7382643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7382643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eColorectal cancer (CRC) exhibits limited responsiveness to immune-checkpoint blockade, necessitating further investigation. The intratumoral Treg/CD8⁺ T-cell ratio serves as a predictive biomarker for therapeutic efficacy. Here, we demonstrate that HSPB1 targeting reduces this ratio and confers therapeutic benefit in CRC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCandidate genes were identified by integrative single-cell transcriptomics, TCGA and spatial transcriptomics, followed by survival analyses of TCGA cohorts. Functional interrogation was performed using CRISPR-Cas9 engineered knockout cell lines. Subcutaneous tumor models were established, and the immune microenvironment was characterized by multiparametric flow cytometry. Mechanistic validation was achieved through bulk RNA-seq and complementary functional assays.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSingle-cell profiling and TCGA WGCNA analyze identified HSPB1 as a putative determinant of the intratumoral Treg/CD8⁺ T-cell ratio, and survival analysis showed its prognostic relevance in CRC. Spatial transcriptomics revealed colocalization of HSPB1-expressing tumor cells with Tregs. Subcutaneous tumor models demonstrated that CRISPR-mediated HSPB1 deletion or pharmacologic inhibition markedly suppressed tumor growth and reprogrammed the Treg-dominated microenvironment. In vitro polarization assays confirmed that targeting HSPB1 selectively restrains Treg differentiation without affecting Th17. Integrated transcriptomic and functional studies further elucidated that HSPB1 orchestrates CCL20\u0026ndash;CCR6 mediated Treg recruitment, thereby shaping the immunosuppressive milieu within colorectal tumors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTargeting HSPB1 exerts dual anti-tumor effects: it directly suppresses neoplastic proliferation and simultaneously alleviates Treg-mediated immunosuppression within the tumor microenvironment.\u003c/p\u003e","manuscriptTitle":"Targeting HSPB1 Inhibits Tumor Growth and Abrogates Treg-Mediated Tumor Immunosuppression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 17:36:42","doi":"10.21203/rs.3.rs-7382643/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":"e828ad52-d69c-43fc-abef-e4486cffe9c5","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-18T15:31:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 17:36:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7382643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7382643","identity":"rs-7382643","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}