HSF4 as a Prognostic Predictor and Therapeutic Target in Colorectal Cancer Linked to Immune Status

HSF4 as a Prognostic Predictor and Therapeutic Target in Colorectal Cancer Linked to Immune Status | 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 HSF4 as a Prognostic Predictor and Therapeutic Target in Colorectal Cancer Linked to Immune Status Ting Wan, Yahui Li, Can Shen, Ling Yang, Jinghua Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8375517/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 Colorectal cancer (CRC) is one of the most common malignancies worldwide, with high morbidity and mortality rates. The Heat Shock Factor (HSF) family of genes plays a pivotal role in the cellular response to environmental stress, particularly heat stress. These genes encode transcription factors that regulate the expression of heat shock proteins, which are crucial for maintaining cellular homeostasis under stressful conditions. Recently, there has been increasing interest in the role of HSF family genes in the development and progression of CRC. In this study, we conducted a comprehensive analysis of CRC samples from The Cancer Genome Atlas (TCGA) database to identify potential therapeutic targets. A total of 533 samples’ gene expression matrices, including 42 normal and 491 tumor samples, were downloaded and processed. Differential expression analysis of the HSF family genes was performed, revealing HSF4 as the most significantly differentially expressed gene between normal and tumor samples. So the HSF4-related signature was constructed, and all CRC patients in TCGA dataset were stratified as low-risk or high-risk groups according to HSF4’s expression. Survival analysis demonstrated a significant association between HSF4 expression and clinical outcomes that means the signature had a powerful prognostic value. Correlation analysis further revealed significant relationships between HSF4 expression and clinical features such as Stage, T stage, M stage, and N stage. Univariate and multivariate Cox regression analysis confirmed the prognostic significance of HSF4. Functional analysis of differentially expressed genes based on HSF4 expression levels revealed enrichment in critical biological processes and pathways. Additionally, it has unique properties in tumor microenvironment (TME). Further analysis showed that different risk groups have different immune cell infiltration and cell proliferation ability. Finally, the low-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In conclusion, this is the first signature to predict the prognosis and immunological status of CRC patients based on HSF family genes. Our findings suggest that HSF4 may serve as a potential therapeutic target in CRC. HSF4 Colorectal cancer clinical outcomes TME immunologic landscape Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Colorectal cancer (CRC) represents the second leading cause of cancer-related mortality worldwide, presenting a significant health challenge due to its increasing incidence and mortality projected for the upcoming decades (Sawicki et al., 2021 ). According to global health statistics, CRC's burden is particularly high in developed regions due to dietary and lifestyle factors. However, developing countries are also witnessing a rapid rise in cases and fatalities. This trend is driven by urbanisation, dietary changes and increased life expectancy, which collectively contribute to the disease's growing prevalence(Hossain et al., 2022 ). The therapeutic landscape for colorectal cancer (CRC) has undergone a profound evolution over the past few decades, with significant advances in surgical techniques, chemotherapeutic agents, and targeted therapies leading to a notable improvement in the outcomes of early-stage disease(Benson et al., 2021 ). Despite these advances, CRC remains highly treatable when detected early, with surgical resection and adjuvant therapies providing a potential cure. Nevertheless, the high recurrence rate and increasing instances of drug resistance in advanced stages highlight the urgent need for the development of novel therapeutic targets and strategies to prevent disease progression and recurrence(Yang et al., 2024 ). Among the various molecular players implicated in CRC, the Heat Shock Factor (HSF) family of transcription factors represents a promising avenue for research and therapeutic intervention(Dastidar et al., 2023 ; Gomez-Pastor et al., 2018 ). These factors are primarily known for their role in cellular stress responses, particularly in mediating the heat shock response, which facilitates cellular protection against stress by promoting the correct folding and prevention of aggregation of stress-denatured proteins (Akerfelt et al., 2010 ). In addition to this well-established role, HSFs are increasingly being recognised for their contributions to cancer development and progression. These factors influence cell proliferation, differentiation, and survival. In cancers, the dysregulated expression and activity of HSF4 may contribute to the maintenance of proteostasis under conditions of cellular stress induced by genetic alterations and environmental factors. Additionally, HSF4 may drive the expression of chaperones and other proteins involved in cancer cell survival and proliferation(Puustinen & Sistonen, 2020 ). Given the intricate network of molecular pathways orchestrated by HSFs in cancer, targeting HSF4 holds promise as a therapeutic strategy to disrupt proteostasis, inhibit cancer cell survival, and potentially overcome drug resistance. Further research is required to elucidate the specific roles of HSF4 in different cancer types and its crosstalk with other oncogenic pathways, in order to fully realise its therapeutic potential in cancer treatment. This study aims to elucidate the role of HSF4 in the progression and prognosis of colorectal cancer using a robust dataset from The Cancer Genome Atlas (TCGA). This dataset comprises 533 individual samples from patients diagnosed with colon and rectal adenocarcinomas (COAD and READ), including both cancerous and normal tissue samples, making it a valuable resource for in-depth genetic and transcriptomic analysis. Through comprehensive data analysis, including differential expression studies, survival analyses, and correlation assessments with clinical features, this research seeks to define the impact of HSF4 expression levels on disease outcomes and its potential utility as a prognostic biomarker. Further investigations in this study involve advanced statistical and bioinformatics techniques to analyze the relationship between HSF4 expression and various clinical parameters such as tumor stage, metastasis, and patient survival rates. Additionally, functional enrichment analyses and drug sensitivity assessments are conducted to explore the biological pathways associated with HSF4 and identify potential therapeutic targets that could be exploited for the development of new CRC treatments. This research not only enhances our understanding of the molecular underpinnings of colorectal cancer but also contributes to the broader field of cancer genomics by highlighting the potential of transcription factors like HSF4 as biomarkers and therapeutic targets in the clinical management of cancer. MATERIALS AND METHODS Publicly Data Collection We downloaded 533 samples’ gene expression data and clinical information for TCGA-COAD and TCGA-READ from the TCGA database ( https://portal.gdc.cancer.gov/ ), including 42 normal and 491 tumor samples. Expression matrices were extracted and merged using Strawberry Perl 5.30.0.1. Identification of differentially expressed genes (DEGs) First, we applied pre-filtering approaches (such as eliminating the genes having “all zeros”, “NA value removal”). After that, we carried out gene-wise standardization. Thereafter, Voom normalization and Limma R tool were then utilized consecutively to identify the differentially expressed genes. Limma used Wilcox test. As a result, we obtained a set of statistically significant genes. After that, we applied volcano plot using bi-filtering approaches (p-value filtering and fold change filtering) consecutively. A up-regulated gene can be stated as a gene that had p-value less than 0.05 and fold change greater than 2, whereas a down-regulated gene be a gene having p-value less than 0.05 and fold change less than 0.5. The risk signature generation in CRC Based on the median expression level of the most significantly differentially expressed HSF family gene in tumor and normal tissues, we classified patients into high- and low-risk groups. Subsequently, we employed univariate and multivariate Cox regression analysis to confirm the association between this risk signature and patient prognosis. Pathway and Function Enrichment Analysis After obtaining DEGs using the limma package, we proceed to perform enrichment analysis using both Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. This process involves annotating the genes with their corresponding biological functions and pathways, enabling us to gain insights into the underlying biological mechanisms associated with the observed gene expression changes. Cell proliferation scoring To investigate the relationship between HSF4 and cell proliferation, the gene set “CELL PROLIFERATION GO 0008283” from the GSEA database ( https://www.gsea-msigdb.org/ ) was downloaded. Single sample GSEA (ssGSEA) analysis were used to quantify the cell proliferation capacity of each sample, as well as plot a correlation scatter plot using the ggplot2 package. Analysis of Immune Cell Infiltration and TME To describe the immune landscape of CRC patients, ssGSEA analysis were used to quantify the infiltration abundance of 29 immune cells in TME. The ESTIMATE algorithm was used to estimate the content of Stromal and Immune cells in malignant tumors, and to infer tumor purity and calculate immune score and stromal score. Chemotherapy Drug Sensitivity Analysis The half maximal inhibitory concentration (IC50) of chemotherapy drugs in each sample from TCGA database was estimated via Genomics of Drug Sensitivity in Cancer (GDSC; http://www.cancerrxgene.org/ ) utilizing the oncoPredict package in R. The differences in drug sensitivity of samples between two risk group were analyzed by Wilcoxon test. Statistical Analysis The patients with CRC were divided into high- and low-risk groups according to the optimal cutoff value. The Kaplan–Meier method was used to evaluate the OS between the high- and the low-risk group, and the log-rank was used to verify the significant difference. The unpaired u-test was used to analyze the distribution of immune cells in the different risk groups. Independent prognostic factors were calculated by Cox proportional hazard regression signature. All statistical analyses were presented via R 3.6.0 ( https://www.r-project.org/ )., and p < 0.05 was considered statistically different. RESULTS Patients’ data preparation 491 samples’ of CRC and 42 healthy samples’ RNA-sequencing expression profiles and clinical information were publicly available and downloaded from TCGA-COAD and TCGA-READ cohorts of the TCGA database. Identification differentially expressed HSF family genes in normal and tumor samples Given the close association between HSF family genes and the occurrence and development of CRC, we conducted a differential analysis of HSF family genes in both normal and tumor samples. Through this analysis, we ultimately identified HSF4 as the gene with the most significant difference based on the adjusted P-value. This indicates that HSF4 may play a crucial role in the pathogenesis of CRC, providing valuable insights into the underlying mechanisms involved in the development of this malignancy. HSF4’s expression is associated with clinical staging. The expression of certain crucial genes can have a profound impact on the clinical outcomes and tumor staging of patients. Therefore, we investigated the correlation between the expression level of HSF4 and clinical characteristics. The result showed a significant association between the expression of HSF4 and various clinical staging parameters, including Stage, T staging, M staging, and N staging. The higher expression levels of HSF4 were associated with more advanced stages of the disease, indicating that this gene may play a role in promoting tumor progression. Similarly, we found significant associations between HSF4 expression and T, M, and N staging, which are key indicators of tumor size, metastasis, and lymph node involvement, respectively. These findings suggest that the expression level of HSF4 could be a potential biomarker for predicting the clinical outcome and staging of patients with certain types of cancers. HSF4’s expression has a significant impact on prognosis. In order to investigate whether the expression of HSF4 has an impact on the prognosis of CRC patients, we conducted univariate and multivariate Cox regression analysis. The results revealed that the HSF4 gene significantly influenced prognosis. This finding suggests that the expression level of HSF4 may play a crucial role in determining the clinical outcome of patients, potentially providing valuable insights for prognostic prediction and personalized treatment strategies. Constructing a risk signature with HSF4’s expression Based on the above research results, we classified the patients into high- and low-expression groups (high- and low-risk group) using the median value of HSF4’s expression as the threshold. Kaplan–Meier survival indicated that lower expression was associated with better OS (p = 0.047). Biological pathways of the HSF4-related risk signature We further paid our interest on the exploration of its potential mechanisms. Firstly, to analyze the molecular biological characteristics of this signature comprehensively, we screened out these genes strongly related to HSF4-related risk signature. The heatmap and volcano plot visualized that 2494 DEGs were identified with limma software with |logFC|> 1 and adjusted P value < 0.05. Then, for these genes, we further performed GO and KEGG function enrichment analysis to deeply study the mechanism. The GO enrichment analysis demonstrated that in the biological process group, these DEGs were mainly involved in cellular response to type II interferon, Golgi vesicle transport, protein localization to cilium. In the cellular component, DEGs were mainly involved in coated vesicle, nuclear speck, and coated vesicle membrane. Thereafter, we conducted KEGG pathway analysis and found that HSF4-expressed signatures were involved in several pathways, including Endocytosis, Human cytomegalovirus infection, and Lipid and atherosclerosis. HSF4’s expression is positively related to cell proliferation We conducted a correlation analysis between HSF4’expression and cell proliferation score. The cell proliferation score was calculated by ssGSEA. The results showed that HSF4’expression was negatively correlated with the cell proliferation score (Fig. 5 B). This negative correlation may reflect the crucial role of HSF4 in cell growth and division. HSF4 could be a key regulatory factor that affects processes such as the cell cycle, DNA replication, or other processes related to cell proliferation. Furthermore, this negative correlation may also be related to certain diseases or pathological states. Especially, in CRC, the expression level of HSF4 may change, affecting the rate of cell proliferation and disease progression. These findings suggest that HSF4 could be a potential biomarker for treatment. Therefore, studying the relationship between HSF4 and cell proliferation is crucial for understanding the pathogenesis of these diseases and finding potential treatment methods. Alteration of tumor microenvironment associated with the HSF4-related risk signature To determine the relationship between the HSF4-related risk signature and the tumor microenvironment (TME), We used ssGSEA to estimate the difference of 29 types of tumor-infiltrating immune cells between the low-risk and high-risk group. The result showed that B cells, pDCs and T helper cells were significantly more enriched in the high-risk group than in the low-risk group. In contrast, aDCs, CD8 + T cells, macrophages, NK cells, DCs, neutrophils and mast cells were mainly in the low-risk group. Additionally, we applied the ESTIMATE algorithm to calculate the estimated score, immune score, stromal score, representing the tumor environment. We found these scores were significantly increased HSF4-low group. Evaluation of the Sensitivity of Chemotherapy Drugs to high- and low-risk groups We further explored the analysis of drug sensitivity in the high- and low-risk groups. We referenced the GDSC database and used the oncoPredict package to conduct drug sensitivity analysis for each sample, observing the differences in drug sensitivity between the high- and low-expression groups. Finally, we found significant differences in 30 drugs between the high- and low-expression groups, indicating that these 30 drugs are potential anticancer drugs for CRC. DISCUSSION The findings from our study provide compelling evidence that HSF4 plays a significant role in the pathogenesis and progression of colorectal cancer (CRC). Differential gene expression analysis identified HSF4 as notably upregulated in tumour samples compared to normal tissue, underscoring its potential involvement in cancer development. Furthermore, the association of HSF4's expression with advanced clinical stages and poor prognosis highlights its role in CRC progression. The significant correlation between HSF4 expression and key staging parameters, including stage, T, M, and N staging, suggests that its expression may directly influence tumour size, metastasis, and lymph node involvement. These associations make HSF4 a promising candidate biomarker for CRC staging and progression, offering a potential tool for clinical assessment and decision-making. The use of HSF4 expression to stratify patients into high- and low-risk groups revealed that lower HSF4 expression correlates with better overall survival (OS). This division into risk groups based on median expression levels not only reinforces the prognostic value of HSF4 but also aids in understanding the differential outcomes in patient survival, emphasising the importance of HSF4 in prognostic evaluations. On a molecular level, the enriched biological pathways associated with HSF4-related differential gene expression, such as response to type II interferon and endocytosis, indicate that HSF4 could be influencing CRC through immune modulation and cellular trafficking processes. This could potentially explain how changes in HSF4 expression affect cancer cell behaviour and interact with the tumour microenvironment. Interestingly, our analysis showed a negative correlation between HSF4 expression and cell proliferation scores. This counterintuitive result suggests that HSF4 may have a complex role in cell growth, potentially acting through mechanisms that inhibit proliferation when overexpressed. This aspect of HSF4's functionality could be critical in understanding its dualistic roles in cancer biology, where it may act differently in various contexts or stages of cancer. Furthermore, the alteration in tumour microenvironment (TME) profiles between high- and low-risk groups based on HSF4 expression was significant. The high-risk group exhibited an enrichment of B cells, pDCs, and T helper cells, which are typically associated with a more active immune environment. Conversely, the low-risk group displayed higher levels of aDCs, CD8 + T cells, and other immune cells that may contribute to a more effective antitumor response. These findings suggest that HSF4 expression levels could be modulating the immune landscape of CRC, thereby affecting tumour progression and patient outcomes. The evaluation of chemotherapy drug sensitivity revealed significant differences in response between the high- and low-risk groups. This underscores the potential of using HSF4 expression levels not only as a biomarker for disease progression but also for tailoring chemotherapy treatments. The identification of 30 drugs with varied sensitivities between these groups provides a foundation for personalised treatment approaches, potentially enhancing therapeutic efficacy and patient survival. CONCLUSION In conclusion, our comprehensive analysis of the HSF family genes in colorectal cancer (CRC) has revealed HSF4 as a gene with significant differential expression between normal and tumor tissues. We further confirmed its prognostic significance through univariate and multivariate Cox regression analysis. The expression level of HSF4 was found to be significantly associated with clinical characteristics such as tumor stage, T stage, M stage, and N stage. Our analysis also revealed a positive correlation between HSF4 expression and cell proliferation, indicating its potential role as a therapeutic target. Additionally, we assessed the tumor microenvironment and immune cell infiltration, providing insights into the tumor immune landscape. Finally, drug sensitivity analysis identified 30 potential anti-CRC drugs that showed significant differences in sensitivity between the high and low HSF4 expression groups. Collectively, our findings highlight the crucial role of HSF4 in CRC and suggest its potential as a prognostic marker and therapeutic target. These results provide valuable insights for future research on CRC pathogenesis and the development of personalized treatment strategies. Declarations FUNDING Yunnan Provincial Department of Science and Technology Kunming Medical University Applied Basic Research, Joint Special Project. Fund number: 202201AY070001-272. Author Contribution Ting Wan: Conceptualization, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization.Yahui Li: Methodology, Software, Validation, Writing – review & editing.Can Shen: Resources, Data curation, Writing – review & editing.Ling Yang: Investigation, Visualization, Writing – review & editing.Jinghua Liu: Supervision, Project administration, Funding acquisition, Writing – review & editing.All authors read and approved the final manuscript. Data Availability The datasets analyzed during the current study are publicly available in The Cancer Genome Atlas (TCGA) database, under the Colon Adenocarcinoma (TCGA-COAD) and Rectal Adenocarcinoma (TCGA-READ) projects. All data can be accessed via the Genomic Data Commons (GDC) portal at https://portal.gdc.cancer.gov/.The complete list of 533 TCGA sample barcodes (case identifiers) used in this analysis is provided as Supplementary Table S1. References Akerfelt M, Morimoto RI, Sistonen L. Heat shock factors: Integrators of cell stress, development and lifespan. Nat Rev Mol Cell Biol. 2010;11(8):545–55. https://doi.org/10.1038/nrm2938 . Benson AB, Venook AP, Al-Hawary MM, Arain MA, Chen Y-J, Ciombor KK, Cohen S, Cooper HS, Deming D, Farkas L, Garrido-Laguna I, Grem JL, Gunn A, Hecht JR, Hoffe S, Hubbard J, Hunt S, Johung KL, Kirilcuk N, Gurski LA. Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Network: JNCCN. 2021;19(3):329–59. https://doi.org/10.6004/jnccn.2021.0012 . Dastidar SG, De Kumar B, Lauckner B, Parrello D, Perley D, Vlasenok M, Tyagi A, Koney NK-K, Abbas A, Nechaev S. Transcriptional responses of cancer cells to heat shock-inducing stimuli involve amplification of robust HSF1 binding. Nat Commun. 2023;14(1):7420. https://doi.org/10.1038/s41467-023-43157-7 . Gomez-Pastor R, Burchfiel ET, Thiele DJ. Regulation of heat shock transcription factors and their roles in physiology and disease. Nat Rev Mol Cell Biol. 2018;19(1):4–19. https://doi.org/10.1038/nrm.2017.73 . Hossain MS, Karuniawati H, Jairoun AA, Urbi Z, Ooi DJ, John A, Lim YC, Kibria KMK, Mohiuddin AKM, Ming LC, Goh KW, Hadi MA. Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies. Cancers. 2022;14(7):1732. https://doi.org/10.3390/cancers14071732 . Puustinen MC, Sistonen L. Molecular Mechanisms of Heat Shock Factors in Cancer. Cells. 2020;9(5):1202. https://doi.org/10.3390/cells9051202 . Sawicki T, Ruszkowska M, Danielewicz A, Niedźwiedzka E, Arłukowicz T, Przybyłowicz KE. (2021). A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers , 13 (9), 2025. https://doi.org/10.3390/cancers13092025 Yang L, Yang J, Kleppe A, Danielsen HE, Kerr DJ. Personalizing adjuvant therapy for patients with colorectal cancer. Nat Reviews Clin Oncol. 2024;21(1):67–79. https://doi.org/10.1038/s41571-023-00834-2 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1TCGASampleBarcodes.xlsx SUPPLEMENTARYMATERIAL.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8375517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576770594,"identity":"231d16d8-0ab6-466f-b880-418372acdcb1","order_by":0,"name":"Ting Wan","email":"","orcid":"","institution":"Affiliated Hospital of Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wan","suffix":""},{"id":576770595,"identity":"9b7c571e-c892-481f-b0fd-8931e900eeca","order_by":1,"name":"Yahui Li","email":"","orcid":"","institution":"Affiliated Hospital of Yunnan 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1","display":"","copyAsset":false,"role":"figure","size":78401,"visible":true,"origin":"","legend":"\u003cp\u003eThe differential expression of HSF family genes in normal and tumour samples was investigated.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/76e68da171159939279b6759.png"},{"id":100689755,"identity":"61fefc5c-38b9-4b68-ae91-077b288a87eb","added_by":"auto","created_at":"2026-01-20 13:46:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130470,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between clinical staging and HSF4 expression\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/59ca28d824871a6c57ead964.png"},{"id":100689662,"identity":"c297e564-36f3-4da3-855f-761fc17f4423","added_by":"auto","created_at":"2026-01-20 13:45:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63207,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival prognosis andHSF4 expression\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/b061559cfac3deb2f382e190.png"},{"id":100689756,"identity":"314086f5-5b88-4512-9bf7-dd86ec3a1afb","added_by":"auto","created_at":"2026-01-20 13:46:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131180,"visible":true,"origin":"","legend":"\u003cp\u003eRisk profiles associated with HSF4 and their enrichment results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/9556981922cca4feb6502044.png"},{"id":100690015,"identity":"efa1b639-5c39-48bb-bb2f-f2935fd54883","added_by":"auto","created_at":"2026-01-20 13:49:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":140877,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between HSF4 expression and cell proliferation fraction\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/08c36b0685d2d59d12fc139f.png"},{"id":100689740,"identity":"adb88640-c362-412d-90b6-96df7fe126dc","added_by":"auto","created_at":"2026-01-20 13:45:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160431,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between HSF4 and immune cell infiltration\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/bd9c491bc8b4ffdf05cf8240.png"},{"id":100689979,"identity":"a754ada1-9f5a-4d23-a193-5967ac6eabb9","added_by":"auto","created_at":"2026-01-20 13:48:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95397,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in sensitivity of chemotherapeutic agents between high and low HSF4 subgroups\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/f2946b3104d378daf1b8bc98.png"},{"id":102416652,"identity":"21656e58-42c2-4a67-88cf-b7e7cf3274fe","added_by":"auto","created_at":"2026-02-11 12:57:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1352065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/71263e1e-9c2e-4674-a54f-6b9aac2f783e.pdf"},{"id":100689863,"identity":"bdb004c3-268e-4c0b-a9d7-4819e2773ea5","added_by":"auto","created_at":"2026-01-20 13:48:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20376,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1TCGASampleBarcodes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/291f6e88d0385bf551b17e1b.xlsx"},{"id":100689751,"identity":"2b63ddcb-18f6-4045-94de-9ff2179508f8","added_by":"auto","created_at":"2026-01-20 13:46:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":194814,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-8375517/v1/76b28ed1c5f8ea5102962612.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HSF4 as a Prognostic Predictor and Therapeutic Target in Colorectal Cancer Linked to Immune Status","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eColorectal cancer (CRC) represents the second leading cause of cancer-related mortality worldwide, presenting a significant health challenge due to its increasing incidence and mortality projected for the upcoming decades (Sawicki et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to global health statistics, CRC's burden is particularly high in developed regions due to dietary and lifestyle factors. However, developing countries are also witnessing a rapid rise in cases and fatalities. This trend is driven by urbanisation, dietary changes and increased life expectancy, which collectively contribute to the disease's growing prevalence(Hossain et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe therapeutic landscape for colorectal cancer (CRC) has undergone a profound evolution over the past few decades, with significant advances in surgical techniques, chemotherapeutic agents, and targeted therapies leading to a notable improvement in the outcomes of early-stage disease(Benson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite these advances, CRC remains highly treatable when detected early, with surgical resection and adjuvant therapies providing a potential cure. Nevertheless, the high recurrence rate and increasing instances of drug resistance in advanced stages highlight the urgent need for the development of novel therapeutic targets and strategies to prevent disease progression and recurrence(Yang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the various molecular players implicated in CRC, the Heat Shock Factor (HSF) family of transcription factors represents a promising avenue for research and therapeutic intervention(Dastidar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gomez-Pastor et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These factors are primarily known for their role in cellular stress responses, particularly in mediating the heat shock response, which facilitates cellular protection against stress by promoting the correct folding and prevention of aggregation of stress-denatured proteins (Akerfelt et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition to this well-established role, HSFs are increasingly being recognised for their contributions to cancer development and progression. These factors influence cell proliferation, differentiation, and survival.\u003c/p\u003e \u003cp\u003eIn cancers, the dysregulated expression and activity of HSF4 may contribute to the maintenance of proteostasis under conditions of cellular stress induced by genetic alterations and environmental factors. Additionally, HSF4 may drive the expression of chaperones and other proteins involved in cancer cell survival and proliferation(Puustinen \u0026amp; Sistonen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the intricate network of molecular pathways orchestrated by HSFs in cancer, targeting HSF4 holds promise as a therapeutic strategy to disrupt proteostasis, inhibit cancer cell survival, and potentially overcome drug resistance. Further research is required to elucidate the specific roles of HSF4 in different cancer types and its crosstalk with other oncogenic pathways, in order to fully realise its therapeutic potential in cancer treatment.\u003c/p\u003e \u003cp\u003eThis study aims to elucidate the role of HSF4 in the progression and prognosis of colorectal cancer using a robust dataset from The Cancer Genome Atlas (TCGA). This dataset comprises 533 individual samples from patients diagnosed with colon and rectal adenocarcinomas (COAD and READ), including both cancerous and normal tissue samples, making it a valuable resource for in-depth genetic and transcriptomic analysis. Through comprehensive data analysis, including differential expression studies, survival analyses, and correlation assessments with clinical features, this research seeks to define the impact of HSF4 expression levels on disease outcomes and its potential utility as a prognostic biomarker.\u003c/p\u003e \u003cp\u003eFurther investigations in this study involve advanced statistical and bioinformatics techniques to analyze the relationship between HSF4 expression and various clinical parameters such as tumor stage, metastasis, and patient survival rates. Additionally, functional enrichment analyses and drug sensitivity assessments are conducted to explore the biological pathways associated with HSF4 and identify potential therapeutic targets that could be exploited for the development of new CRC treatments.\u003c/p\u003e \u003cp\u003eThis research not only enhances our understanding of the molecular underpinnings of colorectal cancer but also contributes to the broader field of cancer genomics by highlighting the potential of transcription factors like HSF4 as biomarkers and therapeutic targets in the clinical management of cancer.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublicly Data Collection\u003c/h2\u003e \u003cp\u003eWe downloaded 533 samples\u0026rsquo; gene expression data and clinical information for TCGA-COAD and TCGA-READ from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including 42 normal and 491 tumor samples. Expression matrices were extracted and merged using Strawberry Perl 5.30.0.1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of differentially expressed genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eFirst, we applied pre-filtering approaches (such as eliminating the genes having \u0026ldquo;all zeros\u0026rdquo;, \u0026ldquo;NA value removal\u0026rdquo;). After that, we carried out gene-wise standardization. Thereafter, Voom normalization and Limma R tool were then utilized consecutively to identify the differentially expressed genes. Limma used Wilcox test. As a result, we obtained a set of statistically significant genes. After that, we applied volcano plot using bi-filtering approaches (p-value filtering and fold change filtering) consecutively. A up-regulated gene can be stated as a gene that had p-value less than 0.05 and fold change greater than 2, whereas a down-regulated gene be a gene having p-value less than 0.05 and fold change less than 0.5.\u003c/p\u003e\n\u003ch3\u003eThe risk signature generation in CRC\u003c/h3\u003e\n\u003cp\u003eBased on the median expression level of the most significantly differentially expressed HSF family gene in tumor and normal tissues, we classified patients into high- and low-risk groups. Subsequently, we employed univariate and multivariate Cox regression analysis to confirm the association between this risk signature and patient prognosis.\u003c/p\u003e\n\u003ch3\u003ePathway and Function Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eAfter obtaining DEGs using the limma package, we proceed to perform enrichment analysis using both Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. This process involves annotating the genes with their corresponding biological functions and pathways, enabling us to gain insights into the underlying biological mechanisms associated with the observed gene expression changes.\u003c/p\u003e\n\u003ch3\u003eCell proliferation scoring\u003c/h3\u003e\n\u003cp\u003eTo investigate the relationship between HSF4 and cell proliferation, the gene set \u0026ldquo;CELL PROLIFERATION GO 0008283\u0026rdquo; from the GSEA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was downloaded. Single sample GSEA (ssGSEA) analysis were used to quantify the cell proliferation capacity of each sample, as well as plot a correlation scatter plot using the ggplot2 package.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Immune Cell Infiltration and TME\u003c/h2\u003e \u003cp\u003eTo describe the immune landscape of CRC patients, ssGSEA analysis were used to quantify the infiltration abundance of 29 immune cells in TME. The ESTIMATE algorithm was used to estimate the content of Stromal and Immune cells in malignant tumors, and to infer tumor purity and calculate immune score and stromal score.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChemotherapy Drug Sensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eThe half maximal inhibitory concentration (IC50) of chemotherapy drugs in each sample from TCGA database was estimated via Genomics of Drug Sensitivity in Cancer (GDSC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"http://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) utilizing the oncoPredict package in R. The differences in drug sensitivity of samples between two risk group were analyzed by Wilcoxon test.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe patients with CRC were divided into high- and low-risk groups according to the optimal cutoff value. The Kaplan\u0026ndash;Meier method was used to evaluate the OS between the high- and the low-risk group, and the log-rank was used to verify the significant difference. The unpaired u-test was used to analyze the distribution of immune cells in the different risk groups. Independent prognostic factors were calculated by Cox proportional hazard regression signature. All statistical analyses were presented via R 3.6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)., and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically different.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; data preparation\u003c/h2\u003e \u003cp\u003e491 samples\u0026rsquo; of CRC and 42 healthy samples\u0026rsquo; RNA-sequencing expression profiles and clinical information were publicly available and downloaded from TCGA-COAD and TCGA-READ cohorts of the TCGA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification differentially expressed HSF family genes in normal and tumor samples\u003c/h2\u003e \u003cp\u003eGiven the close association between HSF family genes and the occurrence and development of CRC, we conducted a differential analysis of HSF family genes in both normal and tumor samples. Through this analysis, we ultimately identified HSF4 as the gene with the most significant difference based on the adjusted P-value. This indicates that HSF4 may play a crucial role in the pathogenesis of CRC, providing valuable insights into the underlying mechanisms involved in the development of this malignancy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHSF4\u0026rsquo;s expression is associated with clinical staging.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe expression of certain crucial genes can have a profound impact on the clinical outcomes and tumor staging of patients. Therefore, we investigated the correlation between the expression level of HSF4 and clinical characteristics. The result showed a significant association between the expression of HSF4 and various clinical staging parameters, including Stage, T staging, M staging, and N staging. The higher expression levels of HSF4 were associated with more advanced stages of the disease, indicating that this gene may play a role in promoting tumor progression. Similarly, we found significant associations between HSF4 expression and T, M, and N staging, which are key indicators of tumor size, metastasis, and lymph node involvement, respectively.\u003c/p\u003e \u003cp\u003eThese findings suggest that the expression level of HSF4 could be a potential biomarker for predicting the clinical outcome and staging of patients with certain types of cancers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHSF4\u0026rsquo;s expression has a significant impact on prognosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to investigate whether the expression of HSF4 has an impact on the prognosis of CRC patients, we conducted univariate and multivariate Cox regression analysis. The results revealed that the HSF4 gene significantly influenced prognosis. This finding suggests that the expression level of HSF4 may play a crucial role in determining the clinical outcome of patients, potentially providing valuable insights for prognostic prediction and personalized treatment strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstructing a risk signature with HSF4\u0026rsquo;s expression\u003c/h2\u003e \u003cp\u003eBased on the above research results, we classified the patients into high- and low-expression groups (high- and low-risk group) using the median value of HSF4\u0026rsquo;s expression as the threshold. Kaplan\u0026ndash;Meier survival indicated that lower expression was associated with better OS (p\u0026thinsp;=\u0026thinsp;0.047).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiological pathways of the HSF4-related risk signature\u003c/h2\u003e \u003cp\u003eWe further paid our interest on the exploration of its potential mechanisms. Firstly, to analyze the molecular biological characteristics of this signature comprehensively, we screened out these genes strongly related to HSF4-related risk signature. The heatmap and volcano plot visualized that 2494 DEGs were identified with limma software with |logFC|\u0026gt; 1 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Then, for these genes, we further performed GO and KEGG function enrichment analysis to deeply study the mechanism. The GO enrichment analysis demonstrated that in the biological process group, these DEGs were mainly involved in cellular response to type II interferon, Golgi vesicle transport, protein localization to cilium. In the cellular component, DEGs were mainly involved in coated vesicle, nuclear speck, and coated vesicle membrane. Thereafter, we conducted KEGG pathway analysis and found that HSF4-expressed signatures were involved in several pathways, including Endocytosis, Human cytomegalovirus infection, and Lipid and atherosclerosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHSF4\u0026rsquo;s expression is positively related to cell proliferation\u003c/h2\u003e \u003cp\u003eWe conducted a correlation analysis between HSF4\u0026rsquo;expression and cell proliferation score. The cell proliferation score was calculated by ssGSEA. The results showed that HSF4\u0026rsquo;expression was negatively correlated with the cell proliferation score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This negative correlation may reflect the crucial role of HSF4 in cell growth and division. HSF4 could be a key regulatory factor that affects processes such as the cell cycle, DNA replication, or other processes related to cell proliferation. Furthermore, this negative correlation may also be related to certain diseases or pathological states. Especially, in CRC, the expression level of HSF4 may change, affecting the rate of cell proliferation and disease progression. These findings suggest that HSF4 could be a potential biomarker for treatment. Therefore, studying the relationship between HSF4 and cell proliferation is crucial for understanding the pathogenesis of these diseases and finding potential treatment methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAlteration of tumor microenvironment associated with the HSF4-related risk signature\u003c/h2\u003e \u003cp\u003eTo determine the relationship between the HSF4-related risk signature and the tumor microenvironment (TME), We used ssGSEA to estimate the difference of 29 types of tumor-infiltrating immune cells between the low-risk and high-risk group. The result showed that B cells, pDCs and T helper cells were significantly more enriched in the high-risk group than in the low-risk group. In contrast, aDCs, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, NK cells, DCs, neutrophils and mast cells were mainly in the low-risk group.\u003c/p\u003e \u003cp\u003eAdditionally, we applied the ESTIMATE algorithm to calculate the estimated score, immune score, stromal score, representing the tumor environment. We found these scores were significantly increased HSF4-low group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Sensitivity of Chemotherapy Drugs to high- and low-risk groups\u003c/h2\u003e \u003cp\u003eWe further explored the analysis of drug sensitivity in the high- and low-risk groups. We referenced the GDSC database and used the oncoPredict package to conduct drug sensitivity analysis for each sample, observing the differences in drug sensitivity between the high- and low-expression groups. Finally, we found significant differences in 30 drugs between the high- and low-expression groups, indicating that these 30 drugs are potential anticancer drugs for CRC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe findings from our study provide compelling evidence that HSF4 plays a significant role in the pathogenesis and progression of colorectal cancer (CRC). Differential gene expression analysis identified HSF4 as notably upregulated in tumour samples compared to normal tissue, underscoring its potential involvement in cancer development. Furthermore, the association of HSF4's expression with advanced clinical stages and poor prognosis highlights its role in CRC progression.\u003c/p\u003e \u003cp\u003eThe significant correlation between HSF4 expression and key staging parameters, including stage, T, M, and N staging, suggests that its expression may directly influence tumour size, metastasis, and lymph node involvement. These associations make HSF4 a promising candidate biomarker for CRC staging and progression, offering a potential tool for clinical assessment and decision-making.\u003c/p\u003e \u003cp\u003eThe use of HSF4 expression to stratify patients into high- and low-risk groups revealed that lower HSF4 expression correlates with better overall survival (OS). This division into risk groups based on median expression levels not only reinforces the prognostic value of HSF4 but also aids in understanding the differential outcomes in patient survival, emphasising the importance of HSF4 in prognostic evaluations.\u003c/p\u003e \u003cp\u003eOn a molecular level, the enriched biological pathways associated with HSF4-related differential gene expression, such as response to type II interferon and endocytosis, indicate that HSF4 could be influencing CRC through immune modulation and cellular trafficking processes. This could potentially explain how changes in HSF4 expression affect cancer cell behaviour and interact with the tumour microenvironment.\u003c/p\u003e \u003cp\u003eInterestingly, our analysis showed a negative correlation between HSF4 expression and cell proliferation scores. This counterintuitive result suggests that HSF4 may have a complex role in cell growth, potentially acting through mechanisms that inhibit proliferation when overexpressed. This aspect of HSF4's functionality could be critical in understanding its dualistic roles in cancer biology, where it may act differently in various contexts or stages of cancer. Furthermore, the alteration in tumour microenvironment (TME) profiles between high- and low-risk groups based on HSF4 expression was significant. The high-risk group exhibited an enrichment of B cells, pDCs, and T helper cells, which are typically associated with a more active immune environment. Conversely, the low-risk group displayed higher levels of aDCs, CD8\u0026thinsp;+\u0026thinsp;T cells, and other immune cells that may contribute to a more effective antitumor response. These findings suggest that HSF4 expression levels could be modulating the immune landscape of CRC, thereby affecting tumour progression and patient outcomes.\u003c/p\u003e \u003cp\u003eThe evaluation of chemotherapy drug sensitivity revealed significant differences in response between the high- and low-risk groups. This underscores the potential of using HSF4 expression levels not only as a biomarker for disease progression but also for tailoring chemotherapy treatments. The identification of 30 drugs with varied sensitivities between these groups provides a foundation for personalised treatment approaches, potentially enhancing therapeutic efficacy and patient survival.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, our comprehensive analysis of the HSF family genes in colorectal cancer (CRC) has revealed HSF4 as a gene with significant differential expression between normal and tumor tissues. We further confirmed its prognostic significance through univariate and multivariate Cox regression analysis. The expression level of HSF4 was found to be significantly associated with clinical characteristics such as tumor stage, T stage, M stage, and N stage. Our analysis also revealed a positive correlation between HSF4 expression and cell proliferation, indicating its potential role as a therapeutic target. Additionally, we assessed the tumor microenvironment and immune cell infiltration, providing insights into the tumor immune landscape. Finally, drug sensitivity analysis identified 30 potential anti-CRC drugs that showed significant differences in sensitivity between the high and low HSF4 expression groups. Collectively, our findings highlight the crucial role of HSF4 in CRC and suggest its potential as a prognostic marker and therapeutic target. These results provide valuable insights for future research on CRC pathogenesis and the development of personalized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eYunnan Provincial Department of Science and Technology Kunming Medical University Applied Basic Research, Joint Special Project. Fund number: 202201AY070001-272.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTing Wan: Conceptualization, Formal analysis, Investigation, Data curation, Writing \u0026ndash; original draft, Visualization.Yahui Li: Methodology, Software, Validation, Writing \u0026ndash; review \u0026amp; editing.Can Shen: Resources, Data curation, Writing \u0026ndash; review \u0026amp; editing.Ling Yang: Investigation, Visualization, Writing \u0026ndash; review \u0026amp; editing.Jinghua Liu: Supervision, Project administration, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are publicly available in The Cancer Genome Atlas (TCGA) database, under the Colon Adenocarcinoma (TCGA-COAD) and Rectal Adenocarcinoma (TCGA-READ) projects. All data can be accessed via the Genomic Data Commons (GDC) portal at https://portal.gdc.cancer.gov/.The complete list of 533 TCGA sample barcodes (case identifiers) used in this analysis is provided as Supplementary Table S1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkerfelt M, Morimoto RI, Sistonen L. Heat shock factors: Integrators of cell stress, development and lifespan. 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A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. \u003cem\u003eCancers\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(9), 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers13092025\u003c/span\u003e\u003cspan address=\"10.3390/cancers13092025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang J, Kleppe A, Danielsen HE, Kerr DJ. Personalizing adjuvant therapy for patients with colorectal cancer. Nat Reviews Clin Oncol. 2024;21(1):67\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41571-023-00834-2\u003c/span\u003e\u003cspan address=\"10.1038/s41571-023-00834-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HSF4, Colorectal cancer, clinical outcomes, TME, immunologic landscape","lastPublishedDoi":"10.21203/rs.3.rs-8375517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8375517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is one of the most common malignancies worldwide, with high morbidity and mortality rates. The Heat Shock Factor (HSF) family of genes plays a pivotal role in the cellular response to environmental stress, particularly heat stress. These genes encode transcription factors that regulate the expression of heat shock proteins, which are crucial for maintaining cellular homeostasis under stressful conditions. Recently, there has been increasing interest in the role of HSF family genes in the development and progression of CRC. In this study, we conducted a comprehensive analysis of CRC samples from The Cancer Genome Atlas (TCGA) database to identify potential therapeutic targets. A total of 533 samples\u0026rsquo; gene expression matrices, including 42 normal and 491 tumor samples, were downloaded and processed. Differential expression analysis of the HSF family genes was performed, revealing HSF4 as the most significantly differentially expressed gene between normal and tumor samples. So the HSF4-related signature was constructed, and all CRC patients in TCGA dataset were stratified as low-risk or high-risk groups according to HSF4\u0026rsquo;s expression. Survival analysis demonstrated a significant association between HSF4 expression and clinical outcomes that means the signature had a powerful prognostic value. Correlation analysis further revealed significant relationships between HSF4 expression and clinical features such as Stage, T stage, M stage, and N stage. Univariate and multivariate Cox regression analysis confirmed the prognostic significance of HSF4. Functional analysis of differentially expressed genes based on HSF4 expression levels revealed enrichment in critical biological processes and pathways. Additionally, it has unique properties in tumor microenvironment (TME). Further analysis showed that different risk groups have different immune cell infiltration and cell proliferation ability. Finally, the low-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In conclusion, this is the first signature to predict the prognosis and immunological status of CRC patients based on HSF family genes. Our findings suggest that HSF4 may serve as a potential therapeutic target in CRC.\u003c/p\u003e","manuscriptTitle":"HSF4 as a Prognostic Predictor and Therapeutic Target in Colorectal Cancer Linked to Immune Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:29:15","doi":"10.21203/rs.3.rs-8375517/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":"f6e0f6f0-d2c0-4080-a931-2dc3048024cc","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 11:29:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8375517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8375517","identity":"rs-8375517","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}