Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer

Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer | 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 Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer Hui Yang, Yin Liu, Xiaofeng Mu, Kun Wang, Mengdi Hao, Huimin Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2712006/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 Radiotherapy is closely related to the tumor immune microenvironment, but the role of immune genes in radiosensitivity and prognosis of rectal cancer (RC) is still unclear. This study aims to construct a prognostic risk model based on radiosensitivity-related immune genes (RRIGs), which can be used for predicting prognosis of RC. Methods GSE133057 dataset of RC was downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between different radiosensitivity groups. RRIGs were obtained by intersecting DEGs and immune genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed to study the biological functions of RRIGs. Transcriptomic and clinical data of RC were downloaded from The Cancer Genome Atlas (TCGA) database, and the entire cohort was randomly divided into training and testing set at a ratio of 7:3. Prognostic genes were selected by Cox analysis, and a risk model and nomogram were subsequently built. The relationship between the model and immune cell infiltration was analyzed by single-sample gene set enrichment analysis (ssGSEA). Results A total of 76 RRIGs were identified, and they were mainly involved in immune-related biological processes and pathways. BMP2, COLEC10, MASP2, and GCGR were screened as prognostic genes after Cox regression analysis. Subsequently, these prognostic genes were used to construct a risk score model, which demonstrated good performance in predicting prognosis, as proven by the receiver operating characteristic (ROC) curves. Cox regression analysis showed that the risk score was an independent prognostic factor for RC. Moreover, we found that the immune microenvironment was different between the low- and high-risk groups. Conclusions We developed and validated a prognostic risk model based on RRIGs, which could serve as a tool for predicting prognosis of RC. These findings enhanced the understanding of the relationship among radiosensitivity, immune genes and prognosis in RC. Risk model Rectal cancer Radiosensitivity Immune gene Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Colorectal cancer (CRC) is one of the most common cancers and ranks third in malignant tumours and second in mortality worldwide[ 1 ]. Approximately one-third of CRC occurs in the rectum, of which 60% patients present in a locally advanced stage (cT3/T4 or N+)[ 2 ]. Currently, neoadjuvant chemoradiotherapy (nCRT) combined with total mesorectal excision (TME) is the standard treatment for locally advanced rectal cancer (LARC). It has been reported that the pathologic complete response (pCR) rate can be as high as 10–30% after nCRT, and these patients will have lower recurrence rate and higher survival rate[ 3 ]. However, about one-third of patients are less sensitive or resistant to nCRT, showing little tumor regression or even progressed after treatment[ 4 ]. At present, there is a lack of effective molecular markers to select which patients will benefit from nCRT. Radiotherapy is one of the most important means for treating tumors. Searching for factors that affect radiosensitivity and prognosis is of great significance for improving the treatment efficacy of tumors. An increasing number of studies have shown that immune genes can affect radiosensitivity and prognosis by regulating antitumor immune surveillance, antigen presentation and so on. Li et al. studied the relationship between gene expression profiles and nCRT response in LARC, and developed a 15-immune gene prediction model to predict the response to nCRT[ 5 ]. Carine et al. calculated the biopsy-adapted immunoscore (IS B ) based on the infiltration of CD3 + and CD8 + T cells, through which they could predict response to nCRT and better define patients eligible to an organ preservation strategy (Watch-and-Wait)[ 6 ]. However, the relationship between immune genes, radiosensitivity, and prognosis in RC is still unclear. Therefore, in this study, we developed a reliable prognosis model based on RRIGs, and verified the prognostic value of the model in RC patients. Materials And Methods Study Design The flowchart of the entire study is shown in Fig. 1 . Data sources GSE133057 dataset, including 33 RC samples with radiotherapy information was acquired from GEO. The clinical information and transcriptomic data of 145 RC patients were downloaded from the TCGA database. Moreover, the immune gene set was obtained from the ImmPort database. Screen DEGs between CR and iCR groups First, we use the R 4.2.1 “limma” software to perform log2 standardization on the mRNA data of the RC samples and take the average value of genes with multiple probes in the GSE133057 dataset. All patients in GSE133057 underwent preoperative radiotherapy based on 5-fluorouracil, with a radiation dose of 50.4 Gy/25f. Surgery was performed 8–12 weeks after the completion of radiotherapy. The response to nCRT was determined by postoperative pathological AJCC staging. AJCC0, complete response, defined as the lack of viable cancer cells; AJCC1, moderate response, defined as single cells or small groups of cancer cells; AJCC2, minimal response, defined as residual cancer outgrown by fibrosis; AJCC3, poor response, defined as minimal or no tumor response[ 7 ]. Patients with AJCC0 were defined as the complete response (CR) group, while patients with AJCC1-3 were defined as the incomplete complete response (iCR) group. DEGs between CR and iCR groups were screened using the “limma R” package. The selection criteria were |log 2 FC|>0.5 and p < 0.05. “Pheatmap” and “ggplot2” packages were used to generate heatmap and volcano plot. Identification of RRIGs RRIGs were identified by intersecting the DEGs and immune genes. GO/KEGG enrichment pathway analysis of RRIGs RRIGs were used for GO and KEGG enrichment pathway analysis. GO analysis includes biological process (BP), cellular component (CC), and molecular function (MF). GO and KEGG were determined to be significantly enriched when p.adjust < 0.05. Construction and validation of the prognostic risk model The prognostic risk model was established and validated using RC samples obtained from the TCGA. These samples were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, univariate Cox regression was performed to screen RRIGs significantly correlated with overall survival (OS). Subsequently, multivariate Cox regression was used to establish the risk model, and the risk score was calculated using the formula: Risk score = h0(t)*exp(β1X1 + β2X2+... + βnXn). In this formula, h0(t) is a constant, β represents the coefficient of multivariate Cox regression, X represents gene expression, and the hazard ratio (HR) can be obtained by taking exp(β). Basing on the median risk score, the cases in the training set were divided into high- and low-risk groups. Kaplan-Meier analysis was used to calculate OS, and the “survival ROC” package was used to plot ROC curves for 1, 3, and 5 years. Risk score distribution plots, survival status scatterplots, and heatmaps were used to evaluate the model. Testing set and the entire TCGA set were used to confirm the accuracy of the model. Independent prognostic analysis and development of a predictive nomogram Next, we incorporated the risk score, age, gender, stage and other clinicopathological factors for univariate and multivariate Cox regression analysis. Factors with p < 0.05 were determined to be independent prognostic factors. Additionally, we generated a nomogram to predict the survival for the RC patients and Harrell’s concordance index (C-index) was calculated to evaluate the prediction accuracy of the nomogram. Immune infiltration analysis To further explore the correlation between the immune microenvironment and risk score, the ssGSEA was used to analyze the infiltration of 28 immune cells. Wilcoxon test was performed to identify the differences between the high- and low-risk groups, and p < 0.05 was considered statistically significant. Furthermore, the correlations between the expressions of the four genes and the infiltration of 28 immune cells were calculated. Results Identification of DEGs Differential analysis between CR and iCR groups in the GSE133057 dataset was performed. A total of 1171 DEGs were identified, among which 698 were upregulated and 473 genes were downregulated in the CR group. A volcano plot and heatmap were generated to show the results (Fig. 2 A,B). Identification of RRIGs Taking intersection of the 698 upregulated genes, 473 downregulated genes and 1793 immune genes, we identified 76 RRIGs with 43 genes upregulated and 33 genes downregulated(Fig. 3 ). GO/KEGG enrichment pathway analysis A total of 460 GO terms were identified as significantly enriched, including 426 GO-BP terms and 34 GO-MF terms. The top 5 terms enriched in GO-BP and GO-MF were displayed in Fig. 4 A. Similarly, 60 KEGG terms were significantly enriched, which mainly included cytokine-cytokine receptor interaction, TNF signaling pathway, IL-17 signaling pathway, cAMP signaling pathway, Toll-like receptor signaling pathway, and so on(Fig. 4 B). Construction and validation of the predictive model After univariate Cox, 6 RRIGs affecting the OS of RC were identified in the training set (Fig. 5A). Then, these 6 genes were included in a multivariate Cox analysis and ultimately four genes, including BMP2, COLEC10, MASP2, and GCGR, were obtained to establish the risk model, as shown in Fig. 5B . The risk score of each patient = h0(t)*exp((-0.4436369xBMP2 expression)+(-3.2845855xCOLEC10 expression)+(-1.5165470xMASP2 expression)+(3.0814537xGCGR expression)). According to the median risk score, the training set was divided into a high-risk group (n = 52) and a low-risk group (n = 52). The Kaplan-Meier OS curves of the two groups were significantly different ( p = 0.0075, Fig. 6 A). The predictive efficiency of the risk model was studied using the ROC curve, which showed a moderate accuracy. The AUC values of the risk model at 1, 3, and 5-year of OS were 0.736, 0.896, and 0.744, respectively (Fig. 6 B). The risk score and the survival status of each patient were shown in the prognostic curve and scatter plot, respectively (Fig. 6 C). The gene expression profiles of the four genes were shown in the heatmap (Fig. 6 C). We validated the risk model using the testing set and the entire TCGA set. Based on the median risk score, the patients were stratified into high- or low-risk group in both sets. There were significant differences in OS between the two risk groups in both the testing set and the entire TCGA set ( p = 0.018, p = 0.017) (Fig. 7 A,D). The AUC values for 1, 3, and 5-year in the testing set were 0.754, 0.799, and 0.937, respectively. And the AUC values for 1, 3, and 5-year in the entire set were 0.736, 0.801, and 0.814, respectively (Fig. 7 B,E). The risk score distribution plots, survival status scatterplots, and heatmaps were shown in the figure (Fig. 7 C,F). Independent prognostic analysis of the risk score Univariate Cox regression analysis indicated that risk score, tumor stage and age were significantly associated with prognosis (Fig. 8 A). Then, multivariate Cox regression analysis indicated that risk score, tumor stage and age were independent prognostic factors (Fig. 8 B). Next, the nomogram was designed and the C-index of the nomogram was 0.84, indicating that the nomogram model had a certain predictive value (Fig. 8 C). Estimation of immune cell infiltration Significant differences were observed in the infiltration of CD56dim natural killer cells, immature dendritic cells, memory B cells, plasmacytoid dendritic cell and type 2 T helper cells between the two risk groups (Fig. 9 A). In addition, the Spearman correlations of four model genes with 28 immune cells were shown in Fig. 9 B-E. Discussion Rectal cancer is one of the most common cancers in the world. In recent years, more attention has been paid to the immune genes related to RC, and their effect on radiosensitivity and prognosis. Immune genes are associated with immune regulation and microenvironment of the tumors, the expression of which can affect patients’ response to radiotherapy[ 8 , 9 , 10 ].In this study, we established and validated a prognostic risk model based on four RRIGs (BMP2, COLEC10, MASP2, GCGR), the model can serve as an independent prognostic factor and has potential value in predicting the prognosis of RC patients. Furthermore, the survival of RC patients was predicted by the nomogram, and these genes may become new intervention targets. BMP2 acts as a tumor suppressor that is involved in cell apoptosis, and loss of BMP2 could lead to tumorigenesis[ 11 ]. Inactivation of BMP signaling pathway has been shown to be highly prevalent in colorectal cancers[ 12 ]. In addition, BMP2 encodes a secreted ligand of the TGF-β (transforming growth factor-beta) protein superfamily. The TGF-β signaling pathway plays a critical role in the tumor immune microenvironment by regulating cell growth, differentiation, proliferation, and apoptosis, which is a key regulatory pathway in CRC[ 13 ]. In our study, we found that BMP2 was highly expressed in the CR group, which may increase radiosensitivity by activating the TGF-β signaling pathway, inducing cell apoptosis and inhibiting proliferation, thus improving the prognosis. COLEC10, which encodes for collectin liver 1 (CL-L1), is rarely reported in RC but has been studied extensively in hepatocellular carcinoma (HCC). COLEC10 is highly expressed in normal liver, and its low expression predicts a worse survival for HCC patients[ 14 ]. Bai et al. reported that COLEC10 expression was negatively correlated with the stem cell index in HCC, suggesting that COLEC10 may be a potential therapeutic target for inhibiting HCC stem cells[ 15 ]. Other studies have reported that COLEC10 affects tumor immune microenvironment by affecting the infiltration of CD8 + T cells, B cells and dendritic cells (DCs), which is related to tumor treatment response[ 16 ]. Similar results were obtained in our study with enhanced COLEC10 expression in the CR group, affecting the infiltration of T cells and DCs, which was associated with radiosensitivity and better prognosis. MASP2 encodes a member of serine proteases, a plasma protein involved in the lectin pathway of the complement system, promotes cell proliferation and reduces cell apoptosis[ 17 ]. The relationship between MASP2 and radiotherapy has not been reported. Our study found that MASP2 expression increased in the iCR group, we hypothesize that MASP2 may influence the effect of radiotherapy through the enhanced proliferation of the tumor cells. GCGR (glucagon receptor), a gene expressed in various organs including liver, kidney, brain, intestine, pancreas and fat tissue, plays an important role in regulating glucose[ 18 ]. Glucagon mediates AMPK and MAPK pathways through GCGR, which promotes the progression of CRC[ 19 ]. Similarly, in this study, GCGR was highly expressed in the high-risk group, resulting in worse prognosis. Thus, targeting GCGR may be a novel approach to improve the efficacy of RC treatment. In order to understand the function of RRIGs, we conducted GO/KEGG enrichment analysis. The GO/KEGG enrichment pathways mainly included cytokine-mediated signaling pathway, epithelial cell proliferation, TNF signaling pathway, IL-17 signaling pathway, cAMP signaling pathway, Toll-like receptor signaling pathway, and so on. Cytokines are major regulators of innate and adaptive immunity, which play important roles in antitumor-specific immune response, such as IFN, GM-CSF, and IL-12[ 20 ]. Radiotherapy causes DNA damage of tumor cells, releasing tumor-related antigens, activating cytokine-mediated signaling pathways, activating immune cells and inducing tumor cell death, thus affecting the efficacy of treatment. In addition, epithelial cell proliferation has been found to be related to radiosensitivity, cancer cells with a high rate of proliferation tend to be more sensitive to radiation according to Bergonie-Tribondeau[ 21 ]. The cAMP signaling pathway is involved in a variety of physiological processes, including metabolism, gene expression, cell growth and differentiation. This signaling pathway can also regulate tumor immune microenvironment after radiotherapy, affecting the immune response and therapeutic effect[ 22 ]. Toll-like receptors (TLRs) are a type of receptors expressed on the surface of immune cells, especially antigen presenting cells (APCs), such as dendritic cells (DCs)[ 23 ]. Radiotherapy can damage tumor cells, releasing some damage-associated moleculars, which can activate TLRs, promote the maturation and activation of DCs, increase the infiltration and activition of T cells, and enhance the antibody production of B cells, enhancing the antitumor immune response. These enriched functions and pathways may help us understand the molecular mechanism of RRIGs regulating RC. Immune cell infiltration refers to the infiltration degree of immune cells in tumor tissues. Immune genes regulate immune cell infiltration and reshape tumor microenvironment, thereby affecting radiotherapy efficacy. Meanwhile, immune infiltration is crucial to tumor prognosis, a good immune status may enhance the efficacy of treatment and improve patients’ survival[ 24 ]. Therefore, it is likely that immune genes can be used as potential targets for treating patients with RC. In this study, we explored the correlation between the risk score and immune status, and found that the immature DC, memory B cell, plasmacytoid DC, and Type 2 T helper cell of the low-risk group were higher than those of the high-risk group. DC is the most functional antigen-presenting cell (APC), which can activate and enhance tumor antigen-specific T cells through delivering tumor-specific antigens and secreting cytokines. Meanwhile, DC can also inhibit tumor immune evasion by expressing inhibitory molecules and secreting inhibitory cytokines[ 25 ]. Memory B cells are important immune cells with long-term memory that can kill tumor cells by delivering tumor-specific antibodies. They participate in antitumor immune response and regulate T cell immune response to enhance immunosurveillance and clearance[ 26 ]. Simson et al. highlighted the potential role for Th2(Type 2 T helper cell)-mediated immune effector cells and their mediators in tumor immune surveillance and clearance[ 27 ]. Th2 cells may affect the therapeutic efficacy by regulating immune cells and cytokines in tumor microenvironment. The enhanced anti-tumor immune response and better prognosis of the low-risk group confirm that our model is reliable in predicting cancer prognosis. Our study elucidates the relationship between immune genes, radiosensitivity and prognosis of RC. The immune status and immune cell infiltration of patients can affect their response to radiotherapy, thereby influencing prognosis. At the same time, radiotherapy promotes the release of tumor-associated antigens, activates immune system, strengthens the antitumor response, and thus improves the prognosis of RC. However, there are some limitations in this study. Firstly, there are few samples of RC in the TCGA database, which results in a relatively small sample size in this study. Secondly, due to the lack of survival information for RC patients in the GEO database, both the modeling and validation data are derived from TCGA. Thirdly, it is difficult to obtain biopsy tissue samples before nCRT, the study lacks validation from clinical specimens. Conclusion The signature with four RRIGs (BMP2, COLEC10, MASP2, and GCGR) might be an independent prognostic factor of RC patients and could be associated with the immune cell infiltration of RC patients. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: The datasets generated and/or analysed during the current study are available in GSE133057 dataset from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and the ImmPort (https://www.immport.org/) database. Competing interests: The authors declare that they have no competing interests. Funding: Not applicable. Authors’ contributions: Conception and design of the study: YH and DL; Acquisition and interpretation of data: LY and MXF; Analysis of data: YH, LY, WK, HMD, and LHM; Image editing: YH, LXQ and YDJ; Supervision: YH, LY and DL; Drafting the manuscript: YH and LY; Revision of the manuscript: DL and MXF; All authors read and approved the final manuscript. Acknowledgements: We are particularly grateful to all the people who have given us help on our article. References Siegel RL, Miller KD, Fuchs HE, et al. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. Capelli G, De Simone I, Spolverato G, et al. Non-Operative Management Versus Total Mesorectal Excision for Locally Advanced Rectal Cancer with Clinical Complete Response After Neoadjuvant Chemoradiotherapy: a GRADE Approach by the Rectal Cancer Guidelines Writing Group of the Italian Association of Medical Oncology (AIOM). J Gastrointest Surg. 2020;24(9):2150–9. Maas M, Nelemans PJ, Valentini V, et al. 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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-2712006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":188402201,"identity":"a9898de3-f3b1-411c-9cda-adfd09ec3e21","order_by":0,"name":"Hui Yang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""},{"id":188402202,"identity":"f8bf1b93-cf35-4c49-a42d-5a1356feb85a","order_by":1,"name":"Yin Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Liu","suffix":""},{"id":188402203,"identity":"40e079c2-2581-44e4-8951-4448d70ae1eb","order_by":2,"name":"Xiaofeng Mu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Mu","suffix":""},{"id":188402205,"identity":"4a228eef-4aa6-4784-b3f9-5b4af53d49e9","order_by":3,"name":"Kun Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":188402207,"identity":"f2bf9f6b-e72b-426f-80ac-32668bdf1dc4","order_by":4,"name":"Mengdi Hao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengdi","middleName":"","lastName":"Hao","suffix":""},{"id":188402209,"identity":"2fa5be0b-ce3d-4c50-adf5-acd1037b492c","order_by":5,"name":"Huimin Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Li","suffix":""},{"id":188402211,"identity":"c42fe785-405f-41a7-92bd-7767242b6c3f","order_by":6,"name":"Xiaoqing Liang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Liang","suffix":""},{"id":188402212,"identity":"1e4e80ba-b8d9-4de7-8dc0-f4ef13cf76fb","order_by":7,"name":"Dajin Yuan","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dajin","middleName":"","lastName":"Yuan","suffix":""},{"id":188402213,"identity":"f195d6a0-68a1-4317-bf7d-165c2edced70","order_by":8,"name":"Lei Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PsarCMBSA4QOBuMRmPQXxGQJCcSjcVzkidFMcO4g41UF9Fx+h3oAuEdcODnURxzq6iPHexanNKJgfzhA4H+EA+HyfmLZTAgrO5LmkNHYkBNiRrRVTpUkcSP5HIA5Xhofn7LdZBPu2vlLaF6oY7VLiOcjFkmpJqIOkTwYtGScFiROgOWxqidIiUoPsRSgqCC+gcORCHv9kQko7kV45mKOw50dA5EBC+wvQDoVsZUOkPBGNtwRH06uq6eyHM7a93R9xVy7W9cTG8f0lmtZfscply+fz+b64J8r2SqNrhxtMAAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2023-03-20 02:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2712006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2712006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35289299,"identity":"bb344504-5911-43f6-b69a-2d175d66cb3a","added_by":"auto","created_at":"2023-04-04 19:52:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68947,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study. CR: complete response; iCR: incomplete complete response; RC: rectal cancer; RRIGs: radiosensitivity-related immune genes.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/b19587677690ba3ee1126b20.jpg"},{"id":35288505,"identity":"c20c39b8-a8ec-4a0c-98ad-e0a1d2dad8bd","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241152,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs between CR and iCR groups. (A) Volcano plot for DEGs. (B) Heatmap of the DEGs.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/46f89759294ee0b975093e46.jpg"},{"id":35288499,"identity":"fe968b11-f9c0-482c-a8ec-c3591fda5a32","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53592,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram. 76 RRIGs were identified with 43 upregulated and 33 downregulated.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/49d04149d1dce6e0b6e7594d.jpg"},{"id":35289298,"identity":"68db8420-68b6-41d2-b0b4-b0114b787c98","added_by":"auto","created_at":"2023-04-04 19:52:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172690,"visible":true,"origin":"","legend":"\u003cp\u003eGO/KEGG enrichment pathway analysis. (A) The top 5 terms enriched by GO-BP/MF of RRIGs. (B) The top 10 terms enriched by KEGG of RRIGs.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/52ac5de46eaeb9be01a29f7e.jpg"},{"id":35288500,"identity":"3d602a1d-4da6-4c80-8d75-6ff14bdb2180","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106545,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of prognostic RRIGs. (A) Univariate forest plot of the correlation between the expression of RRIGs and the OS of RC patients. (B) Multivariate forest plot of the correlation between the expression of RRIGs and the OS\u003c/p\u003e\n\u003cp\u003eof RC patients.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/d075d7e42056ff6d18e8f63b.jpg"},{"id":35288502,"identity":"18f75750-740f-421d-9091-8d49ce17b458","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":240953,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the risk score model in the training set. (A) KM survival curves for the high and low-risk groups(\u003cem\u003ep\u003c/em\u003e=0.0075). (B) ROC curve for the training set. (C) The prognostic curve, scatter plot and heatmap of the training set.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/bf8f47e9e72618b13481f7cf.jpg"},{"id":35288506,"identity":"bb766bc8-da4f-4d8a-a401-c84e83c08681","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":481286,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the risk score model using the testing set and the entire TCGA set. (A) KM survival curves for the high and low-risk groups in the testing set (\u003cem\u003ep\u003c/em\u003e=0.018). (B) ROC curve for the testing set. (C) The prognostic curve, scatter plot and heatmap of the testing set. (D) KM survival curves for the high and low-risk groups in the entire TCGA set (\u003cem\u003ep\u003c/em\u003e=0.017). (E) ROC curve for the entire TCGA set. (F) The prognostic curve, scatter plot and heatmap of the entire TCGA set.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/57eb66e1ddb191a56fb1ea53.jpg"},{"id":35288501,"identity":"2ecc1e2d-fb9d-41c9-a81a-ea4e61d63a87","added_by":"auto","created_at":"2023-04-04 19:44:23","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":173831,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic analysis of the risk score. (A) Univariate Cox regression. (B) Multivariate Cox regression analysis (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). (C) The nomogram designed with the risk score, stage and age (C-index=0.84).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/729f6b4c463ca9687bbbffd9.jpg"},{"id":35289300,"identity":"bb7d35be-21e1-4f5f-bc96-72ca1713b878","added_by":"auto","created_at":"2023-04-04 19:52:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":673905,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune microenvironment was different between the high and low-risk groups. (A) Immune cell infiltration in the high and low-risk groups. (B-E) Spearman correlations of four model genes and immune cell. *p \u0026lt; 0.05, “ns” indicates p≥0.05, not statistically significant.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/3560ae8864a9c8c59669b064.jpg"},{"id":41726808,"identity":"8ce2edc4-ab9d-42db-8ad8-c97d79fdce20","added_by":"auto","created_at":"2023-08-17 18:22:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":792693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2712006/v1/9ad759e3-6daa-4bce-a0fe-0503be326963.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most common cancers and ranks third in malignant tumours and second in mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately one-third of CRC occurs in the rectum, of which 60% patients present in a locally advanced stage (cT3/T4 or N+)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, neoadjuvant chemoradiotherapy (nCRT) combined with total mesorectal excision (TME) is the standard treatment for locally advanced rectal cancer (LARC). It has been reported that the pathologic complete response (pCR) rate can be as high as 10\u0026ndash;30% after nCRT, and these patients will have lower recurrence rate and higher survival rate[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, about one-third of patients are less sensitive or resistant to nCRT, showing little tumor regression or even progressed after treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, there is a lack of effective molecular markers to select which patients will benefit from nCRT.\u003c/p\u003e \u003cp\u003eRadiotherapy is one of the most important means for treating tumors. Searching for factors that affect radiosensitivity and prognosis is of great significance for improving the treatment efficacy of tumors. An increasing number of studies have shown that immune genes can affect radiosensitivity and prognosis by regulating antitumor immune surveillance, antigen presentation and so on. Li et al. studied the relationship between gene expression profiles and nCRT response in LARC, and developed a 15-immune gene prediction model to predict the response to nCRT[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Carine et al. calculated the biopsy-adapted immunoscore (IS\u003csub\u003eB\u003c/sub\u003e) based on the infiltration of CD3\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, through which they could predict response to nCRT and better define patients eligible to an organ preservation strategy (Watch-and-Wait)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the relationship between immune genes, radiosensitivity, and prognosis in RC is still unclear. Therefore, in this study, we developed a reliable prognosis model based on RRIGs, and verified the prognostic value of the model in RC patients.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe flowchart of the entire study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eGSE133057 dataset, including 33 RC samples with radiotherapy information was acquired from GEO. The clinical information and transcriptomic data of 145 RC patients were downloaded from the TCGA database. Moreover, the immune gene set was obtained from the ImmPort database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eScreen DEGs between CR and iCR groups\u003c/h2\u003e \u003cp\u003eFirst, we use the R 4.2.1 \u0026ldquo;limma\u0026rdquo; software to perform log2 standardization on the mRNA data of the RC samples and take the average value of genes with multiple probes in the GSE133057 dataset. All patients in GSE133057 underwent preoperative radiotherapy based on 5-fluorouracil, with a radiation dose of 50.4 Gy/25f. Surgery was performed 8\u0026ndash;12 weeks after the completion of radiotherapy. The response to nCRT was determined by postoperative pathological AJCC staging. AJCC0, complete response, defined as the lack of viable cancer cells; AJCC1, moderate response, defined as single cells or small groups of cancer cells; AJCC2, minimal response, defined as residual cancer outgrown by fibrosis; AJCC3, poor response, defined as minimal or no tumor response[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with AJCC0 were defined as the complete response (CR) group, while patients with AJCC1-3 were defined as the incomplete complete response (iCR) group. DEGs between CR and iCR groups were screened using the \u0026ldquo;limma R\u0026rdquo; package. The selection criteria were |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. \u0026ldquo;Pheatmap\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; packages were used to generate heatmap and volcano plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of RRIGs\u003c/h2\u003e \u003cp\u003eRRIGs were identified by intersecting the DEGs and immune genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGO/KEGG enrichment pathway analysis of RRIGs\u003c/h2\u003e \u003cp\u003eRRIGs were used for GO and KEGG enrichment pathway analysis. GO analysis includes biological process (BP), cellular component (CC), and molecular function (MF). GO and KEGG were determined to be significantly enriched when p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the prognostic risk model\u003c/h2\u003e \u003cp\u003eThe prognostic risk model was established and validated using RC samples obtained from the TCGA. These samples were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, univariate Cox regression was performed to screen RRIGs significantly correlated with overall survival (OS). Subsequently, multivariate Cox regression was used to establish the risk model, and the risk score was calculated using the formula: Risk score\u0026thinsp;=\u0026thinsp;h0(t)*exp(β1X1\u0026thinsp;+\u0026thinsp;β2X2+...\u0026thinsp;+\u0026thinsp;βnXn). In this formula, h0(t) is a constant, β represents the coefficient of multivariate Cox regression, X represents gene expression, and the hazard ratio (HR) can be obtained by taking exp(β). Basing on the median risk score, the cases in the training set were divided into high- and low-risk groups. Kaplan-Meier analysis was used to calculate OS, and the \u0026ldquo;survival ROC\u0026rdquo; package was used to plot ROC curves for 1, 3, and 5 years. Risk score distribution plots, survival status scatterplots, and heatmaps were used to evaluate the model. Testing set and the entire TCGA set were used to confirm the accuracy of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis and development of a predictive nomogram\u003c/h2\u003e \u003cp\u003eNext, we incorporated the risk score, age, gender, stage and other clinicopathological factors for univariate and multivariate Cox regression analysis. Factors with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were determined to be independent prognostic factors. Additionally, we generated a nomogram to predict the survival for the RC patients and Harrell\u0026rsquo;s concordance index (C-index) was calculated to evaluate the prediction accuracy of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eTo further explore the correlation between the immune microenvironment and risk score, the ssGSEA was used to analyze the infiltration of 28 immune cells. Wilcoxon test was performed to identify the differences between the high- and low-risk groups, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Furthermore, the correlations between the expressions of the four genes and the infiltration of 28 immune cells were calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eDifferential analysis between CR and iCR groups in the GSE133057 dataset was performed. A total of 1171 DEGs were identified, among which 698 were upregulated and 473 genes were downregulated in the CR group. A volcano plot and heatmap were generated to show the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of RRIGs\u003c/h2\u003e \u003cp\u003eTaking intersection of the 698 upregulated genes, 473 downregulated genes and 1793 immune genes, we identified 76 RRIGs with 43 genes upregulated and 33 genes downregulated(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO/KEGG enrichment pathway analysis\u003c/h2\u003e \u003cp\u003eA total of 460 GO terms were identified as significantly enriched, including 426 GO-BP terms and 34 GO-MF terms. The top 5 terms enriched in GO-BP and GO-MF were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Similarly, 60 KEGG terms were significantly enriched, which mainly included cytokine-cytokine receptor interaction, TNF signaling pathway, IL-17 signaling pathway, cAMP signaling pathway, Toll-like receptor signaling pathway, and so on(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the predictive model\u003c/h2\u003e \u003cp\u003eAfter univariate Cox, 6 RRIGs affecting the OS of RC were identified in the training set (Fig.\u0026nbsp;5A). Then, these 6 genes were included in a multivariate Cox analysis and ultimately four genes, including BMP2, COLEC10, MASP2, and GCGR, were obtained to establish the risk model, as shown in \u003cb\u003eFig.\u0026nbsp;5B\u003c/b\u003e. The risk score of each patient\u0026thinsp;=\u0026thinsp;h0(t)*exp((-0.4436369xBMP2 expression)+(-3.2845855xCOLEC10 expression)+(-1.5165470xMASP2 expression)+(3.0814537xGCGR expression)). According to the median risk score, the training set was divided into a high-risk group (n\u0026thinsp;=\u0026thinsp;52) and a low-risk group (n\u0026thinsp;=\u0026thinsp;52). The Kaplan-Meier OS curves of the two groups were significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0075, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The predictive efficiency of the risk model was studied using the ROC curve, which showed a moderate accuracy. The AUC values of the risk model at 1, 3, and 5-year of OS were 0.736, 0.896, and 0.744, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The risk score and the survival status of each patient were shown in the prognostic curve and scatter plot, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The gene expression profiles of the four genes were shown in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). \u003c/p\u003e \u003cp\u003eWe validated the risk model using the testing set and the entire TCGA set. Based on the median risk score, the patients were stratified into high- or low-risk group in both sets. There were significant differences in OS between the two risk groups in both the testing set and the entire TCGA set (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA,D). The AUC values for 1, 3, and 5-year in the testing set were 0.754, 0.799, and 0.937, respectively. And the AUC values for 1, 3, and 5-year in the entire set were 0.736, 0.801, and 0.814, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB,E). The risk score distribution plots, survival status scatterplots, and heatmaps were shown in the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC,F).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis of the risk score\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression analysis indicated that risk score, tumor stage and age were significantly associated with prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Then, multivariate Cox regression analysis indicated that risk score, tumor stage and age were independent prognostic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Next, the nomogram was designed and the C-index of the nomogram was 0.84, indicating that the nomogram model had a certain predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of immune cell infiltration\u003c/h2\u003e \u003cp\u003eSignificant differences were observed in the infiltration of CD56dim natural killer cells, immature dendritic cells, memory B cells, plasmacytoid dendritic cell and type 2 T helper cells between the two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In addition, the Spearman correlations of four model genes with 28 immune cells were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-E.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRectal cancer is one of the most common cancers in the world. In recent years, more attention has been paid to the immune genes related to RC, and their effect on radiosensitivity and prognosis. Immune genes are associated with immune regulation and microenvironment of the tumors, the expression of which can affect patients\u0026rsquo; response to radiotherapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].In this study, we established and validated a prognostic risk model based on four RRIGs (BMP2, COLEC10, MASP2, GCGR), the model can serve as an independent prognostic factor and has potential value in predicting the prognosis of RC patients. Furthermore, the survival of RC patients was predicted by the nomogram, and these genes may become new intervention targets.\u003c/p\u003e \u003cp\u003eBMP2 acts as a tumor suppressor that is involved in cell apoptosis, and loss of BMP2 could lead to tumorigenesis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Inactivation of BMP signaling pathway has been shown to be highly prevalent in colorectal cancers[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, BMP2 encodes a secreted ligand of the TGF-β (transforming growth factor-beta) protein superfamily. The TGF-β signaling pathway plays a critical role in the tumor immune microenvironment by regulating cell growth, differentiation, proliferation, and apoptosis, which is a key regulatory pathway in CRC[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our study, we found that BMP2 was highly expressed in the CR group, which may increase radiosensitivity by activating the TGF-β signaling pathway, inducing cell apoptosis and inhibiting proliferation, thus improving the prognosis.\u003c/p\u003e \u003cp\u003eCOLEC10, which encodes for collectin liver 1 (CL-L1), is rarely reported in RC but has been studied extensively in hepatocellular carcinoma (HCC). COLEC10 is highly expressed in normal liver, and its low expression predicts a worse survival for HCC patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Bai et al. reported that COLEC10 expression was negatively correlated with the stem cell index in HCC, suggesting that COLEC10 may be a potential therapeutic target for inhibiting HCC stem cells[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other studies have reported that COLEC10 affects tumor immune microenvironment by affecting the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells, B cells and dendritic cells (DCs), which is related to tumor treatment response[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similar results were obtained in our study with enhanced COLEC10 expression in the CR group, affecting the infiltration of T cells and DCs, which was associated with radiosensitivity and better prognosis.\u003c/p\u003e \u003cp\u003eMASP2 encodes a member of serine proteases, a plasma protein involved in the lectin pathway of the complement system, promotes cell proliferation and reduces cell apoptosis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The relationship between MASP2 and radiotherapy has not been reported. Our study found that MASP2 expression increased in the iCR group, we hypothesize that MASP2 may influence the effect of radiotherapy through the enhanced proliferation of the tumor cells.\u003c/p\u003e \u003cp\u003eGCGR (glucagon receptor), a gene expressed in various organs including liver, kidney, brain, intestine, pancreas and fat tissue, plays an important role in regulating glucose[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Glucagon mediates AMPK and MAPK pathways through GCGR, which promotes the progression of CRC[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, in this study, GCGR was highly expressed in the high-risk group, resulting in worse prognosis. Thus, targeting GCGR may be a novel approach to improve the efficacy of RC treatment.\u003c/p\u003e \u003cp\u003eIn order to understand the function of RRIGs, we conducted GO/KEGG enrichment analysis. The GO/KEGG enrichment pathways mainly included cytokine-mediated signaling pathway, epithelial cell proliferation, TNF signaling pathway, IL-17 signaling pathway, cAMP signaling pathway, Toll-like receptor signaling pathway, and so on. Cytokines are major regulators of innate and adaptive immunity, which play important roles in antitumor-specific immune response, such as IFN, GM-CSF, and IL-12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Radiotherapy causes DNA damage of tumor cells, releasing tumor-related antigens, activating cytokine-mediated signaling pathways, activating immune cells and inducing tumor cell death, thus affecting the efficacy of treatment. In addition, epithelial cell proliferation has been found to be related to radiosensitivity, cancer cells with a high rate of proliferation tend to be more sensitive to radiation according to Bergonie-Tribondeau[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The cAMP signaling pathway is involved in a variety of physiological processes, including metabolism, gene expression, cell growth and differentiation. This signaling pathway can also regulate tumor immune microenvironment after radiotherapy, affecting the immune response and therapeutic effect[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Toll-like receptors (TLRs) are a type of receptors expressed on the surface of immune cells, especially antigen presenting cells (APCs), such as dendritic cells (DCs)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Radiotherapy can damage tumor cells, releasing some damage-associated moleculars, which can activate TLRs, promote the maturation and activation of DCs, increase the infiltration and activition of T cells, and enhance the antibody production of B cells, enhancing the antitumor immune response. These enriched functions and pathways may help us understand the molecular mechanism of RRIGs regulating RC.\u003c/p\u003e \u003cp\u003eImmune cell infiltration refers to the infiltration degree of immune cells in tumor tissues. Immune genes regulate immune cell infiltration and reshape tumor microenvironment, thereby affecting radiotherapy efficacy. Meanwhile, immune infiltration is crucial to tumor prognosis, a good immune status may enhance the efficacy of treatment and improve patients\u0026rsquo; survival[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, it is likely that immune genes can be used as potential targets for treating patients with RC. In this study, we explored the correlation between the risk score and immune status, and found that the immature DC, memory B cell, plasmacytoid DC, and Type 2 T helper cell of the low-risk group were higher than those of the high-risk group. DC is the most functional antigen-presenting cell (APC), which can activate and enhance tumor antigen-specific T cells through delivering tumor-specific antigens and secreting cytokines. Meanwhile, DC can also inhibit tumor immune evasion by expressing inhibitory molecules and secreting inhibitory cytokines[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Memory B cells are important immune cells with long-term memory that can kill tumor cells by delivering tumor-specific antibodies. They participate in antitumor immune response and regulate T cell immune response to enhance immunosurveillance and clearance[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Simson et al. highlighted the potential role for Th2(Type 2 T helper cell)-mediated immune effector cells and their mediators in tumor immune surveillance and clearance[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Th2 cells may affect the therapeutic efficacy by regulating immune cells and cytokines in tumor microenvironment. The enhanced anti-tumor immune response and better prognosis of the low-risk group confirm that our model is reliable in predicting cancer prognosis.\u003c/p\u003e \u003cp\u003eOur study elucidates the relationship between immune genes, radiosensitivity and prognosis of RC. The immune status and immune cell infiltration of patients can affect their response to radiotherapy, thereby influencing prognosis. At the same time, radiotherapy promotes the release of tumor-associated antigens, activates immune system, strengthens the antitumor response, and thus improves the prognosis of RC. However, there are some limitations in this study. Firstly, there are few samples of RC in the TCGA database, which results in a relatively small sample size in this study. Secondly, due to the lack of survival information for RC patients in the GEO database, both the modeling and validation data are derived from TCGA. Thirdly, it is difficult to obtain biopsy tissue samples before nCRT, the study lacks validation from clinical specimens.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe signature with four RRIGs (BMP2, COLEC10, MASP2, and GCGR) might be an independent prognostic factor of RC patients and could be associated with the immune cell infiltration of RC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available in GSE133057 dataset from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and the ImmPort (https://www.immport.org/) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eConception and design of the study:\u0026nbsp;YH and DL;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAcquisition and interpretation of data: LY\u0026nbsp;and MXF;\u0026nbsp;Analysis of data:\u0026nbsp;YH, LY, WK, HMD, and LHM;\u0026nbsp;Image editing:\u0026nbsp;YH, LXQ and YDJ; Supervision:\u0026nbsp;YH, LY and DL;\u0026nbsp;Drafting the manuscript: YH and\u0026nbsp;LY; Revision of the manuscript: DL and MXF;\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We are particularly grateful to all the people who have given us help on our article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, et al. 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Cancer and IgE. 2010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-60761-451-7_11\u003c/span\u003e\u003cspan address=\"10.1007/978-1-60761-451-7_11\" 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":"Risk model, Rectal cancer, Radiosensitivity, Immune gene, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-2712006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2712006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRadiotherapy is closely related to the tumor immune microenvironment, but the role of immune genes in radiosensitivity and prognosis of rectal cancer (RC) is still unclear. This study aims to construct a prognostic risk model based on radiosensitivity-related immune genes (RRIGs), which can be used for predicting prognosis of RC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGSE133057 dataset of RC was downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between different radiosensitivity groups. RRIGs were obtained by intersecting DEGs and immune genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed to study the biological functions of RRIGs. Transcriptomic and clinical data of RC were downloaded from The Cancer Genome Atlas (TCGA) database, and the entire cohort was randomly divided into training and testing set at a ratio of 7:3. Prognostic genes were selected by Cox analysis, and a risk model and nomogram were subsequently built. The relationship between the model and immune cell infiltration was analyzed by single-sample gene set enrichment analysis (ssGSEA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 76 RRIGs were identified, and they were mainly involved in immune-related biological processes and pathways. BMP2, COLEC10, MASP2, and GCGR were screened as prognostic genes after Cox regression analysis. Subsequently, these prognostic genes were used to construct a risk score model, which demonstrated good performance in predicting prognosis, as proven by the receiver operating characteristic (ROC) curves. Cox regression analysis showed that the risk score was an independent prognostic factor for RC. Moreover, we found that the immune microenvironment was different between the low- and high-risk groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe developed and validated a prognostic risk model based on RRIGs, which could serve as a tool for predicting prognosis of RC. These findings enhanced the understanding of the relationship among radiosensitivity, immune genes and prognosis in RC.\u003c/p\u003e","manuscriptTitle":"Construction and validation of prognostic risk model based on radiosensitivity-related immune genes in rectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-04 19:44:18","doi":"10.21203/rs.3.rs-2712006/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":"cbfd2809-4320-449c-be8b-d8d908d90074","owner":[],"postedDate":"April 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-17T18:14:19+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-04 19:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2712006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2712006","identity":"rs-2712006","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}