Ectopic expression of chemokines and chemokine receptors in relation to immune cell infiltration, prostate cancer recurrence

Ectopic expression of chemokines and chemokine receptors in relation to immune cell infiltration, prostate cancer recurrence | 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 Ectopic expression of chemokines and chemokine receptors in relation to immune cell infiltration, prostate cancer recurrence Jialong Zhang, Cong Huang, Hongzhi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117470/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 Interleukins can nurture a tumor promoting environment and simultaneously regulate immune cell infiltration. However, the potential roles of interleukins in the prostate cancer immune landscape remain abstruse. Methods We comprehensively investigated the interleukin expression patterns and tumor immune landscape of prostate cancer patients. And explored the interleukin expression patterns with immune infiltration landscape. The interleukin score was established using LASSO cox regression analysis. Multivariate Cox regression analysis was employed to assess the prognostic value of the interleukin score. Results We identified two distinct interleukin clusters, characterized by different immune cell infiltration, tumor promoting signaling pathways activation and prognosis. The interleukin score was established to estimate the prognosis of individual prostate cancer patient. Further analysis demonstrated that the interleukin score was an independent prognostic factor of PRAD. Finally, we investigated the predictive value of interleukin score in the programed cell death protein (PD-1) blockade therapy of patients with prostate cancer. Conclusions This study demonstrated the correlation between interleukin and tumor immune landscape in prostate cancer. The comprehensive evaluation of interleukin expression patterns in individual prostate patients contribute to our understanding of the immune landscape and helps clinicians selecting proper immunotherapy strategies for prostate patients. chemokine prostate cancer prognostic signature immune cell infiltration drug selection immunotherapies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Prostate cancer is the most common malignancy in men worldwide and metastasis is the leading cause of cancer death [ 1 ] . Although patients with localized tumors have encouraging prognosis, the five-year survival rate for metastatic patients descends to 30% [ 2 ] . Standard treatment advanced prostate cancer relies on androgen-deprivation therapies, which could restrict tumor growth in the early stage but eventually lead to the recurrence and generation of castration-resistant prostate cancer [ 3 ] . Moreover, treatment failure is usually connected with formation of even more invasive subtypes, like neuroendocrine prostate cancer [ 4 , 5 ] . Hence, figuring out the driver genes that promote the conversion from localized to metastatic prostate cancer would contribute to develop novel agents for prostate cancer patients. The interleukin and interleukin receptors play essential roles in tumor progression and cancer dissemination [ 6 ] . After interacting with their corresponding receptors, interleukins activate various oncogenic signaling pathways such as STAT3/RORγ, PI3K and MAPK signaling [ 7 – 9 ] . Many interleukins and interleukin receptors like IL-6, IL-23 and IL-17 have been proved to play important roles in prostate tumor growth and androgen-deprivation therapy resistance [ 8 , 10 , 11 ] . Interleukins promote tumor progression by either directly acting on tumor cells or employing regulatory effects on immune cells or stromal cells in tumor microenvironment [ 12 , 13 ] . Immunotherapies like PD-1 or CTLA-4 inhibition have shown promising effects in many solid tumors’ treatment, however, their efficacy in prostate cancer seems to be limited [ 14 , 15 ] . Considering the critical roles of chemokines in mediating immune cell trafficking and activation, they could be novel targets for immunotherapy agent development. In the present study, we systematically explored mutation and transcriptional profiles and clinical significance of interleukin and interleukin receptors in prostate cancer patients. Tumor subtypes with different prognosis and immune cell infiltration status was identified based on interleukin and interleukin receptors expression. Subsequently, an interleukin related risk model was constructed to predict tumor recurrence. We further investigated the relationship between this risk model and tumor mutation burden, immune cell infiltration and target therapy response. Our study provided a better understanding of interleukin and interleukin receptors in PRAD, which would help to select proper immunotherapies. Material and Methods Patient and Samples mRNA expression data (raw counts) of 495 and 388 PRAD patients with matched clinical annotations including tumor stage, gender and recurrence free time were downloaded from the Cancer Genomes Atlas (TCGA) and Gene expression Omnibus (GEO) database respectively. Gene somatic mutation data (MAF files) of PRAD was obtained from TCGA. Identification of PRAD subtypes Chemokines and chemokine receptors were summarized from previous study and a total of 67 genes was obtained [ 6 ] . Unsupervised clustering algorithm was employed for cluster analysis of these genes. R package Consensus-Clusterplus package was used for the analysis and the repetition was set to 100 to ensure the credibility of the classification. Calculation of immune cell and stromal cell infiltration ESTIMATE algorithm, which could portrait the general infiltration level of stromal and immune cell, was used to calculate immune score and stromal score. Single sample GSEA was used to assess the infiltration of 28 immune cells. Moreover, microenvironment cell population counter (MCP-counter) was also used to evaluate the absolute abundance of immune and stromal cells in the tumor microenvironment. Pathways and enrichment analysis To explore potential pathways and biological processes correlated with chemokine signature, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses was conducted using R package “clusterProfiler”. Tumor mutational burden (TMB) analysis Tumor mutational burden was defined as mutations per million bases. Tumor mutation data was obtained from TCGA database and subsequent analysis was conducted by R package “maftools”. Drug and immunotherapy sensitivity prediction Drug sensitivity prediction was conducted as previously described [ 16 ] . Gene expression profile and somatic data of human cancer cell lines (CCLs) were downloaded from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) project ( https://portals.broadinstitute.org/ccle/ ) [ 17 ] . Drug sensitivity data of CLLs were obtained from the Cancer Therapeutics Response Portal (CTRP v.2.0, released October 2015, https://portals.broadinstitute.org/ctrp ) and PRISM Repurposing dataset (19Q4, released December 2019, https://depmap.org/portal/prism/ ). The CTRP contains the drug sensitivity data of 481 compounds in 835 CCLs and the PRISM contains the sensitivity data of 1448 compounds in 482 CLLs. Both datasets use the area under the dose-response curve (AUC) values as a calibration of drug sensitivity and lower AUC values suggest increased sensitivity to treatment. K-nearest neighbor (k-NN) imputation was used to impute the missing AUC values. The sensitivity to anti-PD-1 and anti-CTLA-4 therapy was predicted by comparing the gene expression of tumor subtypes with 47 melanoma patients treated with immunotherapy using subclass mapping ( https://www.genepattern.org/ ). Construction of chemokine related risk score Univariate cox analysis was used to identify genes correlated with recurrence (P < 0.05). Thereafter, recurrence related genes was analyzed with the LASSO multivariate Cox regression algorithm using R package “glmnet”. Then, the key genes and coefficients in the risk score signature were identified according to the most suitable penalty parameter λ. The risk score formula was Risk score = \(\sum _{i=1}^{n}{Coef}_{i}*{Exp}_{i}\) , where Coefi is the coefficient and Expi is the normalized expression of each gene. The risk score was constructed based on the training set and validated in the external cohort. Statistical analysis All statistical analysis was conducted with R software ( https://www.r-project.org/ ). Unpaired student t test was used to compare two groups with normally distributed variables. One way analysis and Kruskal-Wallis test of variance were used as parametric and non-parametric methods for three groups comparing. The surv-cutpoint function of survminer package was used to determine the optimal cutoff point in each dataset. Detailly, all potential cutoff points were tested to identify the maximum rank statistic and subsequently patients were divided into risk score high and risk score low group according to optimal cutoff point. Survival curves were plot with Kaplan-Meier method and p value was calculated with log rank test. P value less than 0.05 was considered as statistically significant. Results Somatic alternation landscape of interleukins To study the genomic features of interleukins in prostate cancer, we portrayed the mutation and CAN frequency of 492 PRAD patients in the TCGA cohort (Fig. 1 A and B). The DNA mutation of interleukins occurred in about 46% of patients and overall mutation level ranged from 0 to 9%. The interleukins with the highest mutation frequency are IL17C (9%), IL7(7%), IL-34(6%), IL-21(4%) and IL-2(3%) and their most common mutations are deep deletion, amplification, missense mutation and truncating mutation. Importantly, log-rank test indicates that patients with interleukins altered had worse overall survival ( P = 0.046) (Fig. 1 G). Moreover, patients in the altered group had significant higher biochemical recurrence (15.04% vs 8.65%, P = 0.0279) and primary lymph node positive rates (88.05% vs 80.83%, P = 0.0358) (Fig. 1 I J). Besides, patients in the mutated group had higher tumor mutation burden (TMB), mutation count and fraction genome altered. These indicates that mutation and copy number variation of interleukins might co-occurrence with other genes (Fig. 1 C D E and F). In summary, these results suggest that mutation and copy number variation of interleukins means worse overall survival and higher biochemical recurrence rate. Identification of interleukin subtypes in PRAD Many interleukins had similar effects in promote tumor progression and anti-tumor immunity. To investigate whether interleukin co-expressed with each other, we calculated the correlation of these genes. Results showed that the expression of most interleukins significantly correlated with each other (Supplementary Fig. 1). This indicates that interleukins might cooperate with each other to regulate the progression of prostate cancer. Subsequently, based on the expression levels of interleukins from TCGA, two distinct expression patterns were identified using the unsupervised clustering method, including 148 cases in the interleukin cluster1 and 347 cases in interleukin cluster2 (Fig. 2 A). T-SNE analysis showed that patients could roughly divided into two groups, which further corroborated two distinguished subtypes (Fig. 2 B). Moreover, we found patients in cluster 1 had worse recurrence outcomes in the cohort (Fig. 2 C). Similar results were found in external independent cohort (Fig. 2 D-E). The relationship between clinical factors in TCGA cohort and interleukin subtypes were also explored (Fig. 2 G). Patients of cluster 1 had a higher proportion of high Gleason score (GS > = 8), advanced tumor stage and biochemical recurrence. Chip-square test revealed significant difference of these clinical features (Fig. 2 F). The transcriptomic profile of differently expressed interleukins in two subtypes was portrayed in the heatmap. These further demonstrated that dysregulation of interleukins could contribute to the progression of prostate cancer. The immune landscape of interleukin subtypes To determine the difference in the activation of biological process between two clusters, GSVA analysis was carried out. Cluster 1 was remarkably enriched in pathways related to immune activation including “primary immunodeficiency”, and pathways related to tumor progression including “basal cell carcinoma” (Fig. 3 A). Considering the close relationship between interleukin subtypes and immune activity, we calculated the stromal and immune score in tumor samples using ESTIMATE algorithm based on gene expression profiles. Stromal and immune score reflects the tumor associated stromal and immune cell infiltration level. cluster 1 exhibited significantly higher stromal score (Wilcoxon test, P < 0.05) and immune score (Wilcoxon test, P < 0.05) (Fig. 3 B-D). Furthermore, to better characterize the infiltration of immune cells in two clusters, MCP-counter and ssGSEA were conducted. MCP-counter results show that cluster 1 had significantly higher immune cell infiltration, including T cells, CD8 + T cells, cytotoxic lymphocyte, B lineage, NK cells, Monocytic lineage, Myeloid dendritic cells, neutrophils, endothelial and fibroblasts (Fig. 3 E). Similar results were found in cluster 1 using ssGSEA analysis (Fig. 3 F). Moreover, we conducted an investigation into the disparities in human leukocyte antigen (HLA) and major histocompatibility complex (MHC) expression between the two study groups. Remarkably, we observed a consistently elevated expression of HLA and MHC molecules in cluster 1 (Fig. 3 G-H). Because the two clusters differed significantly regarding immune infiltration, we evaluated the correlation with the five common immune checkpoints. Cluster 1 had higher expression of PD-L1, CTLA4 and HAVCR2 (Supplementary Fig. 2A-C). Cluster 1 was associated with a higher TIDE score (Supplementary Fig. 2D). Construction of the interleukin gene signature We developed an interleukin related risk score to evaluate the prognosis of individual patient. The LASSO cox regression analysis was applied to identify optimal prognostic signatures from interleukin and their corresponding receptors. After taking variables into the LASSO cox regression model with minimized lambda, 5 interleukins or receptors including IL4R, CSF1R, IL2RA, IL11 and IL1F10 was used to build the interleukin related score (Fig. 4 A). The expression of these genes is significantly correlated with poor prognosis. The interleukin related signature was calculated using the following formula: interleukin related risk score (IRRS) = (0.0014*IL4R) + (0.0046*CSF1R) + (0.068*IL2RA) + (0.1353*IL11) + (1.1291*IL1F10) (Supplementary Fig. 3). The correlation of IRRS, interleukin subtypes and recurrence status were exhibited by the Sanky plot (Fig. 4 F). The high IRRS group accounts for higher percentage of the patients with recurrence and cluster1 accounts for higher proportion of patients with high IRRS (Fig. 4 B). Moreover, the validity of IRRS to predict patient prognosis was validated in the external cohort (Fig. 4 C-D). These patients were divided into high and low risk groups based on median risk score. The distribution of the risk score and recurrence status in the datasets was displayed (Fig. 4 E). In addition, the AUC values of prognosis prediction reached 0.636 in the training cohort and 0.716 in the validation cohort. These results indicates that the interleukin risk score could be used to predict the prognosis of prostate cancer patient. Immune landscape was significantly associated with the expression of interleukins As the expression of interleukins correlate with immune filtration. We further compared the infiltration of immune and stromal cells using three different algorithms (Fig. 5 A-C). These algorithms showed similar results. Samples in the high risk group had significantly higher degree of immune cell and stromal cell infiltration. Moreover, for better understanding the correlation between risk score, pearson correlation were calculated between risk score and immune infiltration (Fig. 5 D-E). Gene mutations had been demonstrated to shape the immune environment of tumor cells. We explored the mutation patterns in the high and low risk group (Fig. 5 F). We observed that the expression levels of immune modulators were predominantly higher in the high-risk group compared to the low-risk group, with a majority of them exhibiting stimulatory effects. Furthermore, for the purpose of conducting a more comprehensive examination of the high-risk cohort, we conducted enrichment analysis employing three distinct algorithms. Subsequently, we successfully identified a profound association with immunization (Fig. 6 A-C). Association between interleukin score and mutations To investigate the differences in genomic mutations between the high- and low-risk groups, we downloaded simple nucleotide variation data (Fig. 7 A-B). TTN (17%), TP53 (16%), and SPOP (9%) were the top 3 genes with the highest mutation frequencies in the high-risk group, while SPOP (13%), TTN (10%) and TP53 (9%) were the top 3 genes in the low-risk group (Figs. 6 D-E). By comparing two groups, we found that the high-risk group had higher tumor mutation burden (Fig. 7 C). Subsequent investigations revealed that the high-risk group exhibited a noticeably higher proportion of fraction genome altered, as well as both focal and broad copy number alternations copy number variations, in comparison to the low-risk group (Fig. 7 D-E). The aforementioned studies have demonstrated a higher prevalence of genetic mutations in tumors within the high-risk groups. Interleukin gene signature in the role of anti-PD1 therapy Considering the correlation between interleukin and immune cell infiltration, we analyzed the sensitivity of these individuals to immunotherapy (Fig. 7 A). We found that PD-1 therapy was effective in the high-risk group. Two different approaches were adopted to identify candidate agents with higher drug sensitivity in high PPS score patients. The analyses were performed using CTRP and PRISM-derived drug response data, respectively. First, differential drug response analysis between PPS score-high (top decile) and PPS score-low (bottom decile) groups was conducted to identify compounds with lower estimated AUC values in PPS score-high group (log2FC > 0.10). Next, Spearman correlation analysis between AUC value and PPS score was used to select compounds with negative correlation coefficient (Spearman’s r < − 0.30 for CTRP or − 0.35 for PRISM). These analyses yielded five CTRP-derived compounds (including birinapant, PF − 184, etoposide, clofarabine and luvastatin) and three PRISM-derived compounds (including LY2606368, alvocidib and MK-1775). All these compounds had lower estimated AUC values in PPS score-high group and a negative correlation with PPS (Fig. 7 B-C). Discussion The resistance to immune elimination and the formation of pro-tumoral inflammation circumstance are two of the critical hallmarks of cancer [18] . Interleukins mediate key communications between tumor and immune cells in the tumor microenvironment (TME) [19, 20] . Chronic inflammation has been acknowledged as one of the drivers for tumor formation in prostate cancer [21, 22] . After tumorigenesis, the role of interleukin spans tumor growth, metastasis and therapeutic failure [19] . Delineating the landscape of interleukin in tumor immune regulation and progression facilitated the development novel, individualized and highly effective drugs. In the present study, we portrayed the mutation and expression pattern of interleukin and explored their role in tumor immune infiltration and therapeutic resistance, which, in turn, would promote our understanding of the prostate cancer microenvironment and provide more effective immunotherapeutic strategies for patients with prostate cancer. Previous studies have demonstrated that genetic alternations of interleukin contribute to the progression of tumor. For example, IL8 correlates with prostate cancer progression and castration resistance and IL-8 rs4073 polymorphism indicates a higher risk of prostate cancer [23, 24] .The genetic alternation analysis suggested a high frequency of copy number alternations of interleukin in PRAD cohort. Moreover, patients in the mutation group had a higher tumor mutation burden, mutation count and fraction genome altered. These indicate mutation of interleukins might co-occur with other genes. Importantly, patients with IL gene alternations had a poorer prognosis and a higher frequency of biochemical recurrence, suggesting a critical role for interleukin in the development and progression of prostate cancer. In the present study, based on the expression of interleukins, we identified two distinct subtypes with significant different prognosis and immune landscape. Cluster 1 has worse prognosis than cluster 2. Further analysis of clinical traits showed that cluster 1 correlated with advanced tumor stage and higher gleason score, which in part explained the poor prognosis of this cluster. Differentially expressed genes of these two clusters were subjected to gene enrichment analysis to explore the potential reason for these differences. The results suggested that cluster 1 was prominently enriched in signaling pathways correlate with immune activation like B cell receptor signaling pathway. Therefore, we explored the associations between two interleukin related subtypes tumor immune infiltration. Cluster 1 showed significant higher immune cell infiltration based on three different evaluation algorithm. In fact, immune infiltration analysis shows that nearly all kinds of immune cells are highly infiltrated in cluster 1. Indeed, the relationship between immune cells and tumor cells is heterogeneous and different cells might have opposite roles. Tumor infiltrating cells are pivotal mediators of anti-tumor immune response, however, unlike other solid tumors, prostate cancer is one of the two kinds of tumors in which higher CD8+ T cells correlates with worse prognosis. Consistent with previous studies, cluster 1 had significant higher infiltration of CD8+ T cells. B cell is another important mediator of prostate cancer progression. Compared with benign prostatic tissues, prostate tumor had significant higher abundance of B cell infiltration. Moreover, the recruitment of B cells into tumor microenvironment has been linked with the inception of castration resistant and distant metastasis. Macrophages is also one of the crucial immune cell populations with two distinct phenotypes, M1 and M2. Transition from phenotype M1 to M2could be induced by IL-4, IL-6 or IL-13, and correlated with early biochemical relapse. Myeloid derived suppressor cell is another protagonist in tumor promoting immune environment. Importantly, the intra-tumor infiltration of myeloid derived cells could induce resistance of prostate cancer cells to androgen deprivation therapy by secreting IL-23. Similar with macrophages and T helper cells, neutrophils are characterized by phenotype plasticity and could be polarized into anti-tumor ‘N1’ or tumor promoting N2 subtypes by diverse cytokines like IL-1B. Despite not an immune cell, cancer associated fibroblasts are critical members in the tumor immunosuppressive circuity. As noted, interleukin is closely associated with tumor immunity. In the present study, pro-tumourigenic immune cells including macrophages, Tregs, neutrophils and fibroblasts showed higher abundance in cluster 1 than cluster 2. Accordingly, the major histocompatibility complexes and T cell stimulators are increased in cluster 1. Considering the influence of interleukin and corresponding subtypes on the clinical outcomes, an interleukin related risk score based on 5 prognostic signature was constructed using LASSO Cox regression analysis. The interleukin related score could be calculated for individual patient according to the aforementioned formula. And the high and low IRS subtypes were stratified based on optimal cutoff point. In the TCGA PRAD cohort, high risk significantly correlated with worse prognosis. Moreover, we performed log rank test in external validation cohort, GSE21034 and GSE116918, confirmed the practicability of IRS in evaluating patient prognosis. Univariate and multivariate Cox regression analysis proved that IRS was an independent prognostic factor. Additionally, further analysis indicates that IRS significantly correlated with the infiltration of immune cells. The key gene plays an important role in this process. IL1F10 can upregulates Tregs and also can influence components of the host immune and/or cancer microenvironment, and has been shown to improve the prognosis of colorectal cancer and may be involved in the prognosis of colorectal cancer by regulating CD8 tumor-infiltrating T cells and the expression of PD-L1 [25] . IL11 inhibit of monocytes and macrophage activity [26] . IL2RA and IL4 belong to γ chain (γc) family that act mainly as growth and proliferation factors for progenitors and mature cells and also have roles in lineage-specific cell differentiation [27] . The proliferation, differentiation and survival of mononuclear phagocytes depend on signals from the receptor for macrophage colony-stimulating factor, CSF1R [28] . This is consistent with our findings when analyzing the immune profile of the high- and low-risk groups. High mutational burden could augment the immunogenicity of tumor cells and promote the infiltration of immune cells into the tumors [29] . This is due, in part, to the accumulation of genetic and epigenetic alternations contribute to the immunogenicity of the tumor cells. Tumor mutation burden, termed as the total number of nonsynonymous mutations per sequenced coding area of a tumor genome, has been reported as a biomarker for immunotherapy [30] . Patients with advanced stage of tumor treated with immune check point inhibitors suggested that higher somatic TMB was correlated with better overall survival [31] . Immune checkpoint inhibitors, such as monoclonal antibodies targeting CTLA4, protein PD-1, or its ligand PD-L1, have changed the therapeutic paradigm for many cancers [32] . But the clear role of these therapies for unselected patients with prostate cancer has yet to be clarified [33, 34] . In the present study, we found that high risk group correlates with higher immune cell infiltration and higher mutation frequency. In according with previous studies, we found that patients with high risk score are more sensitive to PD-L1 immunotherapy. This could partly be explained by higher TMB and immune cell infiltration level in the high risk group. However, PD-1/PD-L1 blockade combined with other therapeutic methods is a novel and promising treatment strategy contributes to improving the treatment efficacy of prostate cancer [35] . And interleukin targeted therapy is one of the most promising. The research on targeted therapy utilizing interleukins is currently undergoing a thriving transformation [36-38] . Subsequent studies have yielded a promising list of eight drugs that exhibit potential efficacy against prostate cancer. Among the identified drugs, Etoposide and Clofarabine have demonstrated promising efficacy in preclinical trials for treating prostate cancer [39, 40] . These drugs have exhibited significant effectiveness in inhibiting tumor growth and proliferation in prostate cancer models. In addition, Lovastatin has shown potential as a chemotherapeutic sensitizer for paclitaxel-resistant prostate cancer cells [41] . By inhibiting the enzyme CYP2C8, Lovastatin can enhance the sensitivity of drug-resistant prostate cancer cells to paclitaxel. This suggests that Lovastatin could be used as an adjunct therapy to improve the effectiveness of paclitaxel treatment in patients with drug-resistant prostate cancer. And birinapant and Alvocidib are used for other cancers [42, 43] . This provides a potential basis for our future drug choices. In general, in the present study we comprehensively investigated the relationship between interleukin and immune cell infiltration and demonstrated that interleukin significantly correlated the immune cell aggregation and immunotherapy response. However, several limitations should be acknowledged. First, the infiltration of tumor immune cells was acquired based on algorithms because of technical limitations. Besides, despite immune infiltration and prognosis impact of interleukins were identified in PRAD patients, the intrinsic biological mechanisms behind the phenomenon remained obscure. So functional and mechanistic experiments are needed to verify and decipher the roles of interleukins in PRAD. Our results were also restricted by the lack of clinical cohorts to validate the correlation between interleukin score and tumor immune infiltration and the prognostic value of interleukin score in PRAD. Therefore, further verification based on large cohort prospective clinical study are needed in the future. Nonetheless, our study demonstrated that the IRRS model was a practical prognostic signature for PRAD and was associated with immune landscape and immunotherapy efficacy. Moreover, these results were validated in the external independent PRAD cohort. The IRRS model could be used as a helpful tool for prognosis prediction and therapeutic regimen selection. Our comprehensive evaluation of interleukin expression patterns in PRAD promotes our understanding of the correlation between interleukin and immune cells activation and help selecting proper immunotherapeutic strategies for PRAD patients. Declarations Code Availability The underlying code for this study is not publicly available but may be made available from the corresponding author upon reasonable request. Data availability All data supporting the findings of this study are available within the paper and its supplementary materials. Funding This study is funded by The National Natural Science Foundation (82370776 82170787), Anhui Translational Medicine Foundation (202204295107020007) and Anhui Educational Foundation (2022AH051172). Author Contributions H.Z.W supervised the entire project. J.L.Z, C.H contributed to the data interpretation, data analysis, and writing of the draft. Conflict of interest statement: The authors declare no competing interests. Ethics approval All datasets in the present study were downloaded from public databases. These public databases allowed researchers to download and analyze public datasets for scientific purposes and thus ethics approval was not required. References RAWLA P. Epidemiology of Prostate Cancer [J]. World J Oncol, 2019, 10(2): 63-89. SIEGEL R L, MILLER K D, JEMAL A. Cancer Statistics, 2017 [J]. CA Cancer J Clin, 2017, 67(1): 7-30. LITWIN M S, TAN H J. The Diagnosis and Treatment of Prostate Cancer: A Review [J]. Jama, 2017, 317(24): 2532-42. BELTRAN H, RICKMAN D S, PARK K, et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets [J]. Cancer discovery, 2011, 1(6): 487-95. CHANG A J, AUTIO K A, ROACH M, 3RD, et al. High-risk prostate cancer-classification and therapy [J]. Nature reviews Clinical oncology, 2014, 11(6): 308-23. BRIUKHOVETSKA D, DORR J, ENDRES S, et al. Interleukins in cancer: from biology to therapy [J]. Nature reviews Cancer, 2021, 21(8): 481-99. SUN F, WANG J, SUN Q, et al. Interleukin-8 promotes integrin β3 upregulation and cell invasion through PI3K/Akt pathway in hepatocellular carcinoma [J]. J Exp Clin Cancer Res, 2019, 38(1): 449. CALCINOTTO A, SPATARO C, ZAGATO E, et al. IL-23 secreted by myeloid cells drives castration-resistant prostate cancer [J]. Nature, 2018, 559(7714): 363-9. WEN S, HOU Y, FU L, et al. Cancer-associated fibroblast (CAF)-derived IL32 promotes breast cancer cell invasion and metastasis via integrin β3-p38 MAPK signalling [J]. Cancer Lett, 2019, 442(320-32. XU L, CHEN X, SHEN M, et al. Inhibition of IL-6-JAK/Stat3 signaling in castration-resistant prostate cancer cells enhances the NK cell-mediated cytotoxicity via alteration of PD-L1/NKG2D ligand levels [J]. Mol Oncol, 2018, 12(3): 269-86. ZHANG Q, LIU S, PARAJULI K R, et al. Interleukin-17 promotes prostate cancer via MMP7-induced epithelial-to-mesenchymal transition [J]. Oncogene, 2017, 36(5): 687-99. SAKELLARIOU C, ELHAGE O, PAPAEVANGELOU E, et al. Prostate cancer cells enhance interleukin-15-mediated expansion of NK cells [J]. BJU Int, 2020, 125(1): 89-102. SORRENTINO C, CIUMMO S L, CIPOLLONE G, et al. Interleukin-30/IL27p28 Shapes Prostate Cancer Stem-like Cell Behavior and Is Critical for Tumor Onset and Metastasization [J]. Cancer research, 2018, 78(10): 2654-68. WITT K, EVANS-AXELSSON S, LUNDQVIST A, et al. Inhibition of STAT3 augments antitumor efficacy of anti-CTLA-4 treatment against prostate cancer [J]. Cancer Immunol Immunother, 2021, 70(11): 3155-66. XU Y, SONG G, XIE S, et al. The roles of PD-1/PD-L1 in the prognosis and immunotherapy of prostate cancer [J]. Mol Ther, 2021, 29(6): 1958-69. YANG C, HUANG X, LI Y, et al. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology [J]. Brief Bioinform, 2021, 22(3): GHANDI M, HUANG F W, JANé-VALBUENA J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia [J]. Nature, 2019, 569(7757): 503-8. WILCZYŃSKI J R, NOWAK M. Cancer Immunoediting: Elimination, Equilibrium, and Immune Escape in Solid Tumors [J]. Exp Suppl, 2022, 113( BRIUKHOVETSKA D, DöRR J, ENDRES S, et al. Interleukins in cancer: from biology to therapy [J]. Nat Rev Cancer, 2021, 21(8): 481-99. ANESTAKIS D, PETANIDIS S, KALYVAS S, et al. Mechanisms and applications of interleukins in cancer immunotherapy [J]. Int J Mol Sci, 2015, 16(1): 1691-710. TEWARI A K, STOCKERT J A, YADAV S S, et al. Inflammation and Prostate Cancer [J]. Adv Exp Med Biol, 2018, 1095(41-65. VASTO S, CARRUBA G, CANDORE G, et al. Inflammation and prostate cancer [J]. Future Oncol, 2008, 4(5): 637-45. LOPEZ-BUJANDA Z A, HAFFNER M C, CHAIMOWITZ M G, et al. Castration-mediated IL-8 promotes myeloid infiltration and prostate cancer progression [J]. Nat Cancer, 2021, 2(8): 803-18. CHEN C H, HO C H, HU S W, et al. Association between interleukin-8 rs4073 polymorphism and prostate cancer: A meta-analysis [J]. J Formos Med Assoc, 2020, 119(7): 1201-10. HAGHSHENAS M R, ZAMIR M R, SADEGHI M, et al. Clinical relevance and therapeutic potential of IL-38 in immune and non-immune-related disorders [J]. Eur Cytokine Netw, 2022, 33(3): 54-69. AKDIS M, AAB A, ALTUNBULAKLI C, et al. Interleukins (from IL-1 to IL-38), interferons, transforming growth factor β, and TNF-α: Receptors, functions, and roles in diseases [J]. J Allergy Clin Immunol, 2016, 138(4): YAMANE H, PAUL W E. Cytokines of the γ(c) family control CD4+ T cell differentiation and function [J]. Nat Immunol, 2012, 13(11): 1037-44. ROJO R, RAPER A, OZDEMIR D D, et al. Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations [J]. Nat Commun, 2019, 10(1): 3215. KALAORA S, NAGLER A, WARGO J A, et al. Mechanisms of immune activation and regulation: lessons from melanoma [J]. Nature reviews Cancer, 2022, 22(4): 195-207. CHAN T A, YARCHOAN M, JAFFEE E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic [J]. Ann Oncol, 2019, 30(1): 44-56. RICCIUTI B, WANG X, ALESSI J V, et al. Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels [J]. JAMA Oncol, 2022, 8(8): 1160-8. REBELLO R J, OING C, KNUDSEN K E, et al. Prostate cancer [J]. Nat Rev Dis Primers, 2021, 7(1): 9. HANSEN A R, MASSARD C, OTT P A, et al. Pembrolizumab for advanced prostate adenocarcinoma: findings of the KEYNOTE-028 study [J]. Ann Oncol, 2018, 29(8): 1807-13. PHILIPPOU Y, PROTHEROE A S, BRYANT R J. Re: Pembrolizumab for Treatment-refractory Metastatic Castration-resistant Prostate Cancer: Multicohort, Open-label Phase II KEYNOTE-199 Study [J]. Eur Urol, 2020, 77(6): 759-60. XU Y, SONG G, XIE S, et al. The roles of PD-1/PD-L1 in the prognosis and immunotherapy of prostate cancer [J]. Mol Ther, 2021, 29(6): 1958-69. GUO Y, XU F, LU T, et al. Interleukin-6 signaling pathway in targeted therapy for cancer [J]. Cancer Treat Rev, 2012, 38(7): 904-10. JASPERS J E, KHAN J F, GODFREY W D, et al. IL-18-secreting CAR T cells targeting DLL3 are highly effective in small cell lung cancer models [J]. J Clin Invest, 2023, 133(9): BHATIA V, KAMAT N V, PARIVA T E, et al. Targeting advanced prostate cancer with STEAP1 chimeric antigen receptor T cell and tumor-localized IL-12 immunotherapy [J]. Nat Commun, 2023, 14(1): 2041. KAMRADT J M, PIENTA K J. Etoposide in prostate cancer [J]. Expert Opin Pharmacother, 2000, 1(2): 271-5. Clofarabine [J]. Drugs R D, 2004, 5(4): 213-7. LI Y, CHEN S, ZHU J, et al. Lovastatin enhances chemosensitivity of paclitaxel-resistant prostate cancer cells through inhibition of CYP2C8 [J]. Biochem Biophys Res Commun, 2022, 589(85-91. GU S S, ZHANG W, WANG X, et al. Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade [J]. Cancer Discov, 2021, 11(6): 1524-41. WIERNIK P H. Alvocidib (flavopiridol) for the treatment of chronic lymphocytic leukemia [J]. Expert Opin Investig Drugs, 2016, 25(6): 729-34. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.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-4117470","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283357176,"identity":"30fc4118-8f9c-4052-bfe2-ad7a9b3f467e","order_by":0,"name":"Jialong Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jialong","middleName":"","lastName":"Zhang","suffix":""},{"id":283357177,"identity":"02738bb6-76ef-40a8-a478-791447104404","order_by":1,"name":"Cong Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Huang","suffix":""},{"id":283357179,"identity":"89c73f3f-f756-46d1-9c83-4892548d2965","order_by":2,"name":"Hongzhi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYJACZjApwdhw4MMPCTk29vYDRGtpfDizx8aYj+dMArFaGJiNedjSEudJOBjgVW5w/Ozh1wUVd+zmz25uk5zBczi9TYIhgeFHxTbcWs7kpVnPOPMsuXHOwTaJDxaHc9ukGw8w9py5jVOL2YEcM2PetsPJzBKJYFty22QOJDAztuHRcv4NRAsbUIs0D9vhdDaJBAP8Wm7kGD8GarHjkUhsBnk/gaAW+xtvzJh5zhxOkJBIBAeyYRswkA/i84tkf47xZ56Kw/byM9IfgKJSXr69/eCDHxW4tQABmwSQSGxAFjqATz0QMH8AOZCAolEwCkbBKBjJAACjkV1WSb14kQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University, Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-17 14:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117470/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53660864,"identity":"ee66848a-0618-4c68-8281-cc73ae2b3469","added_by":"auto","created_at":"2024-03-28 16:09:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":409962,"visible":true,"origin":"","legend":"\u003cp\u003eThe alternations of interleukins in PRAD. (A) Landscape of genomic alternations of the interleukins in PRAD. Each row represents a gene, and each column represents a patient. The frequency of alternations in top 20 interleukins. (B) Gene alternation frequency of PRAD patients in TCGA. (C) Histogram of the proportion of different mutation modes in PRAD. (D-F) The difference of TMB, fraction genome altered frequency and mutation count between altered and unaltered group. (G) The overall survival of PRAD patient between altered and unaltered group. (I) Histogram of the frequency of biochemical recurrence in altered and unaltered group. (J) Histogram of the frequency of primary lymph node presentation in altered and unaltered group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/9baff43600d06ba9ff57fa74.png"},{"id":53660863,"identity":"2afda20e-a5b1-433c-b2f6-51ef83e5f4fa","added_by":"auto","created_at":"2024-03-28 16:09:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497829,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering of interleukins in PRAD. (A) Consensus matrices of patients in the TCGA cohort for k=1 using 100 iterations of unsupervised consensus clustering method (K-means) to ensure the clustering stability. (B) t-SNE analysis of interleukin related subtypes in TCGA cohort. (C) Comparison of prognosis of patients in different PRAD subtypes in TCGA cohort. (D) t-SNE analysis of interleukin related subtypes in GSE21034. (E) Comparison of prognosis of patients in different PRAD subtypes in GSE21034. (F) Heatmap of interleukin in TCGA cohorts. Interleukin related cluster, recurrence status, N stage, T stage, M stage, Gleason score and age were used as patient annotations. (G) Sankey plot summarized the relationship among the cluster, gleason score, T stage and recurrence status.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/b01d2ebfa06e56de5511668a.png"},{"id":53660871,"identity":"7ada7090-3c4d-409b-8066-00e43fdcc44d","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":378569,"visible":true,"origin":"","legend":"\u003cp\u003eBiological characteristics and immune landscape of interleukin subtypes in the TCGA cohort. (A) Heatmap of the activation status of biological process in different subtypes evaluated by GSVA. Red and blue represents the activation and inhibition of biological process respectively. (B-D) The difference of stromal score, immune score and estimate score in two interleukin subtypes. (E) The difference of immune cell infiltration between two interleukin subtypes evaluated by MCP-counter. (F) The immune cell infiltration landscape between two interleukin subtypes calculated using ssGASEA. (G-H) Gene expression of HLA and MHC gene sets between two interleukin subtypes. Statistical significance at the level of ns \u0026gt;= 0.05, *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001 and **** \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/4c37a0c5fb0cf8ebd53248d4.png"},{"id":53660867,"identity":"3ae98111-ef18-4456-bd38-313ccd62d5e0","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269224,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of interleukin related score. (A) The distribution of risk score, recurrence status and hub gene expression level. (B-D) Kaplan-Meier curves for patients with high or low interleukin score in the TCGA, GSE21034 and GSE1168918. (E) Multivariate cox regression analysis of interleukin core and other clinical characteristics. (F) Sankey plot summarized the relationship among the risk core, cluster, gleason score, T stage and recurrence status.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/a26e287c0f349514b9b0947e.png"},{"id":53660870,"identity":"30c66f17-659e-455c-ac61-2ddf339333c8","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477450,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between risk score and immune cell infiltration. (A) The correlation between risk core and immune score in TCGA cohort. (B) The correlation between risk score and stromal score in TCGA cohort. (C) The correlation between risk score and estimate score in TCGA cohort. (D) The difference of immune cell infiltration level in high and low risk group identified by MCP-counter analysis. (E) The immune cell landscape in high and low risk group evaluated by ssGSEA. (F) Regulation of immunomodulators in high and low risk group. From left to right: mRNA expression (median normalized expression levels); expression versus methylation (gene expression correlation with DNA-methylation beta value); amplification frequency (the difference between the fraction of samples in which an immunomodulator is amplified in a particular subtype and the amplification fraction in all samples); and the deletion frequency (as amplifications) 75 IM by interleukin score.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/2e4bcfffbd3952d9dfb84aa1.png"},{"id":53660866,"identity":"84d66b48-5d49-4ffd-bcca-ce129e3723b7","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":379597,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of highly expressed genes in the high interleukin score group. (A: Cellular Component; B: Biological process; C: Molecular Function)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/522f8b9118e9ae88b9d4c2b8.png"},{"id":53660865,"identity":"0ca5c19f-6a29-4d8d-b868-a2da822e4cf9","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":302241,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between interleukin score and mutations. (A-B) Oncoprint of mutation status of top 20 genes in high(A) and low(B) risk group. (C-D) The tumor mutation burden (C) and fraction genome altered (D) in high and low risk group. (E) Focal and broad copy number alternations among the high and low risk group. Statistical significance at the level of ns \u0026gt;= 0.05, *\u0026lt;0.05, **\u0026lt;0.01, ***\u0026lt;0.001 and **** \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/02c078a657f2cb3d807a2038.png"},{"id":53660869,"identity":"a9e4c750-2ae6-4b10-8c72-c3f24d1e5f3b","added_by":"auto","created_at":"2024-03-28 16:09:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":61070,"visible":true,"origin":"","legend":"\u003cp\u003eImmunotherapy response prediction and identification of candidate agents with higher drug sensitivity in high interleukin score patients. (A) The response of high and low interleukin score patients to PD-1 and CTLA-4 immunotherapy. (B) The results of Spearman correlation analysis and differential drug response analysis of five CTRP derived compounds. (C) The results of Spearman correlation analysis and differential drug response analysis of three PRISM derived compounds.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/316e230c0370fbe1652d2b8c.png"},{"id":53696658,"identity":"61136aff-e65d-4582-b265-bc5926ae7768","added_by":"auto","created_at":"2024-03-29 03:53:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3295265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/34565e4c-5b84-4592-aade-88d9d798758f.pdf"},{"id":53662196,"identity":"9ec3e9b4-36ec-4bcc-87c5-b80743098f00","added_by":"auto","created_at":"2024-03-28 16:17:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":844144,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4117470/v1/31094e45b5d5984a82deb992.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ectopic expression of chemokines and chemokine receptors in relation to immune cell infiltration, prostate cancer recurrence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is the most common malignancy in men worldwide and metastasis is the leading cause of cancer death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although patients with localized tumors have encouraging prognosis, the five-year survival rate for metastatic patients descends to 30%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Standard treatment advanced prostate cancer relies on androgen-deprivation therapies, which could restrict tumor growth in the early stage but eventually lead to the recurrence and generation of castration-resistant prostate cancer \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Moreover, treatment failure is usually connected with formation of even more invasive subtypes, like neuroendocrine prostate cancer\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Hence, figuring out the driver genes that promote the conversion from localized to metastatic prostate cancer would contribute to develop novel agents for prostate cancer patients.\u003c/p\u003e \u003cp\u003eThe interleukin and interleukin receptors play essential roles in tumor progression and cancer dissemination\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. After interacting with their corresponding receptors, interleukins activate various oncogenic signaling pathways such as STAT3/RORγ, PI3K and MAPK signaling\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Many interleukins and interleukin receptors like IL-6, IL-23 and IL-17 have been proved to play important roles in prostate tumor growth and androgen-deprivation therapy resistance\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Interleukins promote tumor progression by either directly acting on tumor cells or employing regulatory effects on immune cells or stromal cells in tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Immunotherapies like PD-1 or CTLA-4 inhibition have shown promising effects in many solid tumors\u0026rsquo; treatment, however, their efficacy in prostate cancer seems to be limited\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Considering the critical roles of chemokines in mediating immune cell trafficking and activation, they could be novel targets for immunotherapy agent development.\u003c/p\u003e \u003cp\u003eIn the present study, we systematically explored mutation and transcriptional profiles and clinical significance of interleukin and interleukin receptors in prostate cancer patients. Tumor subtypes with different prognosis and immune cell infiltration status was identified based on interleukin and interleukin receptors expression. Subsequently, an interleukin related risk model was constructed to predict tumor recurrence. We further investigated the relationship between this risk model and tumor mutation burden, immune cell infiltration and target therapy response. Our study provided a better understanding of interleukin and interleukin receptors in PRAD, which would help to select proper immunotherapies.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient and Samples\u003c/h2\u003e \u003cp\u003emRNA expression data (raw counts) of 495 and 388 PRAD patients with matched clinical annotations including tumor stage, gender and recurrence free time were downloaded from the Cancer Genomes Atlas (TCGA) and Gene expression Omnibus (GEO) database respectively. Gene somatic mutation data (MAF files) of PRAD was obtained from TCGA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of PRAD subtypes\u003c/h2\u003e \u003cp\u003eChemokines and chemokine receptors were summarized from previous study and a total of 67 genes was obtained\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Unsupervised clustering algorithm was employed for cluster analysis of these genes. R package Consensus-Clusterplus package was used for the analysis and the repetition was set to 100 to ensure the credibility of the classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of immune cell and stromal cell infiltration\u003c/h2\u003e \u003cp\u003eESTIMATE algorithm, which could portrait the general infiltration level of stromal and immune cell, was used to calculate immune score and stromal score. Single sample GSEA was used to assess the infiltration of 28 immune cells. Moreover, microenvironment cell population counter (MCP-counter) was also used to evaluate the absolute abundance of immune and stromal cells in the tumor microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePathways and enrichment analysis\u003c/h2\u003e \u003cp\u003eTo explore potential pathways and biological processes correlated with chemokine signature, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses was conducted using R package \u0026ldquo;clusterProfiler\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumor mutational burden (TMB) analysis\u003c/h2\u003e \u003cp\u003eTumor mutational burden was defined as mutations per million bases. Tumor mutation data was obtained from TCGA database and subsequent analysis was conducted by R package \u0026ldquo;maftools\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDrug and immunotherapy sensitivity prediction\u003c/h2\u003e \u003cp\u003eDrug sensitivity prediction was conducted as previously described\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Gene expression profile and somatic data of human cancer cell lines (CCLs) were downloaded from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ccle/\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ccle/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Drug sensitivity data of CLLs were obtained from the Cancer Therapeutics Response Portal (CTRP v.2.0, released October 2015, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ctrp\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ctrp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and PRISM Repurposing dataset (19Q4, released December 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://depmap.org/portal/prism/\u003c/span\u003e\u003cspan address=\"https://depmap.org/portal/prism/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The CTRP contains the drug sensitivity data of 481 compounds in 835 CCLs and the PRISM contains the sensitivity data of 1448 compounds in 482 CLLs. Both datasets use the area under the dose-response curve (AUC) values as a calibration of drug sensitivity and lower AUC values suggest increased sensitivity to treatment. K-nearest neighbor (k-NN) imputation was used to impute the missing AUC values. The sensitivity to anti-PD-1 and anti-CTLA-4 therapy was predicted by comparing the gene expression of tumor subtypes with 47 melanoma patients treated with immunotherapy using subclass mapping (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genepattern.org/\u003c/span\u003e\u003cspan address=\"https://www.genepattern.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of chemokine related risk score\u003c/h2\u003e \u003cp\u003eUnivariate cox analysis was used to identify genes correlated with recurrence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Thereafter, recurrence related genes was analyzed with the LASSO multivariate Cox regression algorithm using R package \u0026ldquo;glmnet\u0026rdquo;. Then, the key genes and coefficients in the risk score signature were identified according to the most suitable penalty parameter λ. The risk score formula was Risk score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{n}{Coef}_{i}*{Exp}_{i}\\)\u003c/span\u003e\u003c/span\u003e, where Coefi is the coefficient and Expi is the normalized expression of each gene. The risk score was constructed based on the training set and validated in the external cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis was conducted with R software (\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). Unpaired student t test was used to compare two groups with normally distributed variables. One way analysis and Kruskal-Wallis test of variance were used as parametric and non-parametric methods for three groups comparing. The surv-cutpoint function of survminer package was used to determine the optimal cutoff point in each dataset. Detailly, all potential cutoff points were tested to identify the maximum rank statistic and subsequently patients were divided into risk score high and risk score low group according to optimal cutoff point. Survival curves were plot with Kaplan-Meier method and p value was calculated with log rank test. P value less than 0.05 was considered as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSomatic alternation landscape of interleukins\u003c/h2\u003e \u003cp\u003eTo study the genomic features of interleukins in prostate cancer, we portrayed the mutation and CAN frequency of 492 PRAD patients in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). The DNA mutation of interleukins occurred in about 46% of patients and overall mutation level ranged from 0 to 9%. The interleukins with the highest mutation frequency are IL17C (9%), IL7(7%), IL-34(6%), IL-21(4%) and IL-2(3%) and their most common mutations are deep deletion, amplification, missense mutation and truncating mutation. Importantly, log-rank test indicates that patients with interleukins altered had worse overall survival (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Moreover, patients in the altered group had significant higher biochemical recurrence (15.04% vs 8.65%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0279) and primary lymph node positive rates (88.05% vs 80.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0358) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI J). Besides, patients in the mutated group had higher tumor mutation burden (TMB), mutation count and fraction genome altered. These indicates that mutation and copy number variation of interleukins might co-occurrence with other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC D E and F). In summary, these results suggest that mutation and copy number variation of interleukins means worse overall survival and higher biochemical recurrence rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of interleukin subtypes in PRAD\u003c/h2\u003e \u003cp\u003eMany interleukins had similar effects in promote tumor progression and anti-tumor immunity. To investigate whether interleukin co-expressed with each other, we calculated the correlation of these genes. Results showed that the expression of most interleukins significantly correlated with each other (Supplementary Fig.\u0026nbsp;1). This indicates that interleukins might cooperate with each other to regulate the progression of prostate cancer. Subsequently, based on the expression levels of interleukins from TCGA, two distinct expression patterns were identified using the unsupervised clustering method, including 148 cases in the interleukin cluster1 and 347 cases in interleukin cluster2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). T-SNE analysis showed that patients could roughly divided into two groups, which further corroborated two distinguished subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, we found patients in cluster 1 had worse recurrence outcomes in the cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Similar results were found in external independent cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). The relationship between clinical factors in TCGA cohort and interleukin subtypes were also explored (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Patients of cluster 1 had a higher proportion of high Gleason score (GS\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;8), advanced tumor stage and biochemical recurrence. Chip-square test revealed significant difference of these clinical features (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The transcriptomic profile of differently expressed interleukins in two subtypes was portrayed in the heatmap. These further demonstrated that dysregulation of interleukins could contribute to the progression of prostate cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe immune landscape of interleukin subtypes\u003c/h2\u003e \u003cp\u003eTo determine the difference in the activation of biological process between two clusters, GSVA analysis was carried out. Cluster 1 was remarkably enriched in pathways related to immune activation including \u0026ldquo;primary immunodeficiency\u0026rdquo;, and pathways related to tumor progression including \u0026ldquo;basal cell carcinoma\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Considering the close relationship between interleukin subtypes and immune activity, we calculated the stromal and immune score in tumor samples using ESTIMATE algorithm based on gene expression profiles. Stromal and immune score reflects the tumor associated stromal and immune cell infiltration level. cluster 1 exhibited significantly higher stromal score (Wilcoxon test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and immune score (Wilcoxon test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D). Furthermore, to better characterize the infiltration of immune cells in two clusters, MCP-counter and ssGSEA were conducted. MCP-counter results show that cluster 1 had significantly higher immune cell infiltration, including T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic lymphocyte, B lineage, NK cells, Monocytic lineage, Myeloid dendritic cells, neutrophils, endothelial and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Similar results were found in cluster 1 using ssGSEA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Moreover, we conducted an investigation into the disparities in human leukocyte antigen (HLA) and major histocompatibility complex (MHC) expression between the two study groups. Remarkably, we observed a consistently elevated expression of HLA and MHC molecules in cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-H). Because the two clusters differed significantly regarding immune infiltration, we evaluated the correlation with the five common immune checkpoints. Cluster 1 had higher expression of PD-L1, CTLA4 and HAVCR2 (Supplementary Fig.\u0026nbsp;2A-C). Cluster 1 was associated with a higher TIDE score (Supplementary Fig.\u0026nbsp;2D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the interleukin gene signature\u003c/h2\u003e \u003cp\u003eWe developed an interleukin related risk score to evaluate the prognosis of individual patient. The LASSO cox regression analysis was applied to identify optimal prognostic signatures from interleukin and their corresponding receptors. After taking variables into the LASSO cox regression model with minimized lambda, 5 interleukins or receptors including IL4R, CSF1R, IL2RA, IL11 and IL1F10 was used to build the interleukin related score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The expression of these genes is significantly correlated with poor prognosis. The interleukin related signature was calculated using the following formula: interleukin related risk score (IRRS) = (0.0014*IL4R) + (0.0046*CSF1R) + (0.068*IL2RA) + (0.1353*IL11) + (1.1291*IL1F10) (Supplementary Fig.\u0026nbsp;3). The correlation of IRRS, interleukin subtypes and recurrence status were exhibited by the Sanky plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The high IRRS group accounts for higher percentage of the patients with recurrence and cluster1 accounts for higher proportion of patients with high IRRS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, the validity of IRRS to predict patient prognosis was validated in the external cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). These patients were divided into high and low risk groups based on median risk score. The distribution of the risk score and recurrence status in the datasets was displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In addition, the AUC values of prognosis prediction reached 0.636 in the training cohort and 0.716 in the validation cohort. These results indicates that the interleukin risk score could be used to predict the prognosis of prostate cancer patient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmune landscape was significantly associated with the expression of interleukins\u003c/h2\u003e \u003cp\u003eAs the expression of interleukins correlate with immune filtration. We further compared the infiltration of immune and stromal cells using three different algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). These algorithms showed similar results. Samples in the high risk group had significantly higher degree of immune cell and stromal cell infiltration. Moreover, for better understanding the correlation between risk score, pearson correlation were calculated between risk score and immune infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). Gene mutations had been demonstrated to shape the immune environment of tumor cells. We explored the mutation patterns in the high and low risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). We observed that the expression levels of immune modulators were predominantly higher in the high-risk group compared to the low-risk group, with a majority of them exhibiting stimulatory effects. Furthermore, for the purpose of conducting a more comprehensive examination of the high-risk cohort, we conducted enrichment analysis employing three distinct algorithms. Subsequently, we successfully identified a profound association with immunization (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between interleukin score and mutations\u003c/h2\u003e \u003cp\u003eTo investigate the differences in genomic mutations between the high- and low-risk groups, we downloaded simple nucleotide variation data (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). TTN (17%), TP53 (16%), and SPOP (9%) were the top 3 genes with the highest mutation frequencies in the high-risk group, while SPOP (13%), TTN (10%) and TP53 (9%) were the top 3 genes in the low-risk group (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). By comparing two groups, we found that the high-risk group had higher tumor mutation burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Subsequent investigations revealed that the high-risk group exhibited a noticeably higher proportion of fraction genome altered, as well as both focal and broad copy number alternations copy number variations, in comparison to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E). The aforementioned studies have demonstrated a higher prevalence of genetic mutations in tumors within the high-risk groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInterleukin gene signature in the role of anti-PD1 therapy\u003c/h2\u003e \u003cp\u003eConsidering the correlation between interleukin and immune cell infiltration, we analyzed the sensitivity of these individuals to immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We found that PD-1 therapy was effective in the high-risk group. Two different approaches were adopted to identify candidate agents with higher drug sensitivity in high PPS score patients. The analyses were performed using CTRP and PRISM-derived drug response data, respectively. First, differential drug response analysis between PPS score-high (top decile) and PPS score-low (bottom decile) groups was conducted to identify compounds with lower estimated AUC values in PPS score-high group (log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0.10). Next, Spearman correlation analysis between AUC value and PPS score was used to select compounds with negative correlation coefficient (Spearman\u0026rsquo;s r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.30 for CTRP or \u0026minus;\u0026thinsp;0.35 for PRISM). These analyses yielded five CTRP-derived compounds (including birinapant, PF\u0026thinsp;\u0026minus;\u0026thinsp;184, etoposide, clofarabine and luvastatin) and three PRISM-derived compounds (including LY2606368, alvocidib and MK-1775). All these compounds had lower estimated AUC values in PPS score-high group and a negative correlation with PPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe resistance to immune elimination and the formation of pro-tumoral inflammation circumstance are two of the critical hallmarks of cancer\u003csup\u003e[18]\u003c/sup\u003e. Interleukins mediate key communications between tumor and immune cells in the tumor microenvironment (TME)\u003csup\u003e[19, 20]\u003c/sup\u003e. Chronic inflammation has been acknowledged as one of the drivers for tumor formation in prostate cancer\u003csup\u003e[21, 22]\u003c/sup\u003e. After tumorigenesis, the role of interleukin spans tumor growth, metastasis and therapeutic failure\u003csup\u003e[19]\u003c/sup\u003e. Delineating the landscape of interleukin in tumor immune regulation and progression facilitated the development novel, individualized and highly effective drugs. In the present study, we portrayed the mutation and expression pattern of interleukin and explored their role in tumor immune infiltration and therapeutic resistance, which, in turn, would promote our understanding of the prostate cancer microenvironment and provide more effective immunotherapeutic strategies for patients with prostate cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have demonstrated that genetic alternations of interleukin contribute to the progression of tumor. For example, IL8 correlates with prostate cancer progression and castration resistance and IL-8 rs4073 polymorphism indicates a higher risk of prostate cancer\u003csup\u003e[23, 24]\u003c/sup\u003e.The genetic alternation analysis suggested a high frequency of copy number alternations of interleukin in PRAD cohort. Moreover, patients in the mutation group had a higher tumor mutation burden, mutation count and fraction genome altered. These indicate mutation of interleukins might co-occur with other genes. Importantly, patients with IL gene alternations had a poorer prognosis and a higher frequency of biochemical recurrence, suggesting a critical role for interleukin in the development and progression of prostate cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, based on the expression of interleukins, we identified two distinct subtypes with significant different prognosis and immune landscape. Cluster 1 has worse prognosis than cluster 2. Further analysis of clinical traits showed that cluster 1 correlated with advanced tumor stage and higher gleason score, which in part explained the poor prognosis of this cluster. Differentially expressed genes of these two clusters were subjected to gene enrichment analysis to explore the potential reason for these differences. The results suggested that cluster 1 was prominently enriched in signaling pathways correlate with immune activation like B cell receptor signaling pathway. Therefore, we explored the associations between two interleukin related subtypes tumor immune infiltration. Cluster 1 showed significant higher immune cell infiltration based on three different evaluation algorithm. In fact, immune infiltration analysis shows that nearly all kinds of immune cells are highly infiltrated in cluster 1. Indeed, the relationship between immune cells and tumor cells is heterogeneous and different cells might have opposite roles. Tumor infiltrating cells are pivotal mediators of anti-tumor immune response, however, unlike other solid tumors, prostate cancer is one of the two kinds of tumors in which higher CD8+ T cells correlates with worse prognosis. Consistent with previous studies, cluster 1 had significant higher infiltration of CD8+ T cells. B cell is another important mediator of prostate cancer progression. Compared with benign prostatic tissues, prostate tumor had significant higher abundance of B cell infiltration. Moreover, the recruitment of B cells into tumor microenvironment has been linked with the inception of castration resistant and distant metastasis. Macrophages is also one of the crucial immune cell populations with two distinct phenotypes, M1 and M2. Transition from phenotype M1 to M2could be induced by IL-4, IL-6 or IL-13, and correlated with early biochemical relapse. Myeloid derived suppressor cell is another protagonist in tumor promoting immune environment. Importantly, the intra-tumor infiltration of myeloid derived cells could induce resistance of prostate cancer cells to androgen deprivation therapy by secreting IL-23. Similar with macrophages and T helper cells, neutrophils are characterized by phenotype plasticity and could be polarized into anti-tumor ‘N1’ or tumor promoting N2 subtypes by diverse cytokines like IL-1B. Despite not an immune cell, cancer associated fibroblasts are critical members in the tumor immunosuppressive circuity. As noted, interleukin is closely associated with tumor immunity. In the present study, pro-tumourigenic immune cells including macrophages, Tregs, neutrophils and fibroblasts showed higher abundance in cluster 1 than cluster 2. Accordingly, the major histocompatibility complexes and T cell stimulators are increased in cluster 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering the influence of interleukin and corresponding subtypes on the clinical outcomes, an interleukin related risk score based on 5 prognostic signature was constructed using LASSO Cox regression analysis. The interleukin related score could be calculated for individual patient according to the aforementioned formula. And the high and low IRS subtypes were stratified based on optimal cutoff point. In the TCGA PRAD cohort, high risk significantly correlated with worse prognosis. Moreover, we performed log rank test in external validation cohort, GSE21034 and GSE116918, confirmed the practicability of IRS in evaluating patient prognosis. Univariate and multivariate Cox regression analysis proved that IRS was an independent prognostic factor. Additionally, further analysis indicates that IRS significantly correlated with the infiltration of immune cells. The key gene plays an important role in this process. IL1F10 can upregulates Tregs and also can influence components of the host immune and/or cancer microenvironment, and has been shown to improve the prognosis of colorectal cancer and may be involved in the prognosis of colorectal cancer by regulating CD8 tumor-infiltrating T cells and the expression of PD-L1\u003csup\u003e[25]\u003c/sup\u003e. IL11 inhibit of monocytes and macrophage activity\u003csup\u003e[26]\u003c/sup\u003e. \u0026nbsp;IL2RA and IL4 belong to γ chain (γc) family that act mainly as growth and proliferation factors for progenitors and mature cells and also have roles in lineage-specific cell differentiation\u003csup\u003e[27]\u003c/sup\u003e.\u0026nbsp;The proliferation, differentiation and survival of mononuclear phagocytes depend on signals from the receptor for macrophage colony-stimulating factor, CSF1R\u003csup\u003e[28]\u003c/sup\u003e. This is consistent with our findings when analyzing the immune profile of the high- and low-risk groups.\u003c/p\u003e\n\u003cp\u003eHigh mutational burden could augment the immunogenicity of tumor cells and promote the infiltration of immune cells into the tumors\u0026nbsp;\u003csup\u003e[29]\u003c/sup\u003e. This is due, in part, to the accumulation of genetic and epigenetic alternations contribute to the immunogenicity of the tumor cells. Tumor mutation burden, termed as the total number of nonsynonymous mutations per sequenced coding area of a tumor genome, has been reported as a biomarker for immunotherapy\u003csup\u003e[30]\u003c/sup\u003e. Patients with advanced stage of tumor treated with immune check point inhibitors suggested that higher somatic TMB was correlated with better overall survival\u0026nbsp;\u003csup\u003e[31]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmune checkpoint inhibitors, such as monoclonal antibodies targeting CTLA4, protein PD-1, or its ligand PD-L1, have changed the therapeutic paradigm for many\u003c/p\u003e\n\u003cp\u003ecancers\u003csup\u003e[32]\u003c/sup\u003e. But the clear role of these therapies for unselected patients with prostate cancer has yet to be clarified\u003csup\u003e[33, 34]\u003c/sup\u003e. In the present study, we found that high risk group correlates with higher immune cell infiltration and higher mutation frequency. In according with previous studies, we found that patients with high risk score are more sensitive to PD-L1 immunotherapy. This could partly be explained by higher TMB and immune cell infiltration level in the high risk group. However, PD-1/PD-L1 blockade combined with other therapeutic methods is a novel and promising treatment strategy contributes to improving the treatment efficacy of prostate cancer\u003csup\u003e[35]\u003c/sup\u003e. And interleukin targeted therapy is one of the most promising. The research on targeted therapy utilizing interleukins is currently undergoing a thriving transformation\u003csup\u003e[36-38]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequent studies have yielded a promising list of eight drugs that exhibit potential efficacy against prostate cancer. Among the identified drugs, Etoposide and Clofarabine have demonstrated promising efficacy in preclinical trials for treating prostate cancer\u003csup\u003e[39, 40]\u003c/sup\u003e. These drugs have exhibited significant effectiveness in inhibiting tumor growth and proliferation in prostate cancer models. In addition, Lovastatin has shown potential as a chemotherapeutic sensitizer for paclitaxel-resistant prostate cancer cells\u003csup\u003e[41]\u003c/sup\u003e. By inhibiting the enzyme CYP2C8, Lovastatin can enhance the sensitivity of drug-resistant prostate cancer cells to paclitaxel. This suggests that Lovastatin could be used as an adjunct therapy to improve the effectiveness of paclitaxel treatment in patients with drug-resistant prostate cancer. And birinapant and Alvocidib are used for other cancers\u003csup\u003e[42, 43]\u003c/sup\u003e.\u0026nbsp;This provides a potential basis for our future drug choices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn general, in the present study we comprehensively investigated the relationship between interleukin and immune cell infiltration and demonstrated that interleukin significantly correlated the immune cell aggregation and immunotherapy response. However, several limitations should be acknowledged. First, the infiltration of tumor immune cells was acquired based on algorithms because of technical limitations. Besides, despite immune infiltration and prognosis impact of interleukins were identified in PRAD patients, the intrinsic biological mechanisms behind the phenomenon remained obscure. So functional and mechanistic experiments are needed to verify and decipher the roles of interleukins in PRAD. Our results were also restricted by the lack of clinical cohorts to validate the correlation between interleukin score and tumor immune infiltration and the prognostic value of interleukin score in PRAD. Therefore, further verification based on large cohort prospective clinical study are needed in the future.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNonetheless, our study demonstrated that the IRRS model was a practical prognostic signature for PRAD and was associated with immune landscape and immunotherapy efficacy. Moreover, these results were validated in the external independent PRAD cohort. The IRRS model could be used as a helpful tool for prognosis prediction and therapeutic regimen selection. Our comprehensive evaluation of interleukin expression patterns in PRAD promotes our understanding of the correlation between interleukin and immune cells activation and help selecting proper immunotherapeutic strategies for PRAD patients.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is funded by The National Natural Science Foundation (82370776 82170787), Anhui Translational Medicine Foundation (202204295107020007) and Anhui Educational Foundation (2022AH051172).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Z.W supervised the entire project. J.L.Z, C.H contributed to the data interpretation, data analysis, and writing of the draft.\u0026nbsp;\u003cem\u003eConflict of interest statement:\u003c/em\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets in the present study were downloaded from public databases. These public databases allowed researchers to download and analyze public datasets for scientific purposes and thus ethics approval was not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRAWLA P. Epidemiology of Prostate Cancer [J]. World J Oncol, 2019, 10(2): 63-89.\u003c/li\u003e\n \u003cli\u003eSIEGEL R L, MILLER K D, JEMAL A. Cancer Statistics, 2017 [J]. CA Cancer J Clin, 2017, 67(1): 7-30.\u003c/li\u003e\n \u003cli\u003eLITWIN M S, TAN H J. The Diagnosis and Treatment of Prostate Cancer: A Review [J]. Jama, 2017, 317(24): 2532-42.\u003c/li\u003e\n \u003cli\u003eBELTRAN H, RICKMAN D S, PARK K, et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets [J]. Cancer discovery, 2011, 1(6): 487-95.\u003c/li\u003e\n \u003cli\u003eCHANG A J, AUTIO K A, ROACH M, 3RD, et al. High-risk prostate cancer-classification and therapy [J]. Nature reviews Clinical oncology, 2014, 11(6): 308-23.\u003c/li\u003e\n \u003cli\u003eBRIUKHOVETSKA D, DORR J, ENDRES S, et al. Interleukins in cancer: from biology to therapy [J]. Nature reviews Cancer, 2021, 21(8): 481-99.\u003c/li\u003e\n \u003cli\u003eSUN F, WANG J, SUN Q, et al. Interleukin-8 promotes integrin \u0026beta;3 upregulation and cell invasion through PI3K/Akt pathway in hepatocellular carcinoma [J]. J Exp Clin Cancer Res, 2019, 38(1): 449.\u003c/li\u003e\n \u003cli\u003eCALCINOTTO A, SPATARO C, ZAGATO E, et al. IL-23 secreted by myeloid cells drives castration-resistant prostate cancer [J]. Nature, 2018, 559(7714): 363-9.\u003c/li\u003e\n \u003cli\u003eWEN S, HOU Y, FU L, et al. Cancer-associated fibroblast (CAF)-derived IL32 promotes breast cancer cell invasion and metastasis via integrin \u0026beta;3-p38 MAPK signalling [J]. Cancer Lett, 2019, 442(320-32.\u003c/li\u003e\n \u003cli\u003eXU L, CHEN X, SHEN M, et al. Inhibition of IL-6-JAK/Stat3 signaling in castration-resistant prostate cancer cells enhances the NK cell-mediated cytotoxicity via alteration of PD-L1/NKG2D ligand levels [J]. Mol Oncol, 2018, 12(3): 269-86.\u003c/li\u003e\n \u003cli\u003eZHANG Q, LIU S, PARAJULI K R, et al. Interleukin-17 promotes prostate cancer via MMP7-induced epithelial-to-mesenchymal transition [J]. Oncogene, 2017, 36(5): 687-99.\u003c/li\u003e\n \u003cli\u003eSAKELLARIOU C, ELHAGE O, PAPAEVANGELOU E, et al. Prostate cancer cells enhance interleukin-15-mediated expansion of NK cells [J]. BJU Int, 2020, 125(1): 89-102.\u003c/li\u003e\n \u003cli\u003eSORRENTINO C, CIUMMO S L, CIPOLLONE G, et al. Interleukin-30/IL27p28 Shapes Prostate Cancer Stem-like Cell Behavior and Is Critical for Tumor Onset and Metastasization [J]. Cancer research, 2018, 78(10): 2654-68.\u003c/li\u003e\n \u003cli\u003eWITT K, EVANS-AXELSSON S, LUNDQVIST A, et al. Inhibition of STAT3 augments antitumor efficacy of anti-CTLA-4 treatment against prostate cancer [J]. Cancer Immunol Immunother, 2021, 70(11): 3155-66.\u003c/li\u003e\n \u003cli\u003eXU Y, SONG G, XIE S, et al. The roles of PD-1/PD-L1 in the prognosis and immunotherapy of prostate cancer [J]. Mol Ther, 2021, 29(6): 1958-69.\u003c/li\u003e\n \u003cli\u003eYANG C, HUANG X, LI Y, et al. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology [J]. Brief Bioinform, 2021, 22(3):\u003c/li\u003e\n \u003cli\u003eGHANDI M, HUANG F W, JAN\u0026eacute;-VALBUENA J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia [J]. Nature, 2019, 569(7757): 503-8.\u003c/li\u003e\n \u003cli\u003eWILCZYŃSKI J R, NOWAK M. Cancer Immunoediting: Elimination, Equilibrium, and Immune Escape in Solid Tumors [J]. Exp Suppl, 2022, 113(\u003c/li\u003e\n \u003cli\u003eBRIUKHOVETSKA D, D\u0026ouml;RR J, ENDRES S, et al. Interleukins in cancer: from biology to therapy [J]. Nat Rev Cancer, 2021, 21(8): 481-99.\u003c/li\u003e\n \u003cli\u003eANESTAKIS D, PETANIDIS S, KALYVAS S, et al. Mechanisms and applications of interleukins in cancer immunotherapy [J]. Int J Mol Sci, 2015, 16(1): 1691-710.\u003c/li\u003e\n \u003cli\u003eTEWARI A K, STOCKERT J A, YADAV S S, et al. Inflammation and Prostate Cancer [J]. Adv Exp Med Biol, 2018, 1095(41-65.\u003c/li\u003e\n \u003cli\u003eVASTO S, CARRUBA G, CANDORE G, et al. Inflammation and prostate cancer [J]. Future Oncol, 2008, 4(5): 637-45.\u003c/li\u003e\n \u003cli\u003eLOPEZ-BUJANDA Z A, HAFFNER M C, CHAIMOWITZ M G, et al. Castration-mediated IL-8 promotes myeloid infiltration and prostate cancer progression [J]. Nat Cancer, 2021, 2(8): 803-18.\u003c/li\u003e\n \u003cli\u003eCHEN C H, HO C H, HU S W, et al. Association between interleukin-8 rs4073 polymorphism and prostate cancer: A meta-analysis [J]. J Formos Med Assoc, 2020, 119(7): 1201-10.\u003c/li\u003e\n \u003cli\u003eHAGHSHENAS M R, ZAMIR M R, SADEGHI M, et al. Clinical relevance and therapeutic potential of IL-38 in immune and non-immune-related disorders [J]. Eur Cytokine Netw, 2022, 33(3): 54-69.\u003c/li\u003e\n \u003cli\u003eAKDIS M, AAB A, ALTUNBULAKLI C, et al. Interleukins (from IL-1 to IL-38), interferons, transforming growth factor \u0026beta;, and TNF-\u0026alpha;: Receptors, functions, and roles in diseases [J]. J Allergy Clin Immunol, 2016, 138(4):\u003c/li\u003e\n \u003cli\u003eYAMANE H, PAUL W E. Cytokines of the \u0026gamma;(c) family control CD4+ T cell differentiation and function [J]. Nat Immunol, 2012, 13(11): 1037-44.\u003c/li\u003e\n \u003cli\u003eROJO R, RAPER A, OZDEMIR D D, et al. Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations [J]. Nat Commun, 2019, 10(1): 3215.\u003c/li\u003e\n \u003cli\u003eKALAORA S, NAGLER A, WARGO J A, et al. Mechanisms of immune activation and regulation: lessons from melanoma [J]. Nature reviews Cancer, 2022, 22(4): 195-207.\u003c/li\u003e\n \u003cli\u003eCHAN T A, YARCHOAN M, JAFFEE E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic [J]. Ann Oncol, 2019, 30(1): 44-56.\u003c/li\u003e\n \u003cli\u003eRICCIUTI B, WANG X, ALESSI J V, et al. Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels [J]. JAMA Oncol, 2022, 8(8): 1160-8.\u003c/li\u003e\n \u003cli\u003eREBELLO R J, OING C, KNUDSEN K E, et al. Prostate cancer [J]. Nat Rev Dis Primers, 2021, 7(1): 9.\u003c/li\u003e\n \u003cli\u003eHANSEN A R, MASSARD C, OTT P A, et al. Pembrolizumab for advanced prostate adenocarcinoma: findings of the KEYNOTE-028 study [J]. Ann Oncol, 2018, 29(8): 1807-13.\u003c/li\u003e\n \u003cli\u003ePHILIPPOU Y, PROTHEROE A S, BRYANT R J. Re: Pembrolizumab for Treatment-refractory Metastatic Castration-resistant Prostate Cancer: Multicohort, Open-label Phase II KEYNOTE-199 Study [J]. Eur Urol, 2020, 77(6): 759-60.\u003c/li\u003e\n \u003cli\u003eXU Y, SONG G, XIE S, et al. The roles of PD-1/PD-L1 in the prognosis and immunotherapy of prostate cancer [J]. Mol Ther, 2021, 29(6): 1958-69.\u003c/li\u003e\n \u003cli\u003eGUO Y, XU F, LU T, et al. Interleukin-6 signaling pathway in targeted therapy for cancer [J]. Cancer Treat Rev, 2012, 38(7): 904-10.\u003c/li\u003e\n \u003cli\u003eJASPERS J E, KHAN J F, GODFREY W D, et al. IL-18-secreting CAR T cells targeting DLL3 are highly effective in small cell lung cancer models [J]. J Clin Invest, 2023, 133(9):\u003c/li\u003e\n \u003cli\u003eBHATIA V, KAMAT N V, PARIVA T E, et al. Targeting advanced prostate cancer with STEAP1 chimeric antigen receptor T cell and tumor-localized IL-12 immunotherapy [J]. Nat Commun, 2023, 14(1): 2041.\u003c/li\u003e\n \u003cli\u003eKAMRADT J M, PIENTA K J. Etoposide in prostate cancer [J]. Expert Opin Pharmacother, 2000, 1(2): 271-5.\u003c/li\u003e\n \u003cli\u003eClofarabine [J]. Drugs R D, 2004, 5(4): 213-7.\u003c/li\u003e\n \u003cli\u003eLI Y, CHEN S, ZHU J, et al. Lovastatin enhances chemosensitivity of paclitaxel-resistant prostate cancer cells through inhibition of CYP2C8 [J]. Biochem Biophys Res Commun, 2022, 589(85-91.\u003c/li\u003e\n \u003cli\u003eGU S S, ZHANG W, WANG X, et al. Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade [J]. Cancer Discov, 2021, 11(6): 1524-41.\u003c/li\u003e\n \u003cli\u003eWIERNIK P H. Alvocidib (flavopiridol) for the treatment of chronic lymphocytic leukemia [J]. Expert Opin Investig Drugs, 2016, 25(6): 729-34.\u003c/li\u003e\n\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":"chemokine, prostate cancer, prognostic signature, immune cell infiltration, drug selection, immunotherapies","lastPublishedDoi":"10.21203/rs.3.rs-4117470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInterleukins can nurture a tumor promoting environment and simultaneously regulate immune cell infiltration. However, the potential roles of interleukins in the prostate cancer immune landscape remain abstruse.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe comprehensively investigated the interleukin expression patterns and tumor immune landscape of prostate cancer patients. And explored the interleukin expression patterns with immune infiltration landscape. The interleukin score was established using LASSO cox regression analysis. Multivariate Cox regression analysis was employed to assess the prognostic value of the interleukin score.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified two distinct interleukin clusters, characterized by different immune cell infiltration, tumor promoting signaling pathways activation and prognosis. The interleukin score was established to estimate the prognosis of individual prostate cancer patient. Further analysis demonstrated that the interleukin score was an independent prognostic factor of PRAD. Finally, we investigated the predictive value of interleukin score in the programed cell death protein (PD-1) blockade therapy of patients with prostate cancer.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrated the correlation between interleukin and tumor immune landscape in prostate cancer. The comprehensive evaluation of interleukin expression patterns in individual prostate patients contribute to our understanding of the immune landscape and helps clinicians selecting proper immunotherapy strategies for prostate patients.\u003c/p\u003e","manuscriptTitle":"Ectopic expression of chemokines and chemokine receptors in relation to immune cell infiltration, prostate cancer recurrence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 16:09:34","doi":"10.21203/rs.3.rs-4117470/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":"25b77155-75a1-4543-9c13-c42091b9bf95","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-13T14:44:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 16:09:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4117470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117470","identity":"rs-4117470","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}