Comprehensive miRNA profiles revealing cancer-immunity

Comprehensive miRNA profiles revealing cancer-immunity | 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 Article Comprehensive miRNA profiles revealing cancer-immunity Jiaqi Yin, Yi zhou, Zhenghong Chang, Jiaxin Yu, Chunyu Wang, Zhipeng Qian, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7430586/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 MicroRNAs (miRNAs) play a pivotal role as post-transcriptional regulators in tumorigenesis, influencing immune pathways in various cancers. However, a systematic identification of potential miRNAs influencing immune pathway activity remains largely elusive. In this study, we presented a comprehensive analysis of miRNAs within 17 immune-related pathways across 32 different cancers. Leveraging GSEA-based and target gene-based computational methods, we identify potential miRNA regulons that are intricately associated with tumor immunity. These miRNAs exhibit a propensity to regulate immune pathways across multiple cancer types. Moreover, miRNA immune regulons manifest expression perturbations in cancer and display significant correlations with immune cell infiltrations. Our study reveals the role of immune-related miRNAs in immune cell development, differentiation, and tumor growth and metastasis dynamics. Furthermore, we optimized two key immunology miRNA regulons, exemplified by hsa-miR-130b-3p and hsa-miR-106b-5p, demonstrating their wide-ranging influence on immune function in tumors. These miRNAs emerged as potential targets for a variety of drugs, offering promise as adjuvant therapy alongside conventional radiotherapy and chemotherapy, as well as contributing to immunotherapeutic approaches. Additionally, our study identifies two molecular subtypes within reproductive system cancers, characterized by distinct tumor mutational burdens (TMB L and TMB H phenotypes). These subtypes exhibit disparities in immune cell infiltrations, checkpoint expression, and prognosis, shedding light on potential avenues for personalized treatment strategies. In summary, our research provides an extensive panorama of miRNA immunology regulons, enhancing our understanding of miRNA function. This knowledge holds significant implications for the development of targeted therapies in tumor immunology and the refinement of personalized treatment approaches. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology immune miRNA Pan cancer pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Tumor cells and their surrounding inflammatory cells, immune cells, cytokines, and interstitial tissues together constitute a complex system of tumor microenvironment, which plays an important role in in cancer development( 1 – 6 ). Understanding how multiple immune abnormalities affect the fate of tumors at different stages, which is necessary for clinical detection and therapeutic intervention. Immunotherapy based on the disorder of immune regulation is the latest cancer treatment strategy after radiotherapy and chemotherapy, and has changed the treatment prospect of a variety of malignant tumors( 2 , 7 , 8 ). However, low response rate, complex and diverse adverse immune events and different levels of drug resistance greatly limit the clinical effectiveness of immunotherapy( 9 – 11 ). In the aspects of basic research and clinical application, it is urgent to elucidate the molecular mechanism of tumor immune regulation and find efficient and reliable markers for immunotherapy prediction. Mis-regulation of gene expression programs have been found to cause a broad range of human diseases. miRNAs as an important class of regulons, regulating various cellular processes( 12 , 13 ). Recently, increasing evidence has revealed that miRNAs exert intricate involvement spans processes such as immune cell development, differentiation, and the orchestration of immune responses against cancer cells( 14 – 16 ). For example, miRNA miR-181 has been revealed that miRNAs can drive early natural killer cell development( 17 ). Let-7 miRNA family has been found to participate in the regulation of invariant natural killer T (NKT)( 18 ). Dynamic expression of let-7 miRNAs influenced the IL-4 and IL-15( 18 ). Understanding the specific miRNA signatures associated with immune regulation in tumors holds great promise for advancing cancer immunotherapy. The exploration of miRNA involvement in tumor immunity represents a crucial avenue for unraveling the complexities of the immune response to cancer and holds potential for the development of innovative and personalized cancer treatments. Therefore, further studies on the impact of miRNA-mediated immune modulation will be essential to identify potential therapeutic targets and develop strategies for harnessing the immune system to combat cancer effectively. To address this gap, we systematically identified potential miRNA regulons associated with immune-related pathways across 32 distinct cancer types. Our observations revealed a tendency of miRNA for immune regulation exhibits heightened expression within immune cell populations, showing perturbed expression patterns in cancer and establishing correlations with immune cell infiltration. This identification of miRNA immune regulons not only aids in recognizing cancer-related genes but also facilitates the delineation of cancer subtypes characterized by clinical features. Attaining a comprehensive understanding of the entire spectrum of potential miRNA regulons linked to immune-related pathways is a crucial prerequisite for unraveling the intricacies of the regulatory network within the tumor microenvironment. Results Landscape of immune-related miRNAs across cancer types To systematically explore the role of miRNA in tumor immune regulation, we proposed a computational framework named ImmMir to analyze the genome-wide expression profiles of 32 cancer types and 17 immune-related pathways (Fig. 1 A and Fig. S1A-B). Firstly, due to the inhibitory effect of miRNA on the expression of target genes, for each miRNA, the mRNA set meeting the significant negative correlation threshold was selected as input and performed hypergeometric test with the mRNAs from each immune-related pathway. In total, we identified 1147 immune-related miRNAs across cancers. We found that the proportions of immune miRNAs were widely distributed and more likely to appear in multiple cancers, there were about 39.57% miRNAs were identified to regulate the immune-related pathways in more than seven cancer types, and these miRNAs regulated immune-related pathways in up to 30 cancers, showing pan-cancer properties (Fig. 1 B). However, miRNA-pathway pairs tended to occur in a single cancer type, 40.9% of miRNA-pathway pairs were identified in only one cancer type (Fig. 1 C). we found that there were more miRNA-pathway pairs in BLCA, LUSC, TGCT and THCA. Especially, we identified the largest number of miRNA-pathway pairs in BLCA, with 919 pairs. This suggests that the number of immune miRNA-pathways identified by target genes is cancer-specific. Next, we further applied the correlation between miRNA and mRNA expression as the rank and applied gene set enrichment analysis to identify immune-related miRNA regulators. As results, we also found that these pathways were potentially regulated by various miRNAs. We identified 1231 immune-related miRNAs, accounting for 79.63% of all miRNAs. We found that most of the immune miRNAs regulate multiple cancers, 29% were identified in more than 7 cancers, while only 213 were identified in a single cancer, accounting for 17.3% of all immune miRNAs. By statistical analysis of the universality of immune miRNAs in regulating immune pathways in cancers, we found that most of the immune miRNAs regulate multiple cancers, 29% of miRNAs were identified in more than 7 cancers, while only 213 immune miRNAs were identified in only one cancer. It accounts for 17.3% of all immune miRNAs (Fig. 1 B). However, miRNA-pathway pairs have different characteristics. In most cancers, miRNAs regulate only one immune-related pathway, accounting for 57.69% of all miRNA-pathway pairs (Fig. 1 C). The results indicate that miRNAs broadly regulate immune pathways in tumors, but the immune pathways that miRNAs are involved in regulating in different cancers are cancer-specific. Wide range of immune pathways regulated by miRNAs Some immune cells will invade tumor tissues during the process of tumor occurrence and development, and the differences of infiltration degree of different immune cells will affect the survival of patients. In order to evaluate whether immune miRNAs tend to participate in immune infiltration, we conducted immune cell infiltration analysis for each patient by using Tumor immune Estimation Resource (TIMER) for immune-related miRNAs in 31 identified solid tumors.. Fisher's exact test was subsequently employed to determine if immune-related miRNAs tended to be associated with immune cell infiltration. We found that a significantly higher proportion of immune-related miRNAs were associated with immune cell infiltration in most cancer types, particularly 87.5% (28/32) of cancers and 78.1% (25/32) of cancer types with CD8 + T cell and macrophage infiltrations showing a significant difference between immune-related and all miRNAs (Fig. 2 A). CD8 + T cell infiltration has been shown to be a biomarker for predicting prognosis and therapeutic response. It is noteworthy that in almost all cancer types and immune cells, immune-related miRNAs had a positive regulatory effect on immune cell invasion (OR > 1)(Fig. 2 A). These results indicate that miRNAs identified by both gene enrichment analysis and target genes are strongly related to immune infiltration, thus participating in the regulation of tumor metastasis and invasion to normal tissues. To confirm the regulation of immune pathways in immune cells by cancer-immune miRNAs. Therefore, gene set enrichment analysis was used to identify the immune pathways regulated by miRNAs in immune cells, and the enrichment scores of 32 tumors and 15 immune cell lines were obtained. It was found that the ES values of tumor immune-related miRNAs identified by gene enrichment analysis were all greater than 0 (Fig. 2 B). This suggests that immune miRNAs have a higher ranking and tend to be highly expressed in immune cells. At the same time, we also found that although immune miRNAs in B naive cells, CD4 stem cells, Treg memory cells, etc. have higher ES, there is little difference in ES scores among immune cells. The ES values of immune miRNAs vary greatly among cancer types, which might be connected to tumor heterogeneity. Immune miRNAs play various functions in different types of tumors and show different expression patterns. On the other hand, immune miRNAs identified by target gene analysis also showed similar expression patterns (Fig. 2 B), and tended to be highly expressed in immune cells, especially B cell naive and CD4 STIM cells. These results suggest that the immune-related miRNAs identified by these two methods are actively expressed in immune cell lines and actively contribute to the functioning of the immune. Finally, considering the multiple effects of miRNA on mRNA expression in immune pathways, the intersection miRNAs identified by the two methods were used as immune miRNAs for subsequent analysis (Fig. S2A-B). Immune-related miRNAs exhibit perturbed expression in cancer Dysregulation of miRNA expression can cause changes in the expression of target genes, resulting in changes in corresponding functions. Therefore, we investigated whether the expression of immune miRNA is dysregulated in cancer and analyzed the differential expression of immune miRNA in different cancer types. First, we used t test to measure the differential expression of miRNAs between cancer samples and normal samples, in which miRNAs with P value 1 were differentially expressed miRNAs. A total of 952 differentially expressed miRNAs were identified in 32 cancer types. These results indicate that miRNA expression in tumors is more likely to be enhanced than in normal tissues to regulate tumor development. Subsequently, to assess whether immune-related miRNAs tend to be differentially expressed in tumors, we used Fisher exact test to compare whether immune-related miRNAs tend to be differentially expressed. We found that among the 10 cancer types, the differential expression ratio of immune-related miRNAs identified by the two methods was higher than that of all miRNAs (Fig. 3 A). Compared with all miRNAs, the differential expression ratio of immune-related miRNAs obtained by target gene analysis was higher (Fig. 3 A). Immune-related miRNAs identified by target gene methods in PRAD had the highest specific difference. Among 225 immune-related miRNAs, the proportion of differentially expressed miRNAs was 34.22%. However, STAD showed the highest proportion difference of differentially expressed miRNAs identified by gene set enrichment analysis compared with target gene methods, and the proportion of differentially expressed 339 immune-related miRNAs in STAD was as high as 56.34%. These results indicate that immune miRNAs tend to perturbate their expression in tumors and participate in the regulation of immune pathways, thus participating in the occurrence and development of cancer. In order to synthesize the immune miRNA characteristics identified by the two methods, we first divided the 32 cancer types of TCGA into 11 systems according to clinical anatomical sites. Subsequently, the intersection number of miRNA regulators involved in immune pathways identified by the two methods was statistically analyzed, as shown in Fig. 3 B, miRNAs are mainly involved in the regulation of Antimicrobials, Antigen Processing and Presentation, Cytokine Receptors and TGFb Family Member Receptor pathways. We found the distribution of immune-related miRNA-pathway pairs identified in the same system is similar. For example, in COAD and PAAD in the Digestive system, miRNAs regulate 14 immune pathways, most of which are the same (13/14). There were also similar numbers of immune-regulating miRNAs in the two cancers, with 115 immune-related miRNAs in COAD and 111 in PAAD. This indicates that cancer types in the same cancer system have similar immune regulation patterns, while different cancer systems have specific immune regulation characteristics. In Digestive system, hsa-miR-671-5p had the highest immune regulatory capacity. In colon cancer, overexpression of hsa-miR-671-5p targeting TRIM67 significantly increased the proliferation, migration, and invasion of tumor-derived cells( 19 ). This indicates that our weight-based approach can effectively identify miRNAs that have immunoregulatory effects on tumors, and these miRNAs have stronger regulatory capabilities than other low-weighted miRNAs in their respective tissue systems, while they may have low weights and regulate fewer immune pathways in other systems, such as in THYM. hsa-miR-129-5p regulates three immune pathways, while only the interferon receptor pathway is regulated in the lung system. This suggests that key immune miRNAs of the cancer system are tissue-dependent. ImmMir prioritizes cancer-related miRNAs Study confirms miRNAs regulate more immune pathways in multiple cancers and miRNA is widely involved in cancer immune regulation in the development of cancer. In order to identify key miRNA regulators with extensive immune regulatory capabilities in the range of malignant tumors, we adopted a weight-based approach to screen such key immune miRNAs. First, we ranked each miRNA according to the number of regulatory pathways in each cancer type, and assigned a weight W to each miRNA in each cancer type according to the ranking. This weight can measure the ability of miRNA to regulate immune function in the corresponding cancer type, then average the number of pathways regulated by each miRNA in each cancer type, and reassign the weight Wmean according to the number of normal pathways, and use this weight to measure the ability of miRNA to regulate immune function in malignant tumors. We found that the 50 miRNAs with the highest weight (Fig. 4 A) tended to regulate multiple pathways in cancer, among which the hsa-miR-301b-3p with the highest weight regulated 10 immune pathways in 15 cancer types. It has been reported that miR-301b-3p is up-regulated in breast cancer and down-regulated by targeting NR3C2, thus promoting tumor cell proliferation, migration and invasion( 20 ). hsa-miR-301b-3p is also involved in tumor growth, metastasis and angiogenesis as an oncogene in many cancers, including hepatocellular carcinoma( 21 ), lung cancer( 22 ), and ovarian cancer( 23 ). We noted that hsa-miR-130b-3p (Wmean = 0.9928), which had a high average weight, was involved in the regulation of 11 immune pathways in 14 cancer types, and had a high rank among key immune miRNAs (rank = 6). The role of hsa-miR-130b-3p in malignant tumors has been reported. For example, it is upregulated in colorectal cancer (CRC) and regulates its expression by targeting the negative direction of CHD9 (chromodomain helicase DNA binding protein 9), thus promoting the proliferation of colorectal cancer (CRC)( 24 ). hsa-miR-130b-3p is also involved in tumor metastasis and angiogenesis as an oncogene in other cancers, such as hepatocellular carcinoma( 25 ), lung cancer( 26 , 27 ), oral squamous cell carcinoma( 28 ), gastric cancer( 29 ), bladder cancer( 30 ). In addition, hsa-miR-106b-5p, as a miRNA that has been frequently studied in the field of cancer, has a high ranking (rank = 8) among key immune miRNAs. miR-106b-5p inhibits tumor growth, metastasis, and invasion by targeting VEGFA and PAK5 (p21 activated kinase 5)( 31 ). VEGFA is a member of the PDGF/ VEGF growth factor family, which can directly participate in the expansion of Treg cells and inhibit the expression of CD8 + CTL and PD-1 receptor, and is also a key factor in tumor angiogenesis. PAK5, a member of the PAK5-EGR1-MMP2 signaling pathway, is an important regulator of cell migration and invasion. These two miRNAs have also been found to be enriched in cytokine receptor pathways in a variety of cancers, and this result also validates their key immunoregulatory role in tumors to some extent (Fig. 4 B-C). In order to evaluate the ability of ImmMir to prioritize cancer-related miRNAs, the average rank distribution of miRNAs associated with immune pathways in each cancer was normalized and it was found that miRNAs ranked high in most cancers (Fig. 4 D. Moreover, the grades of cancer/disease-related miRNAs are known to be significantly higher than those of other miRNAs (Fig. 4 E), and they are significantly enriched in miRNAs in HMDD and MIR2DISEASE (Fig. 4 F). Taken together, these results suggest that integrating ImmMir results can help prioritize cancer-associated miRNAs to improve our understanding of their regulatory functions in cancer. Cancer subtyping based on immune-related miRNAs In addition to identifying the key miRNAs of cancer, cancer subtypes are key to improving personalized treatment. Therefore, we next investigated to what extent miRNAs identified by ImmMir can be applied to molecular cancer subtypes. If the tissues and organs of the reproductive system become cancerous, they often have relatively greater differences due to their special tumor microenvironment. Considering the complexity of the reproductive system, through consistent clustering of the expression profiles of 22 key miRNA regulatory factors in the reproductive system, it was found that the patients were divided into two subtypes, miRNA expression of subtype 1 was higher than that of subtype 2, and most UCEC patients were divided into subtype 1, while PRAD was divided into subtype 2 (Fig. 5 A). 22 miRNAs were differentially expressed in all UCEC patients, and we found that miRNAs such as hsa-miR-130b-3p and hsa-miR-103-3p were differentially up-regulated in cancers (Fig. 5 B). Several other miRNAs have also been shown to promote tumor cell proliferation and metastasis in reproductive system cancers. The inhibitory effect of miR-19a-5p on oncogene EZH2 can accelerate the proliferation, migration and invasion of tumor cells( 32 ). miR-182-5p plays a carcinogenic role in breast cancer by inhibiting the expression of FOXO3a( 33 ). miR-183-5p targets RGS2 to promote breast cancer development( 33 ). Survival analysis showed that cancer subtype 2 (p = 1.904e-05) had a better prognosis (Fig. 5 C). In addition, clustering of miRNA regulators in reticuloendothelial and brain tissue can also distinguish several cancer subtypes, and these subtypes are significantly correlated with patient prognosis (Fig. S3). Immune evasion is one of the main mark of cancers. Tumor cells often have genetic mutations and are presented as neoantigens by the major histocompatibility complex protein (MHC) on their surfaces, and T cells can recognize neoantigens and target these tumor cells for destruction. If immunogenic neoantigens are present on the surface of cancer cells, it helps to identify cancer cells. Since neoantigens are the result of mutations, the greater the number of mutations, the greater the chance that some of the neoantigens presented by the MHC protein will be immunogenic and therefore able to recognize and eradicate cancer cells( 34 – 36 ). To this end, we obtained the TMB of samples from TCGA, standardized log10, and calculated the distribution of TMB of reproductive system subtypes. As shown in Fig. 5 D, subtype 1 has a higher TMB than subtype 2 (rank sum test, p < 2.2e-16), which indicates that there may be more cancer-causing mutations associated with corresponding tumors in subtype 1, with more prominent tumor characteristics and easy recognition by T cells. We defined subtype 1 as TMB H and subtype 2 as TMB L, and TMB H may be sensitive to immune checkpoint inhibitors. At the level of immune cell infiltration, subtype 1 had a lower level of immune cell (except B cell) infiltration (Fig. 5 E), and subtype 1 had a higher enrichment score in the immune mark gene set (Fig. 5 F), indicating that a high level of immune cell infiltration of subtype 1 may have a stronger immune response. These results indicate that these key immune-related miRNAs are more inclined to oncogenes, and may inhibit the expression of immune-related genes through up-regulation, thus weakening the function of immune cells, and thus helping the proliferation and spread of tumor cells. Potential drugs targeting immune-miRNAs Small molecule chemotherapeutic drugs have been widely used in clinical anti-cancer therapy, but the therapeutic effect and outcome of these small molecule drugs are affected by serious side effects such as systemic toxicity( 37 ). The complexity of signaling pathways is another great challenge in cancer therapy( 38 ). When a single signaling pathway is blocked, but cancer cells can continue to develop by activating another parallel signaling pathway( 39 ). From this point of view, cancer can be effectively treated by silencing tumor-related genes, blocking tumor signaling pathways, and supplemencing insufficient regulators. The dysregulated expression of miRNA regulators is closely related to disease diagnosis, prognosis and treatment response. Therefore, we hope to start from the small molecule drugs targeting miRNA and understand the mechanism of their regulation of miRNA target genes, so as to combine with the existing immune checkpoint therapy or chemotherapy to kill or inhibit tumor cells more effectively Based on the small molecule drug and miRNA relationship pairs obtained from SM2miR, miRNA-drug subnetworks related to key immune miRNAs were screened, and then fused with the miRNA-pathway relationship pairs of enrichment analysis results. By further adding the leadingedge genes of known oncogenes into the network to finally obtain the drug-miRNA-leadingedge gene-pathway network, we can find that hsa-miR-16-5p is targeted by up to 15 small molecule drugs (Fig. 6 A). These drugs regulate downstream targets by targeting miRNAs to achieve the purpose of treating or improving the disease. For example, Cisplatin can inhibit the proliferation of human neuroblastoma cells (SH-SY5Y) in vitro and in vivo by up-regulating the expression of miR-16 targeting BDNF( 40 ). hsa-miR-16-5p is usually discussed as a tumor suppressor, and miR-16-5p is overexpressed in neuroblastoma, which inhibits tumor progression by directly targeting MYCN and reducing its expression level( 41 ). It is worth noting that miR-16-5p can inhibit the progression and invasion of osteosarcoma by targeting Smad3( 42 ). Smad3, as an important transporter in the transforming growth factor beta(TGFb) signaling pathway, can transport TGFb signals from the cell membrane to the nucleus, thereby enabling TGFb to bind to related factors. It regulates the expression of target genes, thereby controlling cell proliferation. Through the analysis of radiotherapy and chemotherapy responses of patients with two subtypes, we found that subtype1 had a higher response rate than subtype2 (Fig. 6 B). Taken together, these findings suggest that the identified key immune miRNAs can broadly regulate cancer progression through oncogenes and immune genes, and many small molecule drugs can target these miRNAs. These miRNA drug targets provide our window into miRNA expression intervention. These agents could also help provide an alternative approach when traditional miRNA-targeted therapies fail, as well as the possibility of drug combinations. Discussion There is increasing evidence that miRNAs are important for complex tumor microenvironment systems. So far, however, only a few examples have been found. In this study, we report the integration of GSEA and target gene analysis to identify immune-related miRNA regulators in malignant tumors called ImmMir. In addition, key miRNA regulators with pan-cancer properties were identified in the context of multiple malignant tumors, and their association with cancer immunity was analyzed from different levels. Next, cancers were systematically grouped according to clinical anatomical sites, key miRNA regulators were dug up, and molecular typing of reproductive system cancers was further conducted according to the expression of key miRNAs. Two molecular subtypes were identified, one of which, TBM H subtype, had higher miRNA expression, lower immune infiltration value, and higher mutation load score. And worse prognosis, suggesting that miRNA plays an important role in tumor immunity research and can be used as a potential biological target, indirectly providing guidance for tumor immune-related research and clinical application. We systematically integrated RNA-seq expression profiles of miRNAs and mRNAs in TCGA malignant tumors, clinical information of patients and other data resources, and used GSEA and target gene to identify immune-related miRNA regulators in malignant tumors. Based on the number distribution, immune characteristics, and expression patterns of the two methods, It was revealed that both methods identified immune-related miRNAs widely involved in different cancer types, which tended to be differentially expressed in malignant tumors, and were significantly correlated with the level of immune infiltration, which tended to be cancer-related miRNAs. However, the regulation of specific immune pathways by immune miRNAs is cancer-specific, and specific miRNA-pathway tends to appear in only one type of cancer. We compared the distribution characteristics of the miRNAs identified by the two methods, and found that compared with the immune-related miRNA regulators identified by target gene analysis, gene enrichment analysis identified more immune-related miRNA regulators, but covered fewer cancer types. These results all indicate that immune-related miRNAs have extensive regulation of immunity in malignant tumors. In addition, in order to retain the characteristics of the immune miRNAs identified by both functional enrichment analysis and target gene analysis, we retained the intersection ImmMir of immune miRNA regulators identified by the two methods and identified key miRNA regulators in malignant tumors based on weights. The focus is on immune miRNAs that tend to regulate different pathways in multiple cancers. All key immune miRNAs tend to be differentially expressed, correlated with the level of immune infiltration, and tend to appear in immune cell lines, and are targeted by a variety of small molecule drugs. Functional enrichment analysis of key immune miRNAs showed that these miRNAs were enriched in a large number of functions, such as immune system development and p53-mediated DNA damage repair, among which hsa-miR-130b-3p and hsa-miR-106b-5p were two representative miRNAs. They have high weight and high functional network degree, and have been proved by many studies to be closely related to tumor immunity. We focused on key immune miRNAs in the reproductive system, and based on their expression, molecular typing of cancer samples in the reproductive system was performed, and one subtype, TBMH, was identified as having higher miRNA expression, lower immune infiltration value, higher mutation load score, and worse prognosis. These key miRNAs are risk factors for survival propensity and can be targeted by multiple drugs. These findings have important implications for precision cancer treatment and immunotherapy. Our study not only analyzed the complexity of the cancer immune system, but also identified the key miRNAs that regulate the immunity of malignant tumors. Moreover, the classification of cancer systems based on the expression of key miRNAs is helpful to analyze the expression pattern of miRNA and immune regulation mechanism in the process of tumor development. It provides a potential target for personalized diagnosis and immunotherapy of malignant tumors. Methods Genome-wide miRNA and mRNA expression across cancer types Our analyses are mainly based on miRNA and miRNA expression datasets for 32 cancer types were downloaded from TCGA ( https://portal.gdc.cancer.gov ) via the R package “TCGAbiolinks”( 43 ). The fragments per kilobase of transcript per million (FPKM) mapped reads-based gene expression profile of miRNA and mRNA were obtained. Firstly, the miRBase ID of miRNAs was converted to the corresponding mature miRNA names utilizing the ID mapping file of miRBase( 44 ). Simultaneously, the Ensembl ID was transformed into gene symbols using the ID mapping file of Ensembl. Subsequently, we retained miRNA and mRNA entities only if they were expressed in at least 30% of the samples. For further normalization, instances where the expression value was 0 were replaced with the minimum value in the respective row. Finally, the FPKM values were log2-scaled, resulting in the generation of comprehensive expression profiles for both miRNA and mRNA. These refined expression profiles were then employed in subsequent analytical steps. Identification of immune-related miRNA In view of the influence of tumor purity on miRNA and mRNA expression, we first use the expression profile of mRNA to calculate the purity of the TCGA sample using the ESTIMATE( 45 ) ( https://bioinformatics.mdanderson.org/estimate/rpackage.html ). For each cancer type, Intersection of tumor purity, miRNA expression profile, and mRNA expression profile sample were retained. Tumor purity is used as a covariate, and the partial correlation coefficient (PCC) between miRNA i and mRNA j is calculated as follows: $$\:PCC(miRNA\:i,\:mRNA\:j)=\frac{{R}_{MG}-{R}_{MP}\ast\:{R}_{GP}}{\sqrt{1-{R}_{MP}^{2}}\ast\:\sqrt{1-{R}_{GP}^{2}}}$$ Where R MG R MP R GP are the correlation coefficients between the expression of miRNA i and mRNA j, the expression of miRNA i and tumor purity, and the expression of mRNA j and tumor purity, respectively. We identified miRNA-mRNA pairs whose expression was significantly negatively correlated (p < 0.05, r < 0), furthermore, integrated immune pathway data and obtained miRNAs that regulate specific immune pathways in each cancer background by cumulative hypergeometric test(p < 0.05). Then calculate the RS value according to pcc. $$\:statistic=pcc\ast\:\sqrt{\frac{n-2-g\ast\:n}{1-{pcc}^{2}}}$$ $$\:p=2\ast\:pnorm(-\left|statistic\right|)$$ $$\:RS={-log}_{10}\left(p\right)\ast\:sign\left(p\right)$$ Where n is the number of sample intersections, g is 1, pnorm is the normal distribution value, sign is the positive or negative of p, and finally the RS value between miRNA and mRNA. Specifically, when pcc = INF, pnorm (-INF) = 0. In order to prevent significant results from being excluded, when p = 0, p = p + 0.01. In order to identify immune-related miRNAs, we downloaded 17 immune-related pathways from ImmPort( 46 ). The RS value of miRNA is used as input, and the immune pathway enrichment analysis of miRNA by the fgsea package. Because mRNA should be negative regulation by miRNA, when the ES value > 0, let pp = 2*p-1 and when the ES value 0.995 and padj < 0.05 Immune pathway regulation pair. Differential expression of miRNAs In the process of conducting the differential expression analysis, we initiated by excluding cancer types with fewer than 5 normal samples. Subsequently, differential expression analysis was executed using the t-test for the remaining 17 cancer types. To discern the magnitude of expression changes, the log2-fold change (log2FC) between the expression values of cancer samples and their corresponding normal samples was computed. Genes were deemed significantly differentially expressed if they exhibited a p-value less than 0.05 and |log2FC| greater than 1. miRNA target gene enrichment analysis To validate the robustness of the leading-edge genes associated with immune miRNAs, we retrieved miRNA target gene data from miRTarBase( 47 ) ( http://mirtarbase.mbc.nctu.edu.tw/php/index.php ) and mirDIP( 48 ) ( http://ophid.utoronto.ca/mirDIP/index.jsp ). The Ensembl gene IDs of the miRNA target genes were then converted into corresponding symbols using the Ensembl ID mapping file. Additionally, the miRBase IDs of the miRNAs were transformed into mature miRNA names through the miRBase ID mapping file. To assess the convergence between the leading-edge genes and the target genes of immune-related miRNAs, we conducted an overlap analysis utilizing the hypergeometric test. Simultaneously, non-immune-related miRNAs, characterized by non-significant FDR and pp values, underwent a similar analysis. This comprehensive approach allowed us to rigorously examine the consistency of the identified immune miRNA targets and ascertain their relevance in the context of immune regulation. Validation of immune cell line expression data In order to verify whether miRNAs in immune cells and TCGA cancers tend to regulate the same immune pathways, we collected immune cell line data from three sources. We initially conducted a keyword search using "Immune Cells" in GEO. Subsequently, through manual curation of immune cell-related literature, we identified RNA-seq data specifically pertaining to pure immune cells. This dataset encompasses 28 types of immune cells and comprises a total of 184 samples. Furthermore, we obtained RNA-seq data from Valeria Ranzani's publication( 49 ), which includes 14 types of immune cells and a total of 63 samples. We also downloaded miRNA and mRNA expression profile data for 13 immune cell types and 15 samples from the DICE database( 50 ) ( https://dice-database.org/ ). We integrated immune cell data from three different sources and conducted log2 normalization for subsequent analysis. Next, we examined the expression patterns of tumor immune-related miRNAs identified through gene enrichment analysis and target gene recognition in each cancer type across various immune cell lineages. Within each cell lineage expression profile, we specifically considered the expression of 1546 miRNAs. The expression values for each sample were averaged to obtain miRNA expression profiles associated with immune cell lineages. Utilizing the R package GSVA, we performed single-sample gene set enrichment analysis (ssGSEA)( 51 ) on the immune-related miRNAs identified by both methods. Ultimately, we obtained enrichment scores (ES values) representing the enrichment status between 32 tumor types and 15 immune cell lineages. Immune Infiltration Verification In order to verify that immune-related miRNAs are more likely to appear in immune infiltrating tumors, the infiltration value of the 6 immune cells to the TCGA sample (LAML is a non-solid tumor without infiltration value) was downloaded from TIMER( 52 ) (Tumor immune estimate Resource). At first for each miRNA and immune cell, correlation between the expression value of miRNA and the infiltration value of immune cell was calculated. And then in 31 cancer types, the overlap significance of immune-related and infiltration-related miRNAs was analyzed by fisher test. Cancer miRNA enrichment analysis To verify that immune-related miRNA tend to play a role in cancer, cancer-related miRNAs were downloaded from HMDD( 53 ) ( http://www.cuilab.cn/hmdd ) and miR2disease( 54 ) ( http://www.mir2disease.org/ ). The miRBase ID and the irregular miRNA name are converted into mature miRNA names through the miRBase ID mapping file. And for every cancer, the immune-related miRNAs are first sorted by the number of regulatory immune pathways. Then the rank of each cancer is averaged, and sorting again. HMDD and miR2disease cancer-related miRNA, the average rank of immune-related miRNA as the input of cancer miRNA enrichment analysis (fgsea R package). Literature search verification In order to verify whether the immune-related miRNAs are mentioned in the published literature. At first two keyword searches on all miRNA were perform by RISmed R package, the first time search using “miRNA name”, and the second time search using “miRNA name + immune”. Then, among the miRNAs mentioned in the literature in the first search, fisher exact test is used to detect the significance of immune-related miRNA that mentioned in the second keyword search. Cancer subtype classification In order to classify the TCGA cancer subtypes according to the developmental system (Hematopoietic, Brain, Digestive, Endocrine, Head and Neck, Lung, pleura, Reproductive, reticuloendothelial, Skin, Soft Tissue, Urinary). First, for the cancer types of the each system, the immune-related miRNAs in the TCGA data, the immune-related miRNAs in the immune cells and the differentially expressed miRNAs are intersected. And then the expression value of these miRNA cancer samples uses the ConsensusClusterPlus R package for subtype clustering (maxK = 6, reps = 500, pItem = 0.8, pFeature = 1, seed = 123456, clusterAlg="km", distance= "euclidean"). For the value of k, choose k = 5 according to the relative change in area under CDF curve. We combined the cancer types of the reproductive system. First, we screened 7 cancer types belonging to the reproductive system, and reserved 3 cancer types in TCGA with normal samples greater than 5, including BRCA, PRAD and UCEC, so as to screen miRNAs with dysregulated expression. We then screened 50 key immune miRNA regulators of the reproductive system that were present in all three cancer expression profiles and differentially expressed in at least one cancer type, resulting in 22 miRNAs. After determining the subtype classification of each system, the survival of these subtypes, as well as the distribution of immune infiltration values, and the mutation load distribution of the sample were analyzed. The significant difference p-value is determined using the rank sum test. Validation of miRNA drug targeting data The drug targeting data of miRNAs is downloaded from SM2miR( 55 ) ( http://bioinfo.hrbmu.edu.cn/SM2miR/ ). First, immune-related miRNAs that regulate immune pathways in at least 10 cancers are selected. And then the sankey diagram of these miRNAs, the immune pathways regulated by these miRNAs, leading edge genes, and the drugs that regulate these miRNAs were visualized using the riverplot R package. Declarations Acknowledgments This work was supported by the National Science and Technology Major Project [2022ZD0117700]; the National Natural Science Foundation of China [32300541 and 62231013]; Natural Science Foundation of Heilongjiang Province [LH2023C063]; the China Postdoctoral Science Foundation [2023T160177, 2021M693831]; the Heilongjiang Postdoctoral Foundation [LBH-Z20071]. Author Contributions J.Q.Y and Y.Z contributed data analysis and paper writing. Z.H.C and Z.P.Q collected samples and generated data. J.X.Y and Z.H.C carried out data interpretations and helped data discussion. N.D contributed study design. G.H.W and Z.G.L contributed paper revisiting. Declaration of interests No potential conflicts of interest were disclosed. 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Hauschild AC, Pastrello C, Ekaputeri GKA, Bethune-Waddell D, Abovsky M, Ahmed Z, et al. MirDIP 5.2: tissue context annotation and novel microRNA curation. Nucleic Acids Res. 2023;51(D1):D217-D25. Ranzani V, Rossetti G, Panzeri I, Arrigoni A, Bonnal RJ, Curti S, et al. The long intergenic noncoding RNA landscape of human lymphocytes highlights the regulation of T cell differentiation by linc-MAF-4. Nat Immunol. 2015;16(3):318-25. Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J, et al. Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression. Cell. 2018;175(6):1701-15 e16. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108-e10. Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, et al. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019;47(D1):D1013-D7. Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database issue):D98-104. Liu X, Wang S, Meng F, Wang J, Zhang Y, Dai E, et al. SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression. Bioinformatics. 2013;29(3):409-11. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7430586","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513759452,"identity":"582feb38-91a5-4443-b33c-2d9f325aae56","order_by":0,"name":"Jiaqi Yin","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Yin","suffix":""},{"id":513759455,"identity":"821e8b39-bc6d-4927-819f-8672e96481a6","order_by":1,"name":"Yi zhou","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"zhou","suffix":""},{"id":513759458,"identity":"cb30d134-c5ec-4b94-85d5-54a9338eec42","order_by":2,"name":"Zhenghong Chang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenghong","middleName":"","lastName":"Chang","suffix":""},{"id":513759460,"identity":"17d6ffd2-34ac-436b-b773-6cf3217ffb0f","order_by":3,"name":"Jiaxin Yu","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Yu","suffix":""},{"id":513759463,"identity":"5c7a23f2-c939-4841-9ee6-c5b994c6decc","order_by":4,"name":"Chunyu Wang","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Wang","suffix":""},{"id":513759466,"identity":"2f82f0cf-635a-4ef9-89fe-3501f8c71118","order_by":5,"name":"Zhipeng Qian","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Qian","suffix":""},{"id":513759471,"identity":"d78f6cfd-614b-4583-99cd-3761a5467b50","order_by":6,"name":"Zhigang Liu","email":"","orcid":"","institution":"Foshan Women and Children Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Liu","suffix":""},{"id":513759472,"identity":"60dd00ff-fed8-4856-a530-c2aa6cfbf3ce","order_by":7,"name":"Guohua Wang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Guohua","middleName":"","lastName":"Wang","suffix":""},{"id":513759473,"identity":"c80f13c1-2f4b-448f-b99d-73faffeb6c1f","order_by":8,"name":"Na Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDCCw2DyAA8DMwPDBwYGmwQGCbAIM1FaGGcwMKQRoeUAggRpOUxYC99x3sOvC2ruyPCz8x5s+LjnfJ757O40CYYK68QG9rMHsGmRPMyXZj3j2DMeyWa+xMYZz24Xy9w5u02C4Ux6YgNPXgI2LQaHecyMedgO8wAZ5o95DtxOnCGRu02Cse1wYoMEjwFuLf/AWgybeQ6cg2r5h1eL8WPeNriWA1AtDbi1SAJtYebtA/mFx7BxxoHkxBkyZzdbJBxLN27jycGqhe/8GePPPN/u2PPznzFs+HDALnGGdO/GGx9qrGX72c9g1QIEbBKYYqCgYsOhHgiYP+CWGwWjYBSMglEABAA0VmMguMTUzgAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Na","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-08-22 04:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7430586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7430586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91615704,"identity":"f744ef70-5718-4ace-9ed5-13e23d4f00b3","added_by":"auto","created_at":"2025-09-18 10:32:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":491632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of immune-related miRNA.\u003c/strong\u003e (A) Two methods identifying immune-related miRNA regulators. The flowchart shows the schematic illustration based on gene targeting and GSEA. (B) Histograms represent the number of miRNAs regulating immune pathways in various of cancers simultaneously, and pie plots show the proportion of these miRNAs. (C) The histogram shows the number of miRNA-pathway relationship pairs in various of cancers simultaneously, and the pie plots shows the proportion of these relationship pairs. Blue representing GSEA methods and orange representing gene targeting methods.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/0570cd5107d1f3f3c29a4761.jpg"},{"id":91617026,"identity":"412baac1-49f6-494c-bedb-3a682a7e3d8e","added_by":"auto","created_at":"2025-09-18 10:48:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":739795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor immune-related miRNAs are closely related to tamorigenesis.\u003c/strong\u003e (A) Immune-related miRNAs tend to be miRNAs related to immune infiltration. The Lollipop Chart shows the fisher test results, x-axis representing the OR value; solid circle representing significant; hollow circle representing insignificant. (B) Tumor immune-related miRNAs tend to be highly expressed in immune cell lines. Heatmap shows the ssgsea results of immune-related miRNAs in immune cell line data.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/7dade63f08d8c6f86855a6ae.jpg"},{"id":91616816,"identity":"3b683d8f-3e8c-4a62-9316-d2917d905cf0","added_by":"auto","created_at":"2025-09-18 10:40:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key immune miRNAs.\u003c/strong\u003e (A) Immune-related miRNAs tend to be differentially expressed in tumors, numbers representing the proportion of differentially expressed miRNAs in immune-related miRNAs. (B) The intersection number of miRNA regulators involved in immune pathways identified by the two methods. The top bar-plot shows the number of cancer types involved in each immune pathway. The right bar-plot show the number of immune pathways regulated by miRNA for each cancer type. Gray representing the sum of miRNA numbers in the corresponding row or column.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/6066702520969d25f8a5d6bd.jpg"},{"id":91615710,"identity":"7e0e2746-bf70-48a0-890d-a0af7d798299","added_by":"auto","created_at":"2025-09-18 10:32:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1236856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrioritization of cancer-related miRNAs based on immune regulation. \u003c/strong\u003e(A) Heatmap shows mean weight of the Top50 immune-related miRNAs. (B-C) GSEA plot of key miRNA, line corresponding to the result of one cancer type. (B) for result of hsa-miR-130b-3p. (C) for the result of hsa-miR-106b-5p. (D) The distribution of relative rank for miRNAs. Each row is the normalized rank score for miRNAs. (E) The relative rank score distribution of cancer-related miRNAs and other miRNAs. (F) The enrichment score (ES) distribution for the immune related miRNA-HMDD and MIR2DISEASE respectively.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/f8f8ae7df145987d27b4f8b6.jpg"},{"id":91615708,"identity":"5c4b8eda-961f-4928-bb86-b6f3ae0a381e","added_by":"auto","created_at":"2025-09-18 10:32:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1036392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassifying cancer molecular subtypes of depending on key miRNA regulators.\u003c/strong\u003e (A) reproductive system cancer samples were classified into two subtypes depending on the expression of key miRNAs. (B) Key miRNAs tend to express dysregulation. The outer ring representing differential expression, different colors representing different cancer types, and the inner pie chart representing the number of differentially expressed cancer types. (C) Survival analysis of the two subtypes. (D-F) Tumor immune characteristics of the two subtypes. (D) for mutation load value, (E) for NES value of immune maker gene, (F) for immune infiltration value. Red characteristics subtype 1, blue characteristics subtype 2, * representing significant wilcoxon rank sum test.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/8378844c85185beb7a612cbe.jpg"},{"id":91616815,"identity":"ab503398-1967-4633-8b28-1151b61f0a99","added_by":"auto","created_at":"2025-09-18 10:40:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":231753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA can be used as potential targets for immunotherapy.\u003c/strong\u003e (A) Riverlplot of immune miRNAs used to classify cancer subtypes. Red representing that miRNA had adverse effects on survival, cox test showed HR\u0026gt;0 and p\u0026lt;0.05. (B) The barplot shows the proportion of patients who responded, did not respond or were not recorded after radiotherapy or chemotherapy for two subtypes patients.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/e39782eef753b066b2e24af5.jpg"},{"id":95312860,"identity":"719311ae-f26d-43a1-8b25-e4dc191f0c30","added_by":"auto","created_at":"2025-11-06 15:50:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5073752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7430586/v1/b17d7528-e0c2-48ea-81bb-a969625bc1c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive miRNA profiles revealing cancer-immunity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTumor cells and their surrounding inflammatory cells, immune cells, cytokines, and interstitial tissues together constitute a complex system of tumor microenvironment, which plays an important role in in cancer development(\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Understanding how multiple immune abnormalities affect the fate of tumors at different stages, which is necessary for clinical detection and therapeutic intervention. Immunotherapy based on the disorder of immune regulation is the latest cancer treatment strategy after radiotherapy and chemotherapy, and has changed the treatment prospect of a variety of malignant tumors(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, low response rate, complex and diverse adverse immune events and different levels of drug resistance greatly limit the clinical effectiveness of immunotherapy(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In the aspects of basic research and clinical application, it is urgent to elucidate the molecular mechanism of tumor immune regulation and find efficient and reliable markers for immunotherapy prediction.\u003c/p\u003e\u003cp\u003eMis-regulation of gene expression programs have been found to cause a broad range of human diseases. miRNAs as an important class of regulons, regulating various cellular processes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Recently, increasing evidence has revealed that miRNAs exert intricate involvement spans processes such as immune cell development, differentiation, and the orchestration of immune responses against cancer cells(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For example, miRNA miR-181 has been revealed that miRNAs can drive early natural killer cell development(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Let-7 miRNA family has been found to participate in the regulation of invariant natural killer T (NKT)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Dynamic expression of let-7 miRNAs influenced the IL-4 and IL-15(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Understanding the specific miRNA signatures associated with immune regulation in tumors holds great promise for advancing cancer immunotherapy. The exploration of miRNA involvement in tumor immunity represents a crucial avenue for unraveling the complexities of the immune response to cancer and holds potential for the development of innovative and personalized cancer treatments. Therefore, further studies on the impact of miRNA-mediated immune modulation will be essential to identify potential therapeutic targets and develop strategies for harnessing the immune system to combat cancer effectively.\u003c/p\u003e\u003cp\u003eTo address this gap, we systematically identified potential miRNA regulons associated with immune-related pathways across 32 distinct cancer types. Our observations revealed a tendency of miRNA for immune regulation exhibits heightened expression within immune cell populations, showing perturbed expression patterns in cancer and establishing correlations with immune cell infiltration. This identification of miRNA immune regulons not only aids in recognizing cancer-related genes but also facilitates the delineation of cancer subtypes characterized by clinical features. Attaining a comprehensive understanding of the entire spectrum of potential miRNA regulons linked to immune-related pathways is a crucial prerequisite for unraveling the intricacies of the regulatory network within the tumor microenvironment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLandscape of immune-related miRNAs across cancer types\u003c/h2\u003e\u003cp\u003eTo systematically explore the role of miRNA in tumor immune regulation, we proposed a computational framework named ImmMir to analyze the genome-wide expression profiles of 32 cancer types and 17 immune-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Fig. S1A-B). Firstly, due to the inhibitory effect of miRNA on the expression of target genes, for each miRNA, the mRNA set meeting the significant negative correlation threshold was selected as input and performed hypergeometric test with the mRNAs from each immune-related pathway. In total, we identified 1147 immune-related miRNAs across cancers. We found that the proportions of immune miRNAs were widely distributed and more likely to appear in multiple cancers, there were about 39.57% miRNAs were identified to regulate the immune-related pathways in more than seven cancer types, and these miRNAs regulated immune-related pathways in up to 30 cancers, showing pan-cancer properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, miRNA-pathway pairs tended to occur in a single cancer type, 40.9% of miRNA-pathway pairs were identified in only one cancer type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). we found that there were more miRNA-pathway pairs in BLCA, LUSC, TGCT and THCA. Especially, we identified the largest number of miRNA-pathway pairs in BLCA, with 919 pairs. This suggests that the number of immune miRNA-pathways identified by target genes is cancer-specific.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we further applied the correlation between miRNA and mRNA expression as the rank and applied gene set enrichment analysis to identify immune-related miRNA regulators. As results, we also found that these pathways were potentially regulated by various miRNAs. We identified 1231 immune-related miRNAs, accounting for 79.63% of all miRNAs. We found that most of the immune miRNAs regulate multiple cancers, 29% were identified in more than 7 cancers, while only 213 were identified in a single cancer, accounting for 17.3% of all immune miRNAs. By statistical analysis of the universality of immune miRNAs in regulating immune pathways in cancers, we found that most of the immune miRNAs regulate multiple cancers, 29% of miRNAs were identified in more than 7 cancers, while only 213 immune miRNAs were identified in only one cancer. It accounts for 17.3% of all immune miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, miRNA-pathway pairs have different characteristics. In most cancers, miRNAs regulate only one immune-related pathway, accounting for 57.69% of all miRNA-pathway pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The results indicate that miRNAs broadly regulate immune pathways in tumors, but the immune pathways that miRNAs are involved in regulating in different cancers are cancer-specific.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWide range of immune pathways regulated by miRNAs\u003c/h3\u003e\n\u003cp\u003eSome immune cells will invade tumor tissues during the process of tumor occurrence and development, and the differences of infiltration degree of different immune cells will affect the survival of patients. In order to evaluate whether immune miRNAs tend to participate in immune infiltration, we conducted immune cell infiltration analysis for each patient by using Tumor immune Estimation Resource (TIMER) for immune-related miRNAs in 31 identified solid tumors.. Fisher's exact test was subsequently employed to determine if immune-related miRNAs tended to be associated with immune cell infiltration. We found that a significantly higher proportion of immune-related miRNAs were associated with immune cell infiltration in most cancer types, particularly 87.5% (28/32) of cancers and 78.1% (25/32) of cancer types with CD8\u0026thinsp;+\u0026thinsp;T cell and macrophage infiltrations showing a significant difference between immune-related and all miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). CD8\u0026thinsp;+\u0026thinsp;T cell infiltration has been shown to be a biomarker for predicting prognosis and therapeutic response. It is noteworthy that in almost all cancer types and immune cells, immune-related miRNAs had a positive regulatory effect on immune cell invasion (OR\u0026thinsp;\u0026gt;\u0026thinsp;1)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These results indicate that miRNAs identified by both gene enrichment analysis and target genes are strongly related to immune infiltration, thus participating in the regulation of tumor metastasis and invasion to normal tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo confirm the regulation of immune pathways in immune cells by cancer-immune miRNAs. Therefore, gene set enrichment analysis was used to identify the immune pathways regulated by miRNAs in immune cells, and the enrichment scores of 32 tumors and 15 immune cell lines were obtained. It was found that the ES values of tumor immune-related miRNAs identified by gene enrichment analysis were all greater than 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This suggests that immune miRNAs have a higher ranking and tend to be highly expressed in immune cells. At the same time, we also found that although immune miRNAs in B naive cells, CD4 stem cells, Treg memory cells, etc. have higher ES, there is little difference in ES scores among immune cells. The ES values of immune miRNAs vary greatly among cancer types, which might be connected to tumor heterogeneity. Immune miRNAs play various functions in different types of tumors and show different expression patterns. On the other hand, immune miRNAs identified by target gene analysis also showed similar expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and tended to be highly expressed in immune cells, especially B cell naive and CD4 STIM cells. These results suggest that the immune-related miRNAs identified by these two methods are actively expressed in immune cell lines and actively contribute to the functioning of the immune. Finally, considering the multiple effects of miRNA on mRNA expression in immune pathways, the intersection miRNAs identified by the two methods were used as immune miRNAs for subsequent analysis (Fig. S2A-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eImmune-related miRNAs exhibit perturbed expression in cancer\u003c/h3\u003e\n\u003cp\u003eDysregulation of miRNA expression can cause changes in the expression of target genes, resulting in changes in corresponding functions. Therefore, we investigated whether the expression of immune miRNA is dysregulated in cancer and analyzed the differential expression of immune miRNA in different cancer types. First, we used t test to measure the differential expression of miRNAs between cancer samples and normal samples, in which miRNAs with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(FC)| \u0026gt;1 were differentially expressed miRNAs. A total of 952 differentially expressed miRNAs were identified in 32 cancer types. These results indicate that miRNA expression in tumors is more likely to be enhanced than in normal tissues to regulate tumor development. Subsequently, to assess whether immune-related miRNAs tend to be differentially expressed in tumors, we used Fisher exact test to compare whether immune-related miRNAs tend to be differentially expressed. We found that among the 10 cancer types, the differential expression ratio of immune-related miRNAs identified by the two methods was higher than that of all miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Compared with all miRNAs, the differential expression ratio of immune-related miRNAs obtained by target gene analysis was higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Immune-related miRNAs identified by target gene methods in PRAD had the highest specific difference. Among 225 immune-related miRNAs, the proportion of differentially expressed miRNAs was 34.22%. However, STAD showed the highest proportion difference of differentially expressed miRNAs identified by gene set enrichment analysis compared with target gene methods, and the proportion of differentially expressed 339 immune-related miRNAs in STAD was as high as 56.34%. These results indicate that immune miRNAs tend to perturbate their expression in tumors and participate in the regulation of immune pathways, thus participating in the occurrence and development of cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn order to synthesize the immune miRNA characteristics identified by the two methods, we first divided the 32 cancer types of TCGA into 11 systems according to clinical anatomical sites. Subsequently, the intersection number of miRNA regulators involved in immune pathways identified by the two methods was statistically analyzed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, miRNAs are mainly involved in the regulation of Antimicrobials, Antigen Processing and Presentation, Cytokine Receptors and TGFb Family Member Receptor pathways. We found the distribution of immune-related miRNA-pathway pairs identified in the same system is similar. For example, in COAD and PAAD in the Digestive system, miRNAs regulate 14 immune pathways, most of which are the same (13/14). There were also similar numbers of immune-regulating miRNAs in the two cancers, with 115 immune-related miRNAs in COAD and 111 in PAAD. This indicates that cancer types in the same cancer system have similar immune regulation patterns, while different cancer systems have specific immune regulation characteristics. In Digestive system, hsa-miR-671-5p had the highest immune regulatory capacity. In colon cancer, overexpression of hsa-miR-671-5p targeting TRIM67 significantly increased the proliferation, migration, and invasion of tumor-derived cells(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This indicates that our weight-based approach can effectively identify miRNAs that have immunoregulatory effects on tumors, and these miRNAs have stronger regulatory capabilities than other low-weighted miRNAs in their respective tissue systems, while they may have low weights and regulate fewer immune pathways in other systems, such as in THYM. hsa-miR-129-5p regulates three immune pathways, while only the interferon receptor pathway is regulated in the lung system. This suggests that key immune miRNAs of the cancer system are tissue-dependent.\u003c/p\u003e\n\u003ch3\u003eImmMir prioritizes cancer-related miRNAs\u003c/h3\u003e\n\u003cp\u003eStudy confirms miRNAs regulate more immune pathways in multiple cancers and miRNA is widely involved in cancer immune regulation in the development of cancer. In order to identify key miRNA regulators with extensive immune regulatory capabilities in the range of malignant tumors, we adopted a weight-based approach to screen such key immune miRNAs. First, we ranked each miRNA according to the number of regulatory pathways in each cancer type, and assigned a weight W to each miRNA in each cancer type according to the ranking. This weight can measure the ability of miRNA to regulate immune function in the corresponding cancer type, then average the number of pathways regulated by each miRNA in each cancer type, and reassign the weight Wmean according to the number of normal pathways, and use this weight to measure the ability of miRNA to regulate immune function in malignant tumors. We found that the 50 miRNAs with the highest weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) tended to regulate multiple pathways in cancer, among which the hsa-miR-301b-3p with the highest weight regulated 10 immune pathways in 15 cancer types. It has been reported that miR-301b-3p is up-regulated in breast cancer and down-regulated by targeting NR3C2, thus promoting tumor cell proliferation, migration and invasion(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). hsa-miR-301b-3p is also involved in tumor growth, metastasis and angiogenesis as an oncogene in many cancers, including hepatocellular carcinoma(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), lung cancer(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and ovarian cancer(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe noted that hsa-miR-130b-3p (Wmean\u0026thinsp;=\u0026thinsp;0.9928), which had a high average weight, was involved in the regulation of 11 immune pathways in 14 cancer types, and had a high rank among key immune miRNAs (rank\u0026thinsp;=\u0026thinsp;6). The role of hsa-miR-130b-3p in malignant tumors has been reported. For example, it is upregulated in colorectal cancer (CRC) and regulates its expression by targeting the negative direction of CHD9 (chromodomain helicase DNA binding protein 9), thus promoting the proliferation of colorectal cancer (CRC)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). hsa-miR-130b-3p is also involved in tumor metastasis and angiogenesis as an oncogene in other cancers, such as hepatocellular carcinoma(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), lung cancer(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), oral squamous cell carcinoma(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), gastric cancer(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), bladder cancer(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, hsa-miR-106b-5p, as a miRNA that has been frequently studied in the field of cancer, has a high ranking (rank\u0026thinsp;=\u0026thinsp;8) among key immune miRNAs. miR-106b-5p inhibits tumor growth, metastasis, and invasion by targeting VEGFA and PAK5 (p21 activated kinase 5)(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). VEGFA is a member of the PDGF/ VEGF growth factor family, which can directly participate in the expansion of Treg cells and inhibit the expression of CD8\u0026thinsp;+\u0026thinsp;CTL and PD-1 receptor, and is also a key factor in tumor angiogenesis. PAK5, a member of the PAK5-EGR1-MMP2 signaling pathway, is an important regulator of cell migration and invasion. These two miRNAs have also been found to be enriched in cytokine receptor pathways in a variety of cancers, and this result also validates their key immunoregulatory role in tumors to some extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C).\u003c/p\u003e\u003cp\u003eIn order to evaluate the ability of ImmMir to prioritize cancer-related miRNAs, the average rank distribution of miRNAs associated with immune pathways in each cancer was normalized and it was found that miRNAs ranked high in most cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. Moreover, the grades of cancer/disease-related miRNAs are known to be significantly higher than those of other miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), and they are significantly enriched in miRNAs in HMDD and MIR2DISEASE (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Taken together, these results suggest that integrating ImmMir results can help prioritize cancer-associated miRNAs to improve our understanding of their regulatory functions in cancer.\u003c/p\u003e\n\u003ch3\u003eCancer subtyping based on immune-related miRNAs\u003c/h3\u003e\n\u003cp\u003eIn addition to identifying the key miRNAs of cancer, cancer subtypes are key to improving personalized treatment. Therefore, we next investigated to what extent miRNAs identified by ImmMir can be applied to molecular cancer subtypes. If the tissues and organs of the reproductive system become cancerous, they often have relatively greater differences due to their special tumor microenvironment. Considering the complexity of the reproductive system, through consistent clustering of the expression profiles of 22 key miRNA regulatory factors in the reproductive system, it was found that the patients were divided into two subtypes, miRNA expression of subtype 1 was higher than that of subtype 2, and most UCEC patients were divided into subtype 1, while PRAD was divided into subtype 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e22 miRNAs were differentially expressed in all UCEC patients, and we found that miRNAs such as hsa-miR-130b-3p and hsa-miR-103-3p were differentially up-regulated in cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Several other miRNAs have also been shown to promote tumor cell proliferation and metastasis in reproductive system cancers. The inhibitory effect of miR-19a-5p on oncogene EZH2 can accelerate the proliferation, migration and invasion of tumor cells(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). miR-182-5p plays a carcinogenic role in breast cancer by inhibiting the expression of FOXO3a(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). miR-183-5p targets RGS2 to promote breast cancer development(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Survival analysis showed that cancer subtype 2 (p\u0026thinsp;=\u0026thinsp;1.904e-05) had a better prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In addition, clustering of miRNA regulators in reticuloendothelial and brain tissue can also distinguish several cancer subtypes, and these subtypes are significantly correlated with patient prognosis (Fig. S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eImmune evasion is one of the main mark of cancers. Tumor cells often have genetic mutations and are presented as neoantigens by the major histocompatibility complex protein (MHC) on their surfaces, and T cells can recognize neoantigens and target these tumor cells for destruction. If immunogenic neoantigens are present on the surface of cancer cells, it helps to identify cancer cells. Since neoantigens are the result of mutations, the greater the number of mutations, the greater the chance that some of the neoantigens presented by the MHC protein will be immunogenic and therefore able to recognize and eradicate cancer cells(\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). To this end, we obtained the TMB of samples from TCGA, standardized log10, and calculated the distribution of TMB of reproductive system subtypes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, subtype 1 has a higher TMB than subtype 2 (rank sum test, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16), which indicates that there may be more cancer-causing mutations associated with corresponding tumors in subtype 1, with more prominent tumor characteristics and easy recognition by T cells. We defined subtype 1 as TMB H and subtype 2 as TMB L, and TMB H may be sensitive to immune checkpoint inhibitors. At the level of immune cell infiltration, subtype 1 had a lower level of immune cell (except B cell) infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), and subtype 1 had a higher enrichment score in the immune mark gene set (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), indicating that a high level of immune cell infiltration of subtype 1 may have a stronger immune response. These results indicate that these key immune-related miRNAs are more inclined to oncogenes, and may inhibit the expression of immune-related genes through up-regulation, thus weakening the function of immune cells, and thus helping the proliferation and spread of tumor cells.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePotential drugs targeting immune-miRNAs\u003c/h2\u003e\u003cp\u003eSmall molecule chemotherapeutic drugs have been widely used in clinical anti-cancer therapy, but the therapeutic effect and outcome of these small molecule drugs are affected by serious side effects such as systemic toxicity(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The complexity of signaling pathways is another great challenge in cancer therapy(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). When a single signaling pathway is blocked, but cancer cells can continue to develop by activating another parallel signaling pathway(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). From this point of view, cancer can be effectively treated by silencing tumor-related genes, blocking tumor signaling pathways, and supplemencing insufficient regulators. The dysregulated expression of miRNA regulators is closely related to disease diagnosis, prognosis and treatment response. Therefore, we hope to start from the small molecule drugs targeting miRNA and understand the mechanism of their regulation of miRNA target genes, so as to combine with the existing immune checkpoint therapy or chemotherapy to kill or inhibit tumor cells more effectively\u003c/p\u003e\u003cp\u003eBased on the small molecule drug and miRNA relationship pairs obtained from SM2miR, miRNA-drug subnetworks related to key immune miRNAs were screened, and then fused with the miRNA-pathway relationship pairs of enrichment analysis results. By further adding the leadingedge genes of known oncogenes into the network to finally obtain the drug-miRNA-leadingedge gene-pathway network, we can find that hsa-miR-16-5p is targeted by up to 15 small molecule drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). These drugs regulate downstream targets by targeting miRNAs to achieve the purpose of treating or improving the disease. For example, Cisplatin can inhibit the proliferation of human neuroblastoma cells (SH-SY5Y) in vitro and in vivo by up-regulating the expression of miR-16 targeting BDNF(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). hsa-miR-16-5p is usually discussed as a tumor suppressor, and miR-16-5p is overexpressed in neuroblastoma, which inhibits tumor progression by directly targeting MYCN and reducing its expression level(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It is worth noting that miR-16-5p can inhibit the progression and invasion of osteosarcoma by targeting Smad3(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Smad3, as an important transporter in the transforming growth factor beta(TGFb) signaling pathway, can transport TGFb signals from the cell membrane to the nucleus, thereby enabling TGFb to bind to related factors. It regulates the expression of target genes, thereby controlling cell proliferation. Through the analysis of radiotherapy and chemotherapy responses of patients with two subtypes, we found that subtype1 had a higher response rate than subtype2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTaken together, these findings suggest that the identified key immune miRNAs can broadly regulate cancer progression through oncogenes and immune genes, and many small molecule drugs can target these miRNAs. These miRNA drug targets provide our window into miRNA expression intervention. These agents could also help provide an alternative approach when traditional miRNA-targeted therapies fail, as well as the possibility of drug combinations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is increasing evidence that miRNAs are important for complex tumor microenvironment systems. So far, however, only a few examples have been found. In this study, we report the integration of GSEA and target gene analysis to identify immune-related miRNA regulators in malignant tumors called ImmMir. In addition, key miRNA regulators with pan-cancer properties were identified in the context of multiple malignant tumors, and their association with cancer immunity was analyzed from different levels. Next, cancers were systematically grouped according to clinical anatomical sites, key miRNA regulators were dug up, and molecular typing of reproductive system cancers was further conducted according to the expression of key miRNAs. Two molecular subtypes were identified, one of which, TBM H subtype, had higher miRNA expression, lower immune infiltration value, and higher mutation load score. And worse prognosis, suggesting that miRNA plays an important role in tumor immunity research and can be used as a potential biological target, indirectly providing guidance for tumor immune-related research and clinical application.\u003c/p\u003e\u003cp\u003eWe systematically integrated RNA-seq expression profiles of miRNAs and mRNAs in TCGA malignant tumors, clinical information of patients and other data resources, and used GSEA and target gene to identify immune-related miRNA regulators in malignant tumors. Based on the number distribution, immune characteristics, and expression patterns of the two methods, It was revealed that both methods identified immune-related miRNAs widely involved in different cancer types, which tended to be differentially expressed in malignant tumors, and were significantly correlated with the level of immune infiltration, which tended to be cancer-related miRNAs. However, the regulation of specific immune pathways by immune miRNAs is cancer-specific, and specific miRNA-pathway tends to appear in only one type of cancer. We compared the distribution characteristics of the miRNAs identified by the two methods, and found that compared with the immune-related miRNA regulators identified by target gene analysis, gene enrichment analysis identified more immune-related miRNA regulators, but covered fewer cancer types. These results all indicate that immune-related miRNAs have extensive regulation of immunity in malignant tumors.\u003c/p\u003e\u003cp\u003eIn addition, in order to retain the characteristics of the immune miRNAs identified by both functional enrichment analysis and target gene analysis, we retained the intersection ImmMir of immune miRNA regulators identified by the two methods and identified key miRNA regulators in malignant tumors based on weights. The focus is on immune miRNAs that tend to regulate different pathways in multiple cancers. All key immune miRNAs tend to be differentially expressed, correlated with the level of immune infiltration, and tend to appear in immune cell lines, and are targeted by a variety of small molecule drugs. Functional enrichment analysis of key immune miRNAs showed that these miRNAs were enriched in a large number of functions, such as immune system development and p53-mediated DNA damage repair, among which hsa-miR-130b-3p and hsa-miR-106b-5p were two representative miRNAs. They have high weight and high functional network degree, and have been proved by many studies to be closely related to tumor immunity. We focused on key immune miRNAs in the reproductive system, and based on their expression, molecular typing of cancer samples in the reproductive system was performed, and one subtype, TBMH, was identified as having higher miRNA expression, lower immune infiltration value, higher mutation load score, and worse prognosis. These key miRNAs are risk factors for survival propensity and can be targeted by multiple drugs. These findings have important implications for precision cancer treatment and immunotherapy.\u003c/p\u003e\u003cp\u003eOur study not only analyzed the complexity of the cancer immune system, but also identified the key miRNAs that regulate the immunity of malignant tumors. Moreover, the classification of cancer systems based on the expression of key miRNAs is helpful to analyze the expression pattern of miRNA and immune regulation mechanism in the process of tumor development. It provides a potential target for personalized diagnosis and immunotherapy of malignant tumors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide miRNA and mRNA expression across cancer types\u003c/h2\u003e\u003cp\u003eOur analyses are mainly based on miRNA and miRNA expression datasets for 32 cancer types were downloaded from TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the R package \u0026ldquo;TCGAbiolinks\u0026rdquo;(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The fragments per kilobase of transcript per million (FPKM) mapped reads-based gene expression profile of miRNA and mRNA were obtained. Firstly, the miRBase ID of miRNAs was converted to the corresponding mature miRNA names utilizing the ID mapping file of miRBase(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Simultaneously, the Ensembl ID was transformed into gene symbols using the ID mapping file of Ensembl. Subsequently, we retained miRNA and mRNA entities only if they were expressed in at least 30% of the samples. For further normalization, instances where the expression value was 0 were replaced with the minimum value in the respective row. Finally, the FPKM values were log2-scaled, resulting in the generation of comprehensive expression profiles for both miRNA and mRNA. These refined expression profiles were then employed in subsequent analytical steps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of immune-related miRNA\u003c/h2\u003e\u003cp\u003eIn view of the influence of tumor purity on miRNA and mRNA expression, we first use the expression profile of mRNA to calculate the purity of the TCGA sample using the ESTIMATE(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.mdanderson.org/estimate/rpackage.html\u003c/span\u003e\u003cspan address=\"https://bioinformatics.mdanderson.org/estimate/rpackage.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For each cancer type, Intersection of tumor purity, miRNA expression profile, and mRNA expression profile sample were retained. Tumor purity is used as a covariate, and the partial correlation coefficient (PCC) between miRNA i and mRNA j is calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PCC(miRNA\\:i,\\:mRNA\\:j)=\\frac{{R}_{MG}-{R}_{MP}\\ast\\:{R}_{GP}}{\\sqrt{1-{R}_{MP}^{2}}\\ast\\:\\sqrt{1-{R}_{GP}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere R\u003csub\u003eMG\u003c/sub\u003e R\u003csub\u003eMP\u003c/sub\u003e R\u003csub\u003eGP\u003c/sub\u003e are the correlation coefficients between the expression of miRNA i and mRNA j, the expression of miRNA i and tumor purity, and the expression of mRNA j and tumor purity, respectively. We identified miRNA-mRNA pairs whose expression was significantly negatively correlated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r\u0026thinsp;\u0026lt;\u0026thinsp;0), furthermore, integrated immune pathway data and obtained miRNAs that regulate specific immune pathways in each cancer background by cumulative hypergeometric test(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Then calculate the RS value according to pcc.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:statistic=pcc\\ast\\:\\sqrt{\\frac{n-2-g\\ast\\:n}{1-{pcc}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:p=2\\ast\\:pnorm(-\\left|statistic\\right|)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:RS={-log}_{10}\\left(p\\right)\\ast\\:sign\\left(p\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere n is the number of sample intersections, g is 1, pnorm is the normal distribution value, sign is the positive or negative of p, and finally the RS value between miRNA and mRNA. Specifically, when pcc\u0026thinsp;=\u0026thinsp;INF, pnorm (-INF)\u0026thinsp;=\u0026thinsp;0. In order to prevent significant results from being excluded, when p\u0026thinsp;=\u0026thinsp;0, p\u0026thinsp;=\u0026thinsp;p\u0026thinsp;+\u0026thinsp;0.01.\u003c/p\u003e\u003cp\u003eIn order to identify immune-related miRNAs, we downloaded 17 immune-related pathways from ImmPort(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The RS value of miRNA is used as input, and the immune pathway enrichment analysis of miRNA by the fgsea package. Because mRNA should be negative regulation by miRNA, when the ES value\u0026thinsp;\u0026gt;\u0026thinsp;0, let pp\u0026thinsp;=\u0026thinsp;2*p-1 and when the ES value\u0026thinsp;\u0026lt;\u0026thinsp;0, let pp\u0026thinsp;=\u0026thinsp;1\u0026ndash;2*p. In the follow-up study, the significant positively regulated miRNA immune pathway pair is identified when the miRNA with pp\u0026thinsp;\u0026gt;\u0026thinsp;0.995 and padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 Immune pathway regulation pair.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDifferential expression of miRNAs\u003c/h2\u003e\u003cp\u003eIn the process of conducting the differential expression analysis, we initiated by excluding cancer types with fewer than 5 normal samples. Subsequently, differential expression analysis was executed using the t-test for the remaining 17 cancer types. To discern the magnitude of expression changes, the log2-fold change (log2FC) between the expression values of cancer samples and their corresponding normal samples was computed. Genes were deemed significantly differentially expressed if they exhibited a p-value less than 0.05 and |log2FC| greater than 1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003emiRNA target gene enrichment analysis\u003c/h2\u003e\u003cp\u003eTo validate the robustness of the leading-edge genes associated with immune miRNAs, we retrieved miRNA target gene data from miRTarBase(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirtarbase.mbc.nctu.edu.tw/php/index.php\u003c/span\u003e\u003cspan address=\"http://mirtarbase.mbc.nctu.edu.tw/php/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and mirDIP(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ophid.utoronto.ca/mirDIP/index.jsp\u003c/span\u003e\u003cspan address=\"http://ophid.utoronto.ca/mirDIP/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Ensembl gene IDs of the miRNA target genes were then converted into corresponding symbols using the Ensembl ID mapping file. Additionally, the miRBase IDs of the miRNAs were transformed into mature miRNA names through the miRBase ID mapping file. To assess the convergence between the leading-edge genes and the target genes of immune-related miRNAs, we conducted an overlap analysis utilizing the hypergeometric test. Simultaneously, non-immune-related miRNAs, characterized by non-significant FDR and pp values, underwent a similar analysis. This comprehensive approach allowed us to rigorously examine the consistency of the identified immune miRNA targets and ascertain their relevance in the context of immune regulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eValidation of immune cell line expression data\u003c/h2\u003e\u003cp\u003eIn order to verify whether miRNAs in immune cells and TCGA cancers tend to regulate the same immune pathways, we collected immune cell line data from three sources. We initially conducted a keyword search using \"Immune Cells\" in GEO. Subsequently, through manual curation of immune cell-related literature, we identified RNA-seq data specifically pertaining to pure immune cells. This dataset encompasses 28 types of immune cells and comprises a total of 184 samples. Furthermore, we obtained RNA-seq data from Valeria Ranzani's publication(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), which includes 14 types of immune cells and a total of 63 samples. We also downloaded miRNA and mRNA expression profile data for 13 immune cell types and 15 samples from the DICE database(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dice-database.org/\u003c/span\u003e\u003cspan address=\"https://dice-database.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We integrated immune cell data from three different sources and conducted log2 normalization for subsequent analysis. Next, we examined the expression patterns of tumor immune-related miRNAs identified through gene enrichment analysis and target gene recognition in each cancer type across various immune cell lineages. Within each cell lineage expression profile, we specifically considered the expression of 1546 miRNAs. The expression values for each sample were averaged to obtain miRNA expression profiles associated with immune cell lineages. Utilizing the R package GSVA, we performed single-sample gene set enrichment analysis (ssGSEA)(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) on the immune-related miRNAs identified by both methods. Ultimately, we obtained enrichment scores (ES values) representing the enrichment status between 32 tumor types and 15 immune cell lineages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImmune Infiltration Verification\u003c/h2\u003e\u003cp\u003eIn order to verify that immune-related miRNAs are more likely to appear in immune infiltrating tumors, the infiltration value of the 6 immune cells to the TCGA sample (LAML is a non-solid tumor without infiltration value) was downloaded from TIMER(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) (Tumor immune estimate Resource). At first for each miRNA and immune cell, correlation between the expression value of miRNA and the infiltration value of immune cell was calculated. And then in 31 cancer types, the overlap significance of immune-related and infiltration-related miRNAs was analyzed by fisher test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCancer miRNA enrichment analysis\u003c/h2\u003e\u003cp\u003eTo verify that immune-related miRNA tend to play a role in cancer, cancer-related miRNAs were downloaded from HMDD(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cuilab.cn/hmdd\u003c/span\u003e\u003cspan address=\"http://www.cuilab.cn/hmdd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and miR2disease(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mir2disease.org/\u003c/span\u003e\u003cspan address=\"http://www.mir2disease.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The miRBase ID and the irregular miRNA name are converted into mature miRNA names through the miRBase ID mapping file. And for every cancer, the immune-related miRNAs are first sorted by the number of regulatory immune pathways. Then the rank of each cancer is averaged, and sorting again. HMDD and miR2disease cancer-related miRNA, the average rank of immune-related miRNA as the input of cancer miRNA enrichment analysis (fgsea R package).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLiterature search verification\u003c/h2\u003e\u003cp\u003eIn order to verify whether the immune-related miRNAs are mentioned in the published literature. At first two keyword searches on all miRNA were perform by RISmed R package, the first time search using \u0026ldquo;miRNA name\u0026rdquo;, and the second time search using \u0026ldquo;miRNA name\u0026thinsp;+\u0026thinsp;immune\u0026rdquo;. Then, among the miRNAs mentioned in the literature in the first search, fisher exact test is used to detect the significance of immune-related miRNA that mentioned in the second keyword search.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eCancer subtype classification\u003c/h2\u003e\u003cp\u003eIn order to classify the TCGA cancer subtypes according to the developmental system (Hematopoietic, Brain, Digestive, Endocrine, Head and Neck, Lung, pleura, Reproductive, reticuloendothelial, Skin, Soft Tissue, Urinary). First, for the cancer types of the each system, the immune-related miRNAs in the TCGA data, the immune-related miRNAs in the immune cells and the differentially expressed miRNAs are intersected. And then the expression value of these miRNA cancer samples uses the ConsensusClusterPlus R package for subtype clustering (maxK\u0026thinsp;=\u0026thinsp;6, reps\u0026thinsp;=\u0026thinsp;500, pItem\u0026thinsp;=\u0026thinsp;0.8, pFeature\u0026thinsp;=\u0026thinsp;1, seed\u0026thinsp;=\u0026thinsp;123456, clusterAlg=\"km\", distance= \"euclidean\"). For the value of k, choose k\u0026thinsp;=\u0026thinsp;5 according to the relative change in area under CDF curve. We combined the cancer types of the reproductive system. First, we screened 7 cancer types belonging to the reproductive system, and reserved 3 cancer types in TCGA with normal samples greater than 5, including BRCA, PRAD and UCEC, so as to screen miRNAs with dysregulated expression. We then screened 50 key immune miRNA regulators of the reproductive system that were present in all three cancer expression profiles and differentially expressed in at least one cancer type, resulting in 22 miRNAs. After determining the subtype classification of each system, the survival of these subtypes, as well as the distribution of immune infiltration values, and the mutation load distribution of the sample were analyzed. The significant difference p-value is determined using the rank sum test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eValidation of miRNA drug targeting data\u003c/h2\u003e\u003cp\u003eThe drug targeting data of miRNAs is downloaded from SM2miR(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.hrbmu.edu.cn/SM2miR/\u003c/span\u003e\u003cspan address=\"http://bioinfo.hrbmu.edu.cn/SM2miR/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). First, immune-related miRNAs that regulate immune pathways in at least 10 cancers are selected. And then the sankey diagram of these miRNAs, the immune pathways regulated by these miRNAs, leading edge genes, and the drugs that regulate these miRNAs were visualized using the riverplot R package.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003cbr\u003eThis work was supported by the National Science and Technology Major Project [2022ZD0117700]; the National Natural Science Foundation of China [32300541 and 62231013]; Natural Science Foundation of Heilongjiang Province [LH2023C063]; the China Postdoctoral Science Foundation [2023T160177, 2021M693831]; the Heilongjiang Postdoctoral Foundation [LBH-Z20071].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.Q.Y and Y.Z contributed data analysis and paper writing. Z.H.C and Z.P.Q collected samples and generated data. J.X.Y and Z.H.C carried out data interpretations and helped data discussion. N.D contributed study design. G.H.W and Z.G.L contributed paper revisiting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were disclosed.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll the datasets analyzed in this study are publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJayaram MA, Phillips JJ. Role of the Microenvironment in Glioma Pathogenesis. Annu Rev Pathol. 2024;19:181-201.\u003c/li\u003e\n\u003cli\u003eAnderson NM, Simon MC. The tumor microenvironment. Curr Biol. 2020;30(16):R921-R5.\u003c/li\u003e\n\u003cli\u003eBarkley D, Moncada R, Pour M, Liberman DA, Dryg I, Werba G, et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet. 2022;54(8):1192-201.\u003c/li\u003e\n\u003cli\u003eOliveira G, Wu CJ. 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Bioinformatics. 2013;29(3):409-11.\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":"immune, miRNA, Pan cancer, pathway","lastPublishedDoi":"10.21203/rs.3.rs-7430586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7430586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroRNAs (miRNAs) play a pivotal role as post-transcriptional regulators in tumorigenesis, influencing immune pathways in various cancers. However, a systematic identification of potential miRNAs influencing immune pathway activity remains largely elusive. In this study, we presented a comprehensive analysis of miRNAs within 17 immune-related pathways across 32 different cancers. Leveraging GSEA-based and target gene-based computational methods, we identify potential miRNA regulons that are intricately associated with tumor immunity. These miRNAs exhibit a propensity to regulate immune pathways across multiple cancer types. Moreover, miRNA immune regulons manifest expression perturbations in cancer and display significant correlations with immune cell infiltrations. Our study reveals the role of immune-related miRNAs in immune cell development, differentiation, and tumor growth and metastasis dynamics. Furthermore, we optimized two key immunology miRNA regulons, exemplified by hsa-miR-130b-3p and hsa-miR-106b-5p, demonstrating their wide-ranging influence on immune function in tumors. These miRNAs emerged as potential targets for a variety of drugs, offering promise as adjuvant therapy alongside conventional radiotherapy and chemotherapy, as well as contributing to immunotherapeutic approaches. Additionally, our study identifies two molecular subtypes within reproductive system cancers, characterized by distinct tumor mutational burdens (TMB L and TMB H phenotypes). These subtypes exhibit disparities in immune cell infiltrations, checkpoint expression, and prognosis, shedding light on potential avenues for personalized treatment strategies. In summary, our research provides an extensive panorama of miRNA immunology regulons, enhancing our understanding of miRNA function. This knowledge holds significant implications for the development of targeted therapies in tumor immunology and the refinement of personalized treatment approaches.\u003c/p\u003e","manuscriptTitle":"Comprehensive miRNA profiles revealing cancer-immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 10:32:46","doi":"10.21203/rs.3.rs-7430586/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":"88ca489c-35ba-4afc-8774-9d37db371387","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54583151,"name":"Biological sciences/Cancer"},{"id":54583152,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":54583153,"name":"Biological sciences/Immunology"},{"id":54583154,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-05T21:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 10:32:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7430586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7430586","identity":"rs-7430586","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}