Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer

Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer Yujing Shi, Hui Chen, Zeyuan Liu, Gefenqiang Shen, Xinchen Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1813951/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cervical cancer (CC) is one of the most common gynecologic neoplasms. Hypoxia is an essential trigger for activating immunosuppressive activity and initiating malignant tumors. However, the determination of the role of immunity and hypoxia on the clinical outcome of CC patients remains unclear. Methods: The CC independent cohort were collected from TCGA database. Consensus cluster analysis was employed to determine a molecular subtype based on immune and hypoxia gene sets. Cox relevant analyses were utilized to set up a risk classifier for prognosis assessment. The underlying pathways of classifier genes were detected by GSEA. Moreover, we conducted CIBERSORT algorithm to mirror the immune status of CC samples. Results: We observed two cluster related to immune and hypoxia status and found the significant difference in outcome of patients between the two clusters. A total of 251 candidate genes were extracted from the two clusters and enrolled into Cox relevant analyses. Then, seven hub genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI and VEGFA) were selected to create an immune and hypoxia-based risk classifier (IHBRC). The IHBRC can precisely distinguish patient risk and estimate clinical outcomes. In addition, IHBRC was closely bound up with tumor associated pathways such as hypoxia, P53 signaling and TGF β signaling. IHBRC was also tightly associated with numerous types of immunocytes. Conclusion: This academic research revealed that IHBRC can be served as predictor for prognosis assessment and cancer treatment estimation in CC. hypoxia tumor microenvironment prognostic signature immunotherapy TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Cervical cancer (CC) is the fourth most frequently diagnosed cancer and the second mortal cancer in female population, which posts a serious health threat to women globally [ 1 ]. According to the GLOBOCAN 2020 database, there were 604127 new cases and 341831 new deaths from CC, and the death rate is 12.4 versus 5.2 per 100,000 people in transitioning and in transited countries, respectively [ 2 ]. Etiologically, accumulating evidence has implied that infection with high-risk human papillomavirus (HPV) is the primary factor for CC [ 3 ]. Up to 90% of cases are driven by high-risk HPV strains including 16, 18, 31, 33 and 35, with other low-risk HPV types generally produce benign cervical lesions [ 4 , 5 ]. Despite the promotion of HPV vaccine immunoprevention, many patients are diagnosed with advanced stage at their first diagnosis, making the exploration of early diagnosis biomarkers and effective prognostic model urgently needed [ 6 , 7 ]. Recently, tumor microenvironment (TME) is causing general interest in various cancer settings. TME is composed of multiple cells residing in cancers, including immune cells, fibroblasts, endothelial cells and mesenchymal cells [ 8 ]. These cells closely interact with each other and organize into distinct cellular communities [ 9 ]. Distinct immune cell response categories tumors into 3 types named “hot”, “altered” and “cold” tumors [ 10 ]. Accumulating evidence has identified the immunotherapy as a promising intervention for cancer patients [ 11 ]. By reprograming the immunosuppressive state in the “cold tumor” into an activated one, the usage of immune checkpoint inhibitors as well as some cell-specific compounds has achieved exciting clinic outcome in multiple cancers [ 12 , 13 ]. However, immunotherapy in CC remain largely unexplored. Hypoxia is one of common characteristics of tumors and is closely related to tumor progression and poor prognosis [ 14 , 15 ]. Cells respond to hypoxia environment by regulating various metabolism pathways, which subsequently causes deficient hypervascularization, enhanced tumor cell proliferation, and distant metastasis tendency [ 16 – 18 ]. Emerging evidence has validated the crosstalk between hypoxia and immunophenotype in tumors. For instance, HIF2α has been reported to exert its protective role in pancreatic ductal adenocarcinoma by improving immune responses [ 19 ]. Moreover, Ivraym et al. once reported that hypoxia condition elevated the tumor cell resistance to cytotoxic T lymphocytes mediated lysis, which is dependent on the upregulation of HIF1α and PD-1 expression [ 20 ]. Taken together, it is reasonable to speculate that novel approaches targeting alleviating hypoxia condition could augment the current outcome for CC patients. Most of the indicators proposed in previous studies to predict clinical outcomes of CC patients are limited to single genes, such as HPV, PTEN and FHIT [ 21 ]. However, using only a single biomarker to assess prognosis is greatly partial, as the mechanisms affecting the development of CC are extremely complex. Currently, prognostic signature consisting of multiple genes have been proven to present independent prognostic ability by several reports, which has also attracted the attention of scholars in the field of oncology [ 22 , 23 ]. Compared to the traditional TNM system, the prognostic model is capable of accurately predicting not only clinical outcomes, but also the patient's immune status and treatment benefits. The alteration of metabolic state and immunophenotype in tumors largely restrain the therapy response for CC patients, while the relevant study is still in very early stages. In our current research, we combined the immune-related genes (IRGs) and hypoxia-related genes (HRGs) to establish a prognostic signature with high accuracy for CC. In addition, immune cell infiltration analysis was performed in two risk groups of CC samples. Altogether, our exploration will help clarify the specific immune environment in different populations and provide new ideas and insights for the prevention and treatment of CC. Materials And Methods Data acquisition The TCGA-CSCC dataset containing gene expression and simple nucleotide variation was collected from TCGA website. And the clinical data of TCGA-CSCC dataset was obtained from cBioPortal website. Next, we combined the clinical traits of the two databases by patient ID. Moreover, we extracted IRGs from ImmPort database and collected HRGs from MSigDB website. Gene cluster analysis The consensus cluster algorithm was performed using the “ConsensusClusterPlus” package. To determine the optimal cluster score, we assessed the Delta area and cumulative distribution function (CDF). Next, we compared clinical outcome discrepancies between different subtypes by survival analysis. We also utilized differential analysis to screen differentially expressed genes (DEGs) between different subtypes for subsequent analysis. Development of a risk classifier All CC samples were randomly divided into training set and validation set. The DEGs from cluster analysis were first subject to univariate analysis. Then, we enrolled the potential genes with prognostic value in multivariate analysis. Finally, we created immune- and hypoxia-based risk classifier (IHBRC) according to regression coefficients of each model factors. The risk equation was as follows: \(risk factor={\sum }_{i=1}^{n}({Coef}_{i}\times {\text{E}\text{x}\text{p}}_{i})\) , Coef i was the coefficient of the classifier generated by Cox analyses, and Exp i was the expression level of each model genes. The patients were divided into high- and low-risk groups according to the median risk score. Survival analysis The differences in clinical outcome were detected between two risk groups by Kaplan-Meier analysis. ROC curves were plotted to test the reliability of IRBRC in assessing patients’ outcomes. Univariate and multivariate analyses were applied to confirm the independent value of IHBRC in CC. Gene set enrichment analysis (GSEA) The transcriptome data and risk groups information were enrolled into GSEA. Next, we selected the hallmars. All. v7. 5. symbols. gmt in the MSigDB database as the reference gene set. The default weighted enrichment method was applied for 1000 enrichment analysis. The gene sets with P < 0. 05 and FDR < 0. 25 were considered as significantly enriched gene sets. Immune infiltration analysis CIBERSORT is a powerful algorithm proposed by Newman et al. to mirror the infiltration status of immunocytes. Performing an immunocytes gene set including 547 genes, CIBERSORT was applied to determine 22 immunocyte types containing B cells (naive B cells and memory B cells), T cells (CD8 T cells, naïve CD4 T cells, resting memory CD4 T cells, activated memory CD4 T cells, follicular helper T cells), immunosuppressive cells (T cells regulatory (Tregs), M2 macrophages and eosinophils) as well as other cells (resting NK cells, activated NK cells, monocytes, macrophages, dendritic cells, mast cells, eosinophils and plasma cells). To detect the TME of CC cases, we conducted correlation analysis to analyze the relationship between risk score and 22 immunocytes types. Tumor mutation burden analysis We employed the mutation data of CC cases to compare the tumor mutation burden (TMB) in two subgroups. The TMB value was generated using following equation: \(TMB=\frac{total mutation}{total coverbased}\times {10}^{6}\) . Chemotherapy drug sensitivity analysis To estimate the predictive power of the IHBRC for chemotherapeutic drug efficacy, the half-maximal inhibitory concentration (IC50) was taken as an index to measure the drug sensitivity. The difference in the IC50 between two risk groups was compared by pRRophetic of R. Results Characterization of immune and hypoxia genes To discover the hub genes which could regulate both immunity and hypoxia process, we screened 31 overlapped genes by intersection of IRGs and HRGs lists (Fig. 1A). Then, we performed function analysis on these 31 genes and found that they were enriched in response to hypoxia, leukocyte migration and regulation of angiogenesis (Fig. 1B). Meanwhile, we created a PPI network to better clarify the interaction of 31 genes at protein level (Fig. 1C). Consensus cluster analysis A total of 31 hub genes were incorporated into cluster analysis. The results indicated that CDF value growth was flat when k = 2 and Delta area increased insignificantly at k > 3 (Fig. 2A,B). The fractal matrix showed the favorable intergroup difference and intragroup association, suggesting these pivot genes could categorize all CC samples into two subtypes (Cluster 1 (n=130) and Cluster 2 (n=174)). Therefore, the clustering stability was best for k = 2 (Fig. 2C). Survival analysis illustrated the significant difference in patient outcome between two clusters (Fig. 2D). PCA analysis uncovered the favorable distinction between the two clusters (Fig. 2E). Furthermore, 251 DEGs were collected from differential analysis between two clusters. Development of a risk classifier In the training set, we first determined 24 survival-associated indicators based on above 251 DEGs via univariate analysis (Fig. 3A). Then, the candidate genes were enrolled into LASSO regression to remove the over fitting genes (Fig. 3B,C). Finally, multivariate analysis was employed and seven hub genes were selected to develop an IHBRC (Table 1): risk score = (0.0131 × CCL20) + (0.0638 × CXCL2) + (0.2812 × ITGA5) + (0.0340 × PLOD2) + (0.0697 × PTGS2) + (0.0374 × TGFBI) + (0.1113 × VEGFA). In addition, Fig. 3D demonstrated the prognostic power of seven hub predictors. As suggested by Fig. 4A, high-risk group presented a dismal prognosis benefit in the training set. The AUC values of 1-, 3-, and 5-year survival were 0.845, 0.699, and 0.654, respectively (Fig. 4B). We measured the survival outcome of patients in both groups and found that patients' outcomes were dismal as the risk score elevated (Fig. 4C). Meanwhile, we confirmed the performance of IHBRC in the validation and the entire cohorts using the same analysis described above and obtained the same results for the trend (Fig. 4D-I). Table 1: Multivariate analysis of the seven model genes in cervical cancer. Gene Coefficient P-value CCL20 0.0131 0.007 CXCL2 0.0638 0.001 ITGA5 0.2812 0.001 PLOD2 0.0340 0.001 PTGS2 0.0697 0.008 TGFBI 0.0374 0.001 VEGFA 0.1113 0.001 Independent prognostic analysis To examine the independent value of IHBRC in terms of survival of CC cases, univariate and multivariate analyses were employed. In the training set, univariate analysis demonstrated that low risk score was remarkably correlated with favorable prognosis (Fig. 5A). Furthermore, multivariate analysis still revealed that low risk score was independently associated with favorable outcome of CC patients (Fig. 5B), which could serve as an independent prognostic factor for glioma. These were confirmed by the test and the entire sets (Fig. 5C-F). GSEA enrichment analysis To explore the distinction in molecular pathways between the two groups, we applied GSEA based on hallmarks gene sets. The results disclosed that hallmarks including angiogenesis, hypoxia, IL6/JAK/STAT3 signaling, MTORC1 signaling, P53 signaling, TGF β signaling were markedly enriched in high-risk group (Fig. 6). Immune infiltration analysis In order to mirror the immune status of two groups, we estimated enrichment value of different immunocytes. Fig. 7A illustrated the relationship between seven model biomarkers and immunocytes. As shown in Fig. 7B, risk score was negatively correlated with the infiltration level of memory B cells, naïve B cells, resting dendritic cells, macrophages M1 and CD8 T cells, while neutrophils were activated in IHBRC-high cohort. Analysis of immunotherapy and chemotherapy response Waterfall diagrams indicated the mutational differences in the 20 genes between the two groups. We observed that the IHBRC-high cohort had a higher PIK3CA mutation rate than the IHBRC-low group (31 vs. 20%, Fig. 8A,B). Given the importance of TMB in evaluating immunotherapy response for patients with CC, we observed IHBRC-high group had lower TMB value (Fig. 8C). In addition, high risk score was correlated with a lower IC50 of docetaxel, doxorubicin and gemcitabine (p < 0.05), suggesting that the IHBRC served as a favorable indicator for chemosensitivity (Fig. 8D-F). Construction of IHBRC-related regulatory network The reciprocal regulation of mRNA and miRNA is closely bound up with tumor development. Based on the starbase online tool, we identified the target miRNAs of seven model genes with high relevance scores (Fig. 9). Moreover, miRNA set enrichment analysis was performed to explore the function of the target miRNAs by TAM 2.0 tool. The results showed these miRNAs were mainly involved in cell aging, apoptosis, immune response, inflammation and regulation of Stem Cell (Supplementary Table 1). Discussion Antitumor effects of immune cells could be largely influenced by TME, including intercellular crosstalk between different cell types, chemokines concentrations, and metabolism environment, thus it is crucial to establish a comprehensive understanding on the genetic and population characteristics of TME. In our study, we categories CC patients into two distinct clusters, in which they have totally differed prognosis, based on the expression level of immune- and hypoxia- related genes. Our model is proven to have high efficacy and accuracy via performing a series of bioinformatics analyses, and may shed a light on the understanding of molecular mechanisms underlying CC. A total of hub seven genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI and VEGFA) were identified as risky indicators in our prognostic model, and the involvement of some genes in CC has been reported before. PTGS2, also named COX-2, is a crucial target to prevent progression in various cancer types [ 24 – 26 ]. Early in 2004, Kullarni et al reported that the COX-2 expression was elevated in CC samples compared to normal cervical tissue. A number of signaling including EGF and nuclear factor κB (NF-κB) pathway has been validated to mediate COX-2 expression in CC [ 27 , 28 ]. Moreover, the usage of COX-2 selective inhibitors selectively enhances radio responsiveness in CC cell line under both normoxic and hypoxic conditions [ 29 ]. VEGFA is considered to play a crucial role in physiological and pathological angiogenesis [ 30 ]. In stimulation of VEGFA, endothelial cells proliferate and migrate to form new vessels [ 31 ]. The cross talk between VEGF signaling and immune response has been recently. Briefly, VEGFA contribute to the polarization of macrophages into an M2 immunosuppressive phenotype [ 32 – 34 ]. In turn, these immunosuppressive cells can further produce proangiogenic factors including VEGFA and MMP9 [ 35 ]. The role of CXCL2 in CC has been intensively reported before. Zhang and his colleagues once revealed that CXCL2 may promote tumor proliferation and metastasis induced by the overexpression of A-kinase-interacting protein 1 (AKIP1) in CC [ 36 ]. In agreement with our result, Yang et al recently indicated that the expression level CXCL2 is strongly associated with lymph node metastasis and prognosis in CC patients [ 37 ]. Four other genes including ITGA5, CCL20, TGFBI, and PLOD2 were previously studied in various malignancies, while their involvement in CC remains largely unexplored, and more basic researches are needed to reveal their biological function in CC [ 38 – 40 ]. Molecular signaling were further analyzed in our research to unveil the mechanism underlying CC progression. In general, defective vasculatures and overweighing demands of oxygen contribute to the hypoxia environment in solid tumors [ 41 ]. HIFs induced by the hypoxic microenvironment play a central part in several aspects of tumor formation, especially in the regulation of tumor angiogenesis. HIF has a bidirectional regulatory effect on tumor angiogenesis. In vitro studies revealed that when HIF-1α activity was inhibited, it had different effects on the expression of pro-angiogenic factors: VEGF, angiogenin and TGFβ-1 expression were diminished, while IL-6 and MCP-1 were significantly increased. In vivo tests showed that RNA inhibition of HIF-1α also showed a decrease in VEGF expression and an increase in IL-8 expression. Consequently, when HIF-α is inhibited, one pro-angiogenic factor may be increased when another pro-angiogenic factor is inhibited, and as a result, there may still be an actual increase in tumor vascularization [ 42 , 43 ]. As a result, ATP production shift from oxidative phosphorylation to glycolysis, and the acidic microenvironments subsequently confer the alterations of gene expression and activation of multiple molecular pathways, accelerating the cancer progression [ 44 , 45 ]. The genetic alternations of mTOR protein have a significant role in tumorigenesis [ 46 , 47 ]. A number of molecules are involved in the modulation of mTOR signaling, and specific inhibitors show a good performance in prevention and treatment of various tumors including oral cancer, ovarian cancer, and lung carcinoma [ 48 – 50 ]. TP53, which encodes a sequence-specific DNA-binding transcription factor, is one of the most frequently mutated genes in cancers [ 51 ]. studies show that depletion of TP53 can remarkably increase the incidence of carcinogen-induced carcinogenesis and accelerate the tumor growth and invasiveness [ 52 ]. TGFB is a critical regulator of numerous biological processes in both normal and cancer cells [ 53 ]. Matthew and his team recently reviewed that in B-cell malignancies, targeting the TGFB axis should be considered a promising approach in the context of immunotherapy [ 54 ]. The IL-6/STAT3 pathway is a classic signaling that can induce enhanced EMT process in cancers [ 55 ]. Yuan et al. revealed the function role of IL-6/STAT3 pathway in promoting the malignant progression in oral squamous cell carcinoma patients and further research is urgently needed to establish a more applicable therapeutic strategy targeting STAT3 pathway [ 56 ]. Of note, the immune landscape results validated that the infiltration level of M1-like macrophage and anti-tumor CD8 + T cells is significantly low in high-risk group, which is associated with poor clinical outcome. It has been indicated that M1-like macrophage serve as a protective factor in tumor microenvironment by promoting anti-tumor response [ 57 , 58 ]. For instance, a recent study pointed out that irradiation in CC can bring a subtype shift from M2-like to the M1-like phenotype and eventually lead to an enhanced anti-tumor immune status [ 59 ]. Its well established that CD8 + T cells play key roles in the elimination of HPV in CC [ 60 ]. Previous studies have uncovered that the higher ratios of CD8 + to CD4 + T cells being closely related to improved survival [ 61 ]. On contrary, the infiltration of neutrophils is proven to be positively correlated with survival of CC patients in our model, which is consistent with the common view that neutrophils is regarded as the most important leukocytes involving in first line defense to tissue damage [ 62 – 64 ]. Compared to the classic discipline to divide tumor immunophenotype into three subtypes (hot, altered and cold), our finding compared the immune cell infiltration in high- and low-risk populations, may provide a more accurate model to guide the cellular based immunotherapy in CC. Conclusion In summary, we developed a favorable risk classifier according to immune and hypoxia molecular subtypes. Our proposed risk classifier can be served as predictor for prognosis assessment and cancer treatment estimation in CC. Abbreviations AUC: area under curve; DEGs: differentially expressed genes; CC: cervical cancer; IC50: half maximum inhibitor concentration; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas; TMB: Tumor mutation burden; TME: tumor microenvironment; GSEA: Gene Set Enrichment Analysis. Declarations Acknowledgements We appreciate the The Cancer Genome Atlas for the open data. Data availability statement The public datasets to support the results of this subject can be gained from TCGA (https://portal.gdc.cancer.gov/). Funding This work was supported by the National Natural Science Foundation of China (82003228 and 82102831), Natural Science Foundation of Jiangsu Province (BK20201080) and Research project of clinical medical science and technology development fund of Jiangsu University (JLY2021097). Authors' contributions XD and XS visualized the study and took part in the study design, and performance. YS, HC, ZL and GS conducted the manuscript writing and bioinformatics analysis. All authors read and approved the final manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Written informed consent for publication was obtained from all participants. Competing interests The authors declare that they have no competing interests. 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Schwitalla S, Ziegler PK, Horst D, Becker V, Kerle I, Begus-Nahrmann Y, Lechel A, Rudolph KL, Langer R, Slotta-Huspenina J et al : Loss of p53 in enterocytes generates an inflammatory microenvironment enabling invasion and lymph node metastasis of carcinogen-induced colorectal tumors . Cancer Cell 2013, 23 (1):93-106. Zhang S, Carlsen L, Hernandez Borrero L, Seyhan AA, Tian X, El-Deiry WS: Advanced Strategies for Therapeutic Targeting of Wild-Type and Mutant p53 in Cancer . Biomolecules 2022, 12 (4). Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, Kadel EE, III, Koeppen H, Astarita JL, Cubas R et al : TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells . Nature 2018, 554 (7693):544-548. Timmins MA, Ringshausen I: Transforming Growth Factor-Beta Orchestrates Tumour and Bystander Cells in B-Cell Non-Hodgkin Lymphoma . Cancers (Basel) 2022, 14 (7). Bednarczyk RB, Tuli NY, Hanly EK, Rahoma GB, Maniyar R, Mittelman A, Geliebter J, Tiwari RK: Macrophage inflammatory factors promote epithelial-mesenchymal transition in breast cancer . Oncotarget 2018, 9 (36):24272-24282. You Y, Tian Z, Du Z, Wu K, Xu G, Dai M, Wang Y, Xiao M: M1-like tumor-associated macrophages cascade a mesenchymal/stem-like phenotype of oral squamous cell carcinoma via the IL6/Stat3/THBS1 feedback loop . J Exp Clin Cancer Res 2022, 41 (1):10. Ley K, Laudanna C, Cybulsky MI, Nourshargh S: Getting to the site of inflammation: the leukocyte adhesion cascade updated . Nat Rev Immunol 2007, 7 (9):678-689. Kashfi K, Kannikal J, Nath N: Macrophage Reprogramming and Cancer Therapeutics: Role of iNOS-Derived NO . Cells 2021, 10 (11). Liew PX, Kubes P: The Neutrophil's Role During Health and Disease . Physiol Rev 2019, 99 (2):1223-1248. Otter SJ, Chatterjee J, Stewart AJ, Michael A: The Role of Biomarkers for the Prediction of Response to Checkpoint Immunotherapy and the Rationale for the Use of Checkpoint Immunotherapy in Cervical Cancer . Clin Oncol (R Coll Radiol) 2019, 31 (12):834-843. R SJ: The Immune Microenvironment in Human Papilloma Virus-Induced Cervical Lesions-Evidence for Estrogen as an Immunomodulator . Front Cell Infect Microbiol 2021, 11 :649815. Zhang Y, He S, Xu C, Jiang Y, Miao Q, Pu K: An Activatable Polymeric Nanoprobe for Fluorescence and Photoacoustic Imaging of Tumor-Associated Neutrophils in Cancer Immunotherapy . Angew Chem Int Ed Engl 2022. Wang H, Yung MMH, Ngan HYS, Chan KKL, Chan DW: The Impact of the Tumor Microenvironment on Macrophage Polarization in Cancer Metastatic Progression . Int J Mol Sci 2021, 22 (12). Ren J, Li L, Yu B, Xu E, Sun N, Li X, Xing Z, Han X, Cui Y, Wang X et al : Extracellular vesicles mediated proinflammatory macrophage phenotype induced by radiotherapy in cervical cancer . BMC Cancer 2022, 22 (1):88. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Supplementary Table 1: Potential function of the target miRNAs. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1813951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":118441266,"identity":"3c057474-b01d-44a5-bcc1-9276a6b01726","order_by":0,"name":"Yujing Shi","email":"","orcid":"","institution":"Jurong people's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Shi","suffix":""},{"id":118441267,"identity":"b099596b-03ac-4b99-80ee-0b2e81434713","order_by":1,"name":"Hui Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Chen","suffix":""},{"id":118441270,"identity":"22fb0ca8-6d6e-4d60-ad24-4810b7135e21","order_by":2,"name":"Zeyuan Liu","email":"","orcid":"","institution":"Nanjing Jiangning Hospital and the Affiliated Jiangning Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeyuan","middleName":"","lastName":"Liu","suffix":""},{"id":118441272,"identity":"4e147205-4dbc-4f4c-98e8-ad866cf133db","order_by":3,"name":"Gefenqiang Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gefenqiang","middleName":"","lastName":"Shen","suffix":""},{"id":118441274,"identity":"666090bb-ee03-442e-a030-0156845fbe5d","order_by":4,"name":"Xinchen Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinchen","middleName":"","lastName":"Sun","suffix":""},{"id":118441276,"identity":"65f9d0b9-a97c-4926-a128-c355f40b379c","order_by":5,"name":"Xiaoke Di","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3PMWrDMBSA4ScE9qLEq0NLcwUZQ+lg4qtIGDR56OgtCgJ5yQF8jBxBjkiy+AAeMnvqkG4eWigt3VqsZMugf3k8eN/wAHy+OyzHAAaAQYRRPV5otnKSRP2SRa3konkVhZNQ8zMY0O4kH8hlj6STnEhixko8Qc9lmlGDIbSH3SSxAWu3XZmihsuipOc5ECH6aYKNmemKq5hLW9IBQ0yeHQTJ9lNXax3zjXr5Xt0EGzvTJSOkVRiuIYkKmH3sRNKEG422VBSB65c86tL3t6pY5jYcYPzIVlFoj5Pkb8Ft5z6fz+f7ry/VEk/E8ONlywAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoke","middleName":"","lastName":"Di","suffix":""}],"badges":[],"createdAt":"2022-07-01 04:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1813951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1813951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23773023,"identity":"1a4e5998-4de9-4eb6-8bea-3f833059d4e1","added_by":"auto","created_at":"2022-07-12 16:35:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76052,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of Immune and Hypoxia Genes. (A) The Venn plot of overlapped genes. (B) GO function enrichment analysis. (C) The PPI network of the overlapped genes.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/409cc55d1bf59c85ac9612e2.png"},{"id":23773024,"identity":"cdd2392c-f665-4ae5-a121-0ea7d7786eb4","added_by":"auto","created_at":"2022-07-12 16:35:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81850,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus Clustering determined a molecular subtype related to immune and hypoxia. (A) The CDF score of consensus index. (B) Relative change of CDF curve. (C) Consensus matrix for k=2. (D) The Kaplan–Meier survival analysis. (E) Principal component analysis of the two clusters.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/4a48d3e1f2c35c7d9667278e.png"},{"id":23773026,"identity":"640b71de-bab0-426d-a80c-2351d592402c","added_by":"auto","created_at":"2022-07-12 16:35:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110625,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a risk classifier. (A) Univariate Cox regression analysis. (B,C) LASSO coefficients for risk classifier. (C) Consensus matrix for k=2. (D) The survival analysis of classifier genes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/7fcab684801339237d45b063.png"},{"id":23773028,"identity":"9cd0a446-c582-4a8a-9e39-19fb5f321205","added_by":"auto","created_at":"2022-07-12 16:35:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107639,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive value of the classifier. \u003c/p\u003e\u003cp\u003e(A) Survival curves of prognostic difference between two risk groups in the training set. (B) ROC curve of the assessment reliability of the classifier in the training set. (C) The distribution of risk score and survival status in the training set. (D–F). (G–I) The testing set and the entire set were used to confirm the predictive value of the classifier.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/24a9ad38105ca91a2c14c52c.png"},{"id":23773983,"identity":"eb6fc05c-68b8-4bd6-af16-dbd7cb06a4bc","added_by":"auto","created_at":"2022-07-12 16:40:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48547,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognosis analysis of the classifier.\u0026nbsp;(A–C) Univariate Cox regression analysis. (D–F) Multivariate Cox regression analysis.\u003c/p\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/0ce664d936a121aa823285fa.png"},{"id":23775320,"identity":"d6692187-deef-4b9c-a7cd-79d8aaaa655a","added_by":"auto","created_at":"2022-07-12 16:50:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":85535,"visible":true,"origin":"","legend":"\u003cp\u003eGene Set Enrichment Analysis. (A) Angiogenesis. (B) hypoxia. (C) IL6/JAK/STAT3 signaling. (D) MTORC1 signaling. (E) P53 signaling. (F) TGF β signaling.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/f73fd22cb048d76e4fee3b16.png"},{"id":23774980,"identity":"37402506-e5a7-4b6d-853f-ffc9131de309","added_by":"auto","created_at":"2022-07-12 16:45:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124970,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis. (A) The relationship between seven model biomarkers and immunocytes. (B) Correlation analysis of risk score and immunocytes (memory B cells, naïve B cells, resting dendritic cells, macrophages M1, CD8 T cells and neutrophils).\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/9c4e289e2cdcd53e563843a6.png"},{"id":23773984,"identity":"f78c0fe6-4d90-44aa-91f0-0cdbe580f278","added_by":"auto","created_at":"2022-07-12 16:40:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59614,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immunotherapy and chemotherapy response. (A,B) The top 20 mutated genes in the two groups. (C) The TMB in the two groups. (D–F) Chemotherapeutic response in the two groups.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/b0c76d1cf54a4e76d2db88e6.png"},{"id":23773986,"identity":"16c14609-1d35-492a-91c6-542dc1a5a1cf","added_by":"auto","created_at":"2022-07-12 16:40:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":440687,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of IHBRC-related regulatory network.\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/877aee835373b5f6fbb94b95.png"},{"id":23851382,"identity":"b646aa1d-a7bc-47e8-862c-1e83ad62b684","added_by":"auto","created_at":"2022-07-14 09:59:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3673800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/d7b3d7e9-806f-4567-a568-a6efc4fd9d30.pdf"},{"id":23773982,"identity":"3d5992e1-f90d-4eec-976d-154fc3038cda","added_by":"auto","created_at":"2022-07-12 16:40:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16400,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1: Potential function of the target miRNAs.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1813951/v1/0b759c6fb6025fd5ffb83a95.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is the fourth most frequently diagnosed cancer and the second mortal cancer in female population, which posts a serious health threat to women globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the GLOBOCAN 2020 database, there were 604127 new cases and 341831 new deaths from CC, and the death rate is 12.4 versus 5.2 per 100,000 people in transitioning and in transited countries, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Etiologically, accumulating evidence has implied that infection with high-risk human papillomavirus (HPV) is the primary factor for CC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Up to 90% of cases are driven by high-risk HPV strains including 16, 18, 31, 33 and 35, with other low-risk HPV types generally produce benign cervical lesions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the promotion of HPV vaccine immunoprevention, many patients are diagnosed with advanced stage at their first diagnosis, making the exploration of early diagnosis biomarkers and effective prognostic model urgently needed [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, tumor microenvironment (TME) is causing general interest in various cancer settings. TME is composed of multiple cells residing in cancers, including immune cells, fibroblasts, endothelial cells and mesenchymal cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These cells closely interact with each other and organize into distinct cellular communities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Distinct immune cell response categories tumors into 3 types named \u0026ldquo;hot\u0026rdquo;, \u0026ldquo;altered\u0026rdquo; and \u0026ldquo;cold\u0026rdquo; tumors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Accumulating evidence has identified the immunotherapy as a promising intervention for cancer patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By reprograming the immunosuppressive state in the \u0026ldquo;cold tumor\u0026rdquo; into an activated one, the usage of immune checkpoint inhibitors as well as some cell-specific compounds has achieved exciting clinic outcome in multiple cancers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, immunotherapy in CC remain largely unexplored.\u003c/p\u003e \u003cp\u003eHypoxia is one of common characteristics of tumors and is closely related to tumor progression and poor prognosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cells respond to hypoxia environment by regulating various metabolism pathways, which subsequently causes deficient hypervascularization, enhanced tumor cell proliferation, and distant metastasis tendency [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emerging evidence has validated the crosstalk between hypoxia and immunophenotype in tumors. For instance, HIF2α has been reported to exert its protective role in pancreatic ductal adenocarcinoma by improving immune responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, Ivraym et al. once reported that hypoxia condition elevated the tumor cell resistance to cytotoxic T lymphocytes mediated lysis, which is dependent on the upregulation of HIF1α and PD-1 expression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Taken together, it is reasonable to speculate that novel approaches targeting alleviating hypoxia condition could augment the current outcome for CC patients.\u003c/p\u003e \u003cp\u003eMost of the indicators proposed in previous studies to predict clinical outcomes of CC patients are limited to single genes, such as HPV, PTEN and FHIT [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, using only a single biomarker to assess prognosis is greatly partial, as the mechanisms affecting the development of CC are extremely complex. Currently, prognostic signature consisting of multiple genes have been proven to present independent prognostic ability by several reports, which has also attracted the attention of scholars in the field of oncology [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Compared to the traditional TNM system, the prognostic model is capable of accurately predicting not only clinical outcomes, but also the patient's immune status and treatment benefits.\u003c/p\u003e \u003cp\u003eThe alteration of metabolic state and immunophenotype in tumors largely restrain the therapy response for CC patients, while the relevant study is still in very early stages. In our current research, we combined the immune-related genes (IRGs) and hypoxia-related genes (HRGs) to establish a prognostic signature with high accuracy for CC. In addition, immune cell infiltration analysis was performed in two risk groups of CC samples. Altogether, our exploration will help clarify the specific immune environment in different populations and provide new ideas and insights for the prevention and treatment of CC.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCGA-CSCC dataset containing gene expression and simple nucleotide variation was collected from TCGA website. And the clinical data of TCGA-CSCC dataset was obtained from cBioPortal website. Next, we combined the clinical traits of the two databases by patient ID. Moreover, we extracted IRGs from ImmPort database and collected HRGs from MSigDB website.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene cluster analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe consensus cluster algorithm was performed using the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package. To determine the optimal cluster score, we assessed the Delta area and cumulative distribution function (CDF). Next, we compared clinical outcome discrepancies between different subtypes by survival analysis. We also utilized differential analysis to screen differentially expressed genes (DEGs) between different subtypes for subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of a risk classifier\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll CC samples were randomly divided into training set and validation set. The DEGs from cluster analysis were first subject to univariate analysis. Then, we enrolled the potential genes with prognostic value in multivariate analysis. Finally, we created immune- and hypoxia-based risk classifier (IHBRC) according to regression coefficients of each model factors. The risk equation was as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(risk factor={\\sum }_{i=1}^{n}({Coef}_{i}\\times {\\text{E}\\text{x}\\text{p}}_{i})\\)\u003c/span\u003e\u003c/span\u003e, Coef\u003csub\u003ei\u003c/sub\u003e was the coefficient of the classifier generated by Cox analyses, and Exp\u003csub\u003ei\u003c/sub\u003e was the expression level of each model genes. The patients were divided into high- and low-risk groups according to the median risk score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in clinical outcome were detected between two risk groups by Kaplan-Meier analysis. ROC curves were plotted to test the reliability of IRBRC in assessing patients\u0026rsquo; outcomes. Univariate and multivariate analyses were applied to confirm the independent value of IHBRC in CC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptome data and risk groups information were enrolled into GSEA. Next, we selected the hallmars. All. v7. 5. symbols. gmt in the MSigDB database as the reference gene set. The default weighted enrichment method was applied for 1000 enrichment analysis. The gene sets with P \u0026lt; 0. 05 and FDR \u0026lt; 0. 25 were considered as significantly enriched gene sets.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT is a powerful algorithm proposed by Newman et al. to mirror the infiltration status of immunocytes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePerforming an immunocytes gene set including 547 genes, CIBERSORT was applied to determine 22 immunocyte types containing B cells (naive B cells and memory B cells), T cells (CD8 T cells, na\u0026iuml;ve CD4 T cells, resting memory CD4 T cells, activated memory CD4 T cells, follicular helper T cells), immunosuppressive cells (T cells regulatory (Tregs), M2 macrophages and eosinophils) as well as other cells (resting NK cells, activated NK cells, monocytes, macrophages, dendritic cells, mast cells, eosinophils and plasma cells). To detect the TME of CC cases, we conducted correlation analysis to analyze the relationship between risk score and 22 immunocytes types. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the mutation data of CC cases to compare the tumor mutation burden (TMB) in two subgroups. The TMB value was generated using following equation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(TMB=\\frac{total mutation}{total coverbased}\\times {10}^{6}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy drug sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the predictive power of the IHBRC for chemotherapeutic drug efficacy, the half-maximal inhibitory concentration (IC50) was taken as an index to measure the drug sensitivity. The difference in the IC50 between two risk groups was compared by pRRophetic of R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacterization of immune and hypoxia genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo discover the hub genes which could regulate both immunity and hypoxia process, we screened 31 overlapped genes by intersection of IRGs and HRGs lists (Fig. 1A). Then, we performed function analysis on these 31 genes and found that they were enriched in response to hypoxia, leukocyte migration and regulation of angiogenesis (Fig. 1B). Meanwhile, we created a PPI network to better clarify the interaction of 31 genes at protein level (Fig. 1C). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsensus cluster analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 31 hub genes were incorporated into cluster analysis. The results indicated that CDF value growth was flat when \u003cem\u003ek\u003c/em\u003e = 2 and Delta area increased insignificantly at \u003cem\u003ek\u003c/em\u003e \u0026gt; 3 (Fig. 2A,B). The fractal matrix showed the favorable intergroup difference and intragroup association, suggesting these pivot genes could categorize all CC samples into two subtypes (Cluster 1 (n=130) and Cluster 2 (n=174)). Therefore, the clustering stability was best for \u003cem\u003ek\u003c/em\u003e = 2 (Fig. 2C). Survival analysis illustrated the significant difference in patient outcome between two clusters (Fig. 2D). PCA analysis uncovered the favorable distinction between the two clusters (Fig. 2E). Furthermore, 251 DEGs were collected from differential analysis between two clusters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of a risk classifier\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the training set, we first determined 24 survival-associated indicators based on above 251 DEGs via univariate analysis (Fig. 3A). Then, the candidate genes were enrolled into LASSO regression to remove the over fitting genes (Fig. 3B,C). Finally, multivariate analysis was employed and seven hub genes were selected to develop an IHBRC (Table 1): risk score = (0.0131 \u0026times; CCL20) + (0.0638 \u0026times; CXCL2) + (0.2812 \u0026times; ITGA5) + (0.0340 \u0026times; PLOD2) + (0.0697 \u0026times; PTGS2) + (0.0374 \u0026times; TGFBI) + (0.1113 \u0026times; VEGFA). In addition, Fig. 3D demonstrated the prognostic power of seven hub predictors.\u003c/p\u003e\n\u003cp\u003eAs suggested by Fig. 4A, high-risk group presented a dismal prognosis benefit in the training set. The AUC values of 1-, 3-, and 5-year survival were 0.845, 0.699, and 0.654, respectively (Fig. 4B). We measured the survival outcome of patients in both groups and found that patients\u0026apos; outcomes were dismal as the risk score elevated (Fig. 4C). Meanwhile, we confirmed the performance of IHBRC in the validation and the entire cohorts using the same analysis described above and obtained the same results for the trend (Fig. 4D-I). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Multivariate analysis of the seven model genes in cervical cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003eCCL20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.0638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003eITGA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.2812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003ePLOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.0340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.0697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003eTGFBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.0374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003e0.1113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.78688524590164%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent prognostic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the independent value of IHBRC in terms of survival of CC cases, univariate and multivariate analyses were employed. In the training set, univariate analysis demonstrated that low risk score was remarkably correlated with favorable prognosis (Fig. 5A). Furthermore, multivariate analysis still revealed that low risk score was independently associated with favorable outcome of CC patients (Fig. 5B), which could serve as an independent prognostic factor for glioma. These were confirmed by the test and the entire sets (Fig. 5C-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the distinction in molecular pathways between the two groups, we applied GSEA based on hallmarks gene sets. The results disclosed that hallmarks including angiogenesis, hypoxia, IL6/JAK/STAT3 signaling, MTORC1 signaling, P53 signaling, TGF \u0026beta; signaling were markedly enriched in high-risk group (Fig. 6). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to mirror the immune status of two groups, we estimated enrichment value of different immunocytes. Fig. 7A illustrated the relationship between seven model biomarkers and immunocytes. As shown in Fig. 7B, risk score was negatively correlated with the infiltration level of memory B cells, na\u0026iuml;ve B cells, resting dendritic cells, macrophages M1 and CD8 T cells, while neutrophils were activated in IHBRC-high cohort. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of immunotherapy and chemotherapy response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWaterfall diagrams indicated the mutational differences in the 20 genes between the two groups. We observed that the IHBRC-high cohort had a higher PIK3CA mutation rate than the IHBRC-low group (31 vs. 20%, Fig. 8A,B). Given the importance of TMB in evaluating immunotherapy response\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efor patients with CC, we observed IHBRC-high group had lower TMB value (Fig. 8C). In addition, high risk score was correlated with a lower IC50 of docetaxel, doxorubicin and gemcitabine (p \u0026lt; 0.05), suggesting that the IHBRC served as a favorable indicator for chemosensitivity (Fig. 8D-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of IHBRC-related regulatory network\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reciprocal regulation of mRNA and miRNA is closely bound up with tumor development. Based on the starbase online tool, we identified the target miRNAs of seven model genes with high relevance scores (Fig. 9). Moreover, miRNA set enrichment analysis was performed to explore the function of the target miRNAs by TAM 2.0 tool. The results showed these miRNAs were mainly involved in cell aging, apoptosis, immune response, inflammation and regulation of Stem Cell (Supplementary Table 1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAntitumor effects of immune cells could be largely influenced by TME, including intercellular crosstalk between different cell types, chemokines concentrations, and metabolism environment, thus it is crucial to establish a comprehensive understanding on the genetic and population characteristics of TME. In our study, we categories CC patients into two distinct clusters, in which they have totally differed prognosis, based on the expression level of immune- and hypoxia- related genes. Our model is proven to have high efficacy and accuracy via performing a series of bioinformatics analyses, and may shed a light on the understanding of molecular mechanisms underlying CC.\u003c/p\u003e \u003cp\u003eA total of hub seven genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI and VEGFA) were identified as risky indicators in our prognostic model, and the involvement of some genes in CC has been reported before. PTGS2, also named COX-2, is a crucial target to prevent progression in various cancer types [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Early in 2004, Kullarni et al reported that the COX-2 expression was elevated in CC samples compared to normal cervical tissue. A number of signaling including EGF and nuclear factor κB (NF-κB) pathway has been validated to mediate COX-2 expression in CC [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, the usage of COX-2 selective inhibitors selectively enhances radio responsiveness in CC cell line under both normoxic and hypoxic conditions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. VEGFA is considered to play a crucial role in physiological and pathological angiogenesis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In stimulation of VEGFA, endothelial cells proliferate and migrate to form new vessels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The cross talk between VEGF signaling and immune response has been recently. Briefly, VEGFA contribute to the polarization of macrophages into an M2 immunosuppressive phenotype [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In turn, these immunosuppressive cells can further produce proangiogenic factors including VEGFA and MMP9 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The role of CXCL2 in CC has been intensively reported before. Zhang and his colleagues once revealed that CXCL2 may promote tumor proliferation and metastasis induced by the overexpression of A-kinase-interacting protein 1 (AKIP1) in CC [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In agreement with our result, Yang et al recently indicated that the expression level CXCL2 is strongly associated with lymph node metastasis and prognosis in CC patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Four other genes including ITGA5, CCL20, TGFBI, and PLOD2 were previously studied in various malignancies, while their involvement in CC remains largely unexplored, and more basic researches are needed to reveal their biological function in CC [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular signaling were further analyzed in our research to unveil the mechanism underlying CC progression. In general, defective vasculatures and overweighing demands of oxygen contribute to the hypoxia environment in solid tumors [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. HIFs induced by the hypoxic microenvironment play a central part in several aspects of tumor formation, especially in the regulation of tumor angiogenesis. HIF has a bidirectional regulatory effect on tumor angiogenesis. In vitro studies revealed that when HIF-1α activity was inhibited, it had different effects on the expression of pro-angiogenic factors: VEGF, angiogenin and TGFβ-1 expression were diminished, while IL-6 and MCP-1 were significantly increased. In vivo tests showed that RNA inhibition of HIF-1α also showed a decrease in VEGF expression and an increase in IL-8 expression. Consequently, when HIF-α is inhibited, one pro-angiogenic factor may be increased when another pro-angiogenic factor is inhibited, and as a result, there may still be an actual increase in tumor vascularization [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, ATP production shift from oxidative phosphorylation to glycolysis, and the acidic microenvironments subsequently confer the alterations of gene expression and activation of multiple molecular pathways, accelerating the cancer progression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The genetic alternations of mTOR protein have a significant role in tumorigenesis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A number of molecules are involved in the modulation of mTOR signaling, and specific inhibitors show a good performance in prevention and treatment of various tumors including oral cancer, ovarian cancer, and lung carcinoma [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. TP53, which encodes a sequence-specific DNA-binding transcription factor, is one of the most frequently mutated genes in cancers [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. studies show that depletion of TP53 can remarkably increase the incidence of carcinogen-induced carcinogenesis and accelerate the tumor growth and invasiveness [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. TGFB is a critical regulator of numerous biological processes in both normal and cancer cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Matthew and his team recently reviewed that in B-cell malignancies, targeting the TGFB axis should be considered a promising approach in the context of immunotherapy [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The IL-6/STAT3 pathway is a classic signaling that can induce enhanced EMT process in cancers [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Yuan et al. revealed the function role of IL-6/STAT3 pathway in promoting the malignant progression in oral squamous cell carcinoma patients and further research is urgently needed to establish a more applicable therapeutic strategy targeting STAT3 pathway [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf note, the immune landscape results validated that the infiltration level of M1-like macrophage and anti-tumor CD8\u0026thinsp;+\u0026thinsp;T cells is significantly low in high-risk group, which is associated with poor clinical outcome. It has been indicated that M1-like macrophage serve as a protective factor in tumor microenvironment by promoting anti-tumor response [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For instance, a recent study pointed out that irradiation in CC can bring a subtype shift from M2-like to the M1-like phenotype and eventually lead to an enhanced anti-tumor immune status [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Its well established that CD8\u0026thinsp;+\u0026thinsp;T cells play key roles in the elimination of HPV in CC [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Previous studies have uncovered that the higher ratios of CD8\u0026thinsp;+\u0026thinsp;to CD4\u0026thinsp;+\u0026thinsp;T cells being closely related to improved survival [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. On contrary, the infiltration of neutrophils is proven to be positively correlated with survival of CC patients in our model, which is consistent with the common view that neutrophils is regarded as the most important leukocytes involving in first line defense to tissue damage [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Compared to the classic discipline to divide tumor immunophenotype into three subtypes (hot, altered and cold), our finding compared the immune cell infiltration in high- and low-risk populations, may provide a more accurate model to guide the cellular based immunotherapy in CC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we developed a favorable risk classifier according to immune and hypoxia molecular subtypes. Our proposed risk classifier can be served as predictor for prognosis assessment and cancer treatment estimation in CC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: area under curve; DEGs: differentially expressed genes; CC: cervical cancer; IC50: half maximum inhibitor concentration; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas; TMB: Tumor mutation burden; TME: tumor microenvironment; GSEA: Gene Set Enrichment Analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the The Cancer Genome Atlas for the open data. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe public datasets to support the results of this subject can be gained from TCGA (https://portal.gdc.cancer.gov/). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82003228 and 82102831), Natural Science Foundation of Jiangsu Province (BK20201080) and Research project of clinical medical science and technology development fund of Jiangsu University (JLY2021097).\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXD and XS visualized the study and took part in the study design, and performance. YS, HC, ZL and GS conducted the manuscript writing and bioinformatics analysis. All authors read and approved the final manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBouvard V, Wentzensen N, Mackie A, Berkhof J, Brotherton J, Giorgi-Rossi P, Kupets R, Smith R, Arrossi S, Bendahhou K\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe IARC Perspective on Cervical Cancer Screening\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2021, 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\u003cstrong\u003e22\u003c/strong\u003e(1):88.\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":"hypoxia, tumor microenvironment, prognostic signature, immunotherapy, TCGA","lastPublishedDoi":"10.21203/rs.3.rs-1813951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1813951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cervical cancer (CC) is one of the most common gynecologic neoplasms. Hypoxia is an essential trigger for activating immunosuppressive activity and initiating malignant tumors. However, the determination of the role of immunity and hypoxia on the clinical outcome of CC patients remains unclear.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The CC independent cohort were collected from TCGA database. Consensus cluster analysis was employed to determine a molecular subtype based on immune and hypoxia gene sets. Cox relevant analyses were utilized to set up a risk classifier for prognosis assessment. The underlying pathways of classifier genes were detected by GSEA. Moreover, we conducted CIBERSORT algorithm to mirror the immune status of CC samples.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe\u003cstrong\u003e \u003c/strong\u003eobserved two cluster related to immune and hypoxia status and found the significant difference in outcome of patients between the two clusters. A total of 251 candidate genes were extracted from the two clusters and enrolled into Cox relevant analyses. Then, seven hub genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI and VEGFA) were selected to create an immune and hypoxia-based risk classifier (IHBRC). The IHBRC can precisely distinguish patient risk and estimate clinical outcomes. In addition, IHBRC was closely bound up with tumor associated pathways such as hypoxia, P53 signaling and TGF β signaling. IHBRC was also tightly associated with numerous types of immunocytes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This academic research revealed that IHBRC can be served as predictor for prognosis assessment and cancer treatment estimation in CC.\u003c/p\u003e","manuscriptTitle":"Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-12 16:35:35","doi":"10.21203/rs.3.rs-1813951/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":"ae9fc577-153c-4058-bf66-bfc38df94e1c","owner":[],"postedDate":"July 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-14T09:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-12 16:35:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1813951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1813951","identity":"rs-1813951","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}