Screening of tumor microenviroment immune-related lncRNA related to the prognosis of cervical cancer

Screening of tumor microenviroment immune-related lncRNA related to the prognosis of 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Screening of tumor microenviroment immune-related lncRNA related to the prognosis of cervical cancer Lan Zhang, Yang Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1460885/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) represents a major gynecologic health problem, accounting for the fourth most widespread cancer in women. A factor that may contribute to the occurrence and development of CC is involving disorders of long noncoding RNAs (lncRNAs). The current study aimed to isolate immune-related lncRNA signatures capable of predicting CC prognosis and identify targets for immunotherapy. Methods: RNA-seq data and clinical characteristic information of CC were acquired from The Cancer Genome Atlas (TCGA) database and immune-related genes were downloaded from ImmPort Shared Data. Prognostic genes were screened via using univariate, LASSO, and multivariate Cox regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess the prognostic signature. The association between risk score and immune cell infiltration or antiviral genes was evaluated by CIBERSORT and Pearson correlation analysis. Results: In this study, we constructed a prognostic risk signature model comprising 20 immune-related lncRNAs. With this model, patients in the high-risk group showed a poorer prognosis than those in the low-risk group. Results from univariate and multivariate Cox regression analyses demonstrated that this risk model was an independent prognostic factor. According to CIBERSORT algorithm results, we found different tumor microenviroment immune cell infiltration levels in the low- and high-risk groups and antiviral genes were closely associated with 20 immune-related lncRNAs, while further analysis showed that lower expressions of these genes were present in the high-risk group. Conclusions: Our study shows immune-related lncRNAs can serve as effective agents for the prognosis of CC and offer the potential for immunotherapy targets. Cervical cancer Immune gene LncRNA Immune microenviroment Antivirus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Cervical cancer (CC) is the fourth most common type of malignant tumors, with a high incidence and mortality rate among women [ 1 ]. Survey results reveal that an estimated 266,000 women die annually from CC, and this number is anticipated to double by 2035 [ 2 ], with of 90% of these deaths occurring in developing countries. As one example, in China, the incidence and mortality rates of CC are showing a significant increase, especially in young women [ 3 ]. Persistent high-risk human papillomavirus (HPV) is considered a leading factor involved with the development and progression of CC [ 4 ]. At present, treatments for CC include surgery, radiotherapy, chemotherapy, and more recently use of potential targeted and biological therapies based on specific molecular features [ 5 ]. Apart from these, the use of immunotherapy targeted production of HPV antigens or adoptive T-cell transfer therapy offer a novel and promising opportunity for recurrent or metastatic patients. However, even with these advances in CC treatment, the prognosis remains rather dismal, with 5-year overall survival (OS) rates for stage Ⅱ patients being only 65%, and this percent that gradually drops to 40% for more severe patients [ 6 ]. Given these current conditions, there is an urgent need to identify the prognostic biomarkers which can promptly and accurately predict the clinical outcomes and provide personalized treatment regimens. Long non-coding RNAs (lncRNAs), a type of non-coding RNA with a sequence of over 200 nucleotides, have been shown to exhibit multiple biological activities. Emerging studies have revealed that lncRNAs participate in the regulation of chromatin remodeling, levels of transcriptional and post-transcriptional activity, as well as splicing and epigenetics [ 7 ]. Disorders in lncRNAs can lead to increase in the occurrence of cancer, and are related to tumor immunity, immune cell migration and infiltration, antigen release and presentation, as well as immunotherapy [ 8 , 9 ]. It has been reported that immune-related lncRNAs could be considered as prognostic biomarkers for melanoma, breast cancer, bladder cancer, hepatocellular carcinoma, lung adenocarcinoma, and renal cell carcinoma, however, related research on immune-related lncRNA signatures in CC remains lacking [ 10 – 15 ]. With regard to immune-related lncRNA signatures in CC, previous studies have focused on a single abnormal expression of lncRNA, such as XLOC_006390, as being involved in multiple facets to facilitate CC tumorigenesis and metastasis [ 16 ]. However, joint multiple immune-related lncRNAs in CC have not yet been investigated. Therefore, considering the predictive value of multiple immune-related lncRNAs as a promising prognostic biomarkers, we conducted a signature model comprising 20 immune-related lncRNAs by univariate and LASSO Cox regression analysis as related to CC. In addition, samples were further divided into low- and high-risk groups as based on the median risk score. Finally, the proportion of 22 immune cells via CIBERSORT algorithms were calculated as a means to assess the associations between risk scores and differential proportions of immune cells or antiviral genes. Methods Patients and datasets The genomic data and corresponding clinical information were acquired from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/repository ). In total, 254 CC samples and 3 adjacent normal tissue samples were included in this research. The detailed clinical data including age, survival stage, survival time, T stage, lymph node status and metastasis at the time of diagnosis were recorded (Table 1 ). Patients with a survival time less than 30 days were not included in the study. Table 1 Clinical characteristics of cervical cancer patients Clinical Characteristics Total % Age 254 47.96 (20–88) Grade Ⅰ 16 6.30 Ⅱ 112 44.09 Ш 100 39.37 Ⅳ 1 0.39 GX 23 9.06 Unknow 2 0.79 T classification T1 118 46.46 T2 58 22.83 T3 16 6.30 T4 9 3.54 Tis 1 0.39 TX 15 5.91 Unknow 37 14.57 M classification M0 98 38.58 M1 10 3.94 MX 107 42.13 Unknow 39 15.35 N classification N0 109 42.91 N1 50 19.69 NX 58 22.83 Unknow 37 14.57 Survival status Survival 190 74.80 Death 64 25.20 Distinction of immune-related lncRNAs Immune-related genes (IRGs) were downloaded from the Immport Shared Data. Pearson’s correlation analyse was use to differentiate between expressed IRGs and lncRNAs to identify immune-related lncRNAs in CC samples (correlation coefficient > 0.6, p < .001)[ 13 ]. Finally, 286 immune-related lncRNAs were identified by constructing an immuno-lncRNAs co-expression network. Expression levels of these immune-related lncRNAs were then applied to further build the prognostic model. Construction and validation of the immune-related lncRNAs signatures associated with prognosis According to univariate Cox regression analysis, the independent prognostic predictors among immune-related lncRNAs were selected based on the “survival” package. LASSO analysis and the stepwise Cox proportional hazards regression model were used to construct the immune-related lncRNA prognostic model [ 13 ]. The formula for calculating the risk scores was: ∑ɨcoefficient (lncRNAɨ)×expression (lncRNAɨ). As based on the median risk score, patients were divided into either high- or low-risk groups. Immune and stromal scores Immune and stromal scores were calculated as based on the “ESTIMATE” package. Potential regulatory pathways analysis Gene set enrichment analysis (GSEA) was used to quantify normalized enrichment scores between the low- and high-risk score groups [ 17 ]. Evaluation of the immune-related lncRNAs signatures as independent prognostic factors in CC Both uni- and multi-variate Cox regression analysis were performed to assess the clinical information, including risk score, age, and stage of disease [ 18 ]. Comparisons of the 22 immune cell subtypes in the two risk groups The CIBERSORT package was applied to estimate the proportion of 22 immune cell subtypes in the two risk groups as based on the expression profiles [ 13 ]. The number of permutations was set at 1000. Samples with a p < 0.05 in the CIBERSORT analysis were used for further analysis. Associations between risk scores and immune cell subtypes were assessed with use of Pearson’s correlation. Statistical analysis All statistical analyses were performed using R software (version 4.1.1, https://www.r-project.org/ ). A p-value of < 0.05 was regarded as statistically significant. Results Identification of immune-related lncRNAs A total of 14086 lncRNAs were identified from the TCGA database, and 2579 IRGs from the Immport database were downloaded. The immune-related lncRNAs were identified by constructing a co-expression network of immunogenic genes and lncRNAs, with a final number of 286 immune-related lncRNAs being extracted (Fig. 1 a). Construction and validation of immune-related lncRNAs signatures Univariate Cox regression analysis was used to determine whether any associations were present between the 286 immune-related lncRNAs and OS of CC samples. From this analysis, we identified 41 survival associated immune-related lncRNAs (Fig. 1 b). Then, 20 immune-related lncRNAs were utilized to build the prognostic model by stepwise LASSO regression analysis (Fig. 1 c-d). Risk score of each patient was calculated according to the expression levels of the 20 lncRNAs and their corresponding coefficients (Fig. 1 e). Risk score= (-0.1518*AC100847.1) + (-0.1755*LINC01943) + (0.8935*AP001528.1) + (0.0161*AP001527.2) + (-0.1930*AP001318.2) + (-0.0715*AC008035.1)+ (-0.2153*AC108134.3) + (0.5749*ac007998.3) + (0.2186*ac008429.1) + (0.0183*miat) + (0.0780*AC008687.3) + (-0.1504*AC243829.4) + (0.0147*MEG3) + (0.0179 *AC002401.4) + (-0.2278*AC009097.2) + (-0.0865*AL109811.2) + (-0.5642*AC007728.2) + (-0.1477*AC008124.1) + (-0.0017*USP30-AS1) + (0.0875*DNM3OS). Based on the median risk score as a cutoff, patients were further divided into either high- or low-risk groups, with each patient’s survival outcome and lncRNA expression levels presented in Fig. 2 a. Results from Kaplan-Meier (K-M) survival curves indicated that the OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (Fig. 2 b). In addition, areas under the receiver-operating characteristic curves (ROC) were 0.801, 0.800, and 0.789 at 1, 3, and 5 years post diagnosis, respectively, indicating that these 20 immune-related lncRNAs exhibited a reliable capacity for predicting the OS of CC (Fig. 2 c). Prognostic analysis of risk models in different subgroups of CC patients To further clarify the role of these 20 immune-related lncRNAs signatures in CC progression, data obtained between the signature and clinical features were subjected to correlational analysis. It was found that elderly patients (age > 50), high Grade (3/4), and a poor pathological stage were significantly correlated with a high risk score, while low risk scores were significantly associated with a good prognosis. These findings provide support for the accuracy of this risk models (Fig. 3 ). Enrichment Analysis Based on Risk Score We investigated the different functions of immune-related lncRNAs in high- and low-risk groups. Figure 4 contained the top 10 most highly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) items within the high risk group as compared with that of the low risk group. Among the top 10 KEGG pathways, ECM receptor interaction and focal adhesion were significantly related to the progression of CC. The top 10 most highly enriched molecular functions, cellular components and biological processes included oxidative phosphorylation, oxidoreductase activity, and other energy metabolism-related activities, all of which were significant factors linked to the tumorigenesis and progression of CC. Identification of Independent Prognostic Factors Both uni- and multi-variate Cox regression analysis were performed to investigate the independence of the prognostic signatures. Results showed that both risk score and pathological N stage were related to the prognosis of CC (Fig. 5 ). Taken together, these results illustrated that the risk score value served as an independent factor for the prognosis of CC. Correlation Between Risk Models and Immune Status The patients were divided into two groups (high-risk and low-risk) based on the median risk score. Clinical information recorded on these patients included age, immune score, grade of disease, tumor invasion (T), lymph node (N), and metastasis (M). Differences observed between the low- versus high-risk groups tended to be in pathological T stage and immune score (Fig. 6 a). Further analysis revealed that the high-risk group had lower ESTIMATE scores but no difference of stromal score were found between the two groups (Fig. 6 b-d). Given the above results, we further analyzed the immune components and relationship between the signature and immune cell subtype infiltration of CC. The infiltration of 22 types of immune cells in the low- and high-risk groups were determined (Fig. 7 a). The results demonstrated that T cells CD8, T cells follicular helper, T cells regulatory, as well as mast cells resting were negatively correlated with risk scores, while macrophages M0, dendritic cell activated, mast cells activated, along with neutrophils were significantly increased in the high-risk group (Fig. 7 b). These results suggested that the signature was related to the immune states of CC, while high risk scores were associated with lower immune cell compositions in the CC microenvironment. Correlation Between Differential Antiviral Genes and Immune-related LncRNAs in the Two Groups Persistent infections with high-risk HPVs are the major causative factor for the development of CC and it has been reported that HPV infects approxiamately 100 million women around the world [ 19 ]. We analyzed the association between the 20 immune-related lncRNAs and antiviral genes, with the results that antiviral genes, including APOBEC3G, PML, MX1, TLR3, TRAIL , and XAF1 were all correlated with at least 6 of the 20 immune-related lncRNAs. In addition, compared with that of the low-risk group, the high-risk group showed lower expressions of antiviral genes (Fig. 8 ). These results further demonstrated that these 20 lncRNAs signatures affected the existence of HPV and progression of CC. Discussion CC is one of the most prevalent cancers, producing malignant tumors of genital tract, and a high mortality rate among females. Accordingly, this condition represents a prominent public health issue [ 20 ]. HPV infection is considered an indispensable factor involved with the development and progression of CC. Findings from epidemiological studies indicate that 80% of sexually active women have at least one opportunity HPV infection within their lifetime [ 21 ]. As most cases of high-risk HPV experience a self-limiting clinical course of approximately 1–2 years, leaving few cases progressing to precancer or cancer [ 22 ]. It should be noted that although prophylactic vaccines against HPV have shown promising results in recent years, the implementation of an universal HPV vaccination program is not realistic in developing countries. The 5-year survival rate of patients diagnosed with stage ⅡB decreases from 50–60% to 30–40% in stage ШB [ 23 ], therefore, a clear need exists for the development of an effective prognostic model that offers novel therapeutic targets for CC. Importantly, we found that this risk predication model can not only judge the prognosis, but also provide a better index of the immune status and antiviral ability of these CC patients. Emerging studies, which demonstrate the existence of viral antigens in CC, suggest that immunotherapy can serve as a promising treatment option. ADXS11-011, a live attenuated bioengineered molecule, could promote the differentiation of cytotoxic T-lymphocytes to kill cancer cells by targeting HPV transformed cells and results from a phase Ⅱ clinical trial study have demonstrated that ADXS11-011 achieved a 36% rate of 12-month survival in recurrent or persistent CC [ 24 ]. Additional evidence from a comprehensive serial study have demonstrated that anti-PD1/PD-L1 agents produce an overall response rate of 13.3%-26% [ 5 ]. And adoptive T-cell transfer therapy targeting E7 T-cell receptors has emerged as a promising therapy that could provide durable responses in a number of patients with advanced CC [ 25 ]. Importantly, immunotherapy has proved to exert a vital role in the treatment of CC and cell immune responses are potential factors that can influence tumor progression and prognosis [ 26 , 27 ]. Related studies have provided evidence that dendritic cells may play an immunosuppressive role thereby enabling a host tolerance to HPV antigens [ 28 ]. Moreover, CD8 T cells and resting mast cells were found to be associated with an overall better survival rate, while activated mast cells are related with poorer survival [ 29 ]. In our current study, we also found that dendritic cell activated and mast cells activated were positively correlated with the risk score, while T cells CD8 and mast cells resting were negatively associated with the risk score. Such results may provide a partial explanation for the poor prognosis at the level of tumor immunity. LncRNAs have been involved with human disease pathogenesis and are recognized as biomarkers and therapeutic targets. Recent studies have provided compelling evidence that lncRNAs act as vital regulators in immune response processes, including T cell development, immune escape [ 30 ], antiviral innate immunity [ 31 ], macrophage M2 polarization [ 32 ], and maintenance of epidermal homeostasis [ 33 ]. In addition, the predictive value of lncRNA PCA3 has been shown to be greater than that of the prostate-specific antigen for prostate cancer [ 34 ]. Similarly, HULC, MALAT1 and HOTAIR are novel prognostic markers significantly associated with survival in osteosarcoma, non-samll-cell lung cancer and some gastroenteric tumor [ 35 – 37 ]. As therapeutic agents, lncRNAs can regulate post-transcriptional degradation, antisense pairing, or drug delivery [ 38 – 40 ]. Despite these exciting results regarding the roles of immune-related lncRNAs as effective biomarkers, their capacity for predicting survival and relative mechanisms as involved with CC are still lacking. To our knowledge, this report provides the first evidence for the prognostic value of a model for CC. Our established model indicates that the risk score is an independent prognostic factor in CC as demonstrated from results obtained with uni- and multi-variate Cox regression analysis. We divided the patients into two groups as based on their median risk score, and further study was to analyze the association between immune-related lncRNA signatures and clinical characteristics of CC. The results strongly indicated that the risk score was associated with the progression of CC. Importantly, a significant correlation was obtained between the 20 immune-related lncRNA signatures and the expression of antiviral genes, which provides an explanation for the prognostic outcome at the level of antiviral ability. Based on these results, the underlying mechanism of regulation in CC development and treatment will require further exploration. This study strongly indicates that the immune-related lncRNAs show a notable prognostic value for CC patients and offer the potential immunotherapy targets. Despite these promising results, there remain some limitations regarding this study that need to be addressed. First, the amount of data available was only validated in the TCGA dataset, therefore additional patient datasets will be required to corroborate these findings. In addition, further analyses and experiments will be needed to substantiate our results before the immune-related lncRNAs can be applied in the clinic. In summary, we present the first evidence for construction and validation of a novel prognostic immune-related lncRNA in CC. We also report that the risk score-based groups have different immune states and antiviral ability. Collectively, our data provide new insights into the prognostic and therapeutic evaluation of CC and offer the foundation for further investigations into the regulatory mechanisms involved with CC. Abbreviations CC cervical cancer lncRNA long noncoding RNAs TCGA the Cancer Genome Atlas ROC receiver operating characteristic HPV human papillomavirus OS overall survival IRGs immune-related genes GSEA gene set enrichment analysis KEGG encyclopedia of genes and genomes GO gene ontology. Declarations Acknowledgements Not applicable. Authors’ contributions ZL and YY designed the study, data analysis and wrote the manuscript. YY revised and finalized the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China under Grant number 81803148. Availability of data and materials The data used to support the findings of this study is included within the article, and the data are available from the corresponding author upon request. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Author details Department of Dermatology, The First Hospital of China Medical University and National joint Engineering Research Center for Theranostics of Immunological Skin Diseases, The First Hospital of China Medical University and Key Laboratory of Immunodermatology, Ministry of Health and Ministry of Education, Shenyang, China References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. Torre LA, Islami F, Siegel RL, Ward EM, Jemal A. 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Knockdown of nuclear-located enhancer RNAs and long ncRNAs using locked nucleic acid gapmeRs . Methods Mol Bio. 2017; 1468 :11–18. Modarresi F, Faghihi MA, Lopez-Toledano MA, Fatemi RP, Magistri M, Brothers SP, et al. Inhibition of natural antisense transcripts in vivo results in gene-specific transcriptional upregulation . Nat Biotechnol. 2012; 30 ( 5 ):453–459. Mizrahi A, Czerniak A, Levy T, Amiur S, Gallula J, Matouk I, et al. Development of targeted therapy for ovarian cancer mediated by a plasmid expression diphtheria toxin under the control of H19 regulatory sequences. J Transl Med. 2009;7:69. 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 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-1460885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":93125456,"identity":"0fe478fb-1deb-4374-95ba-85c715ca3b1a","order_by":0,"name":"Lan Zhang","email":"","orcid":"","institution":"The First Hospital of China Medical University, The First Hospital of China Medical University, Ministry of Health and Ministry of Education","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Zhang","suffix":""},{"id":93125458,"identity":"ce3e3a71-334a-4727-9b7c-811e33c75dc3","order_by":1,"name":"Yang Yang","email":"data:image/png;base64,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","orcid":"","institution":"The First Hospital of China Medical University, The First Hospital of China Medical University, Ministry of Health and Ministry of Education","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2022-03-17 07:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1460885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1460885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20019908,"identity":"8c827a24-9fa2-496d-9f84-8be436a1279f","added_by":"auto","created_at":"2022-04-06 13:34:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2289793,"visible":true,"origin":"","legend":"\u003cp\u003eDistinction of immune-related lncRNAs correlated with CC prognosis. \u003cstrong\u003e(a) \u003c/strong\u003eConstruction of a co-expression network between immunogenic genes and lncRNAs. \u003cstrong\u003e(b) \u003c/strong\u003eImmune-related lncRNAs in combination of survival time and survival status evaluated by Univariate cox regression analysis. \u003cstrong\u003e(c-e)\u003c/strong\u003e LASSO model and LASSO coefficient profiles to identify the immune-related lncRNAs correlated with prognosis.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/11bb06145fb1bd16dbf0ffbc.jpg"},{"id":20019906,"identity":"94b4ca9f-5117-4b21-8181-8933777761e0","added_by":"auto","created_at":"2022-04-06 13:34:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":994103,"visible":true,"origin":"","legend":"\u003cp\u003eRisk score and survival analysis.\u003cstrong\u003e (a) \u003c/strong\u003eRisk scores, survival status and expression of the 20 immune-related lncRNAs.\u003cstrong\u003e (b)\u003c/strong\u003e Kaplan-Meier analysis of the OS in the high- and low-risk groups. \u003cstrong\u003e(c)\u003c/strong\u003e Time-dependent ROC curves at 1, 3, and 5 years post diagnosis of immune-related lncRNA signatures in CC.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/f0dc93aea720febc19b1e7cb.jpg"},{"id":20020166,"identity":"0925d7ac-ab97-434e-862c-a419a8ccc1f9","added_by":"auto","created_at":"2022-04-06 13:39:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":583234,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic significance of the high-and low-risk groups with different clinical features. Age\u0026lt;=50 \u003cstrong\u003e(a)\u003c/strong\u003e, Age \u0026gt;50 \u003cstrong\u003e(b)\u003c/strong\u003e, Grade 1-2 \u003cstrong\u003e(c)\u003c/strong\u003e, Grade 3-4 \u003cstrong\u003e(d)\u003c/strong\u003e, M0 \u003cstrong\u003e(e)\u003c/strong\u003e, M1 \u003cstrong\u003e(f)\u003c/strong\u003e, N0 \u003cstrong\u003e(g)\u003c/strong\u003e, N1 \u003cstrong\u003e(h)\u003c/strong\u003e, T1-2 \u003cstrong\u003e(i)\u003c/strong\u003e, T3-4\u003cstrong\u003e (j)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/387b5bb8c69fd68f8822418b.jpg"},{"id":20020168,"identity":"7eb8e7a8-4901-4d5d-a1b0-fbdae30f3267","added_by":"auto","created_at":"2022-04-06 13:39:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":539309,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent enrichment of immune-related lncRNAs between high- and low-risk groups. Kyoto Encyclopedia of Genes and Genomes (KEGG)\u003cstrong\u003e (a)\u003c/strong\u003e, Gene ontology (GO) Molecular function \u003cstrong\u003e(b)\u003c/strong\u003e, cellular component \u003cstrong\u003e(c)\u003c/strong\u003e, and biological process \u003cstrong\u003e(d)\u003c/strong\u003e.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/dee709a67748b4845d99413a.jpg"},{"id":20019905,"identity":"096c8a8b-3f4f-47c5-82fe-cf6171a29b40","added_by":"auto","created_at":"2022-04-06 13:34:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":209334,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate and multivariate cox analysis of risk score, age, tumor grade and stage as independent predictors. Univariate Cox analysis \u003cstrong\u003e(a)\u003c/strong\u003e, Multivariate Cox analysis \u003cstrong\u003e(b)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/5890aa62e96da008f1d6bf39.jpg"},{"id":20020167,"identity":"54606991-4dd9-4d82-9cc7-f4f80dd50b11","added_by":"auto","created_at":"2022-04-06 13:39:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":859889,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between risk scores and clinical characteristics of CC. \u003cstrong\u003e(a)\u003c/strong\u003e Association between immune-related lncRNA signatures and clinical features.\u003cstrong\u003e \u003c/strong\u003eThe analysis of immune infiltration between the low- and high-risk groups in Immune score \u003cstrong\u003e(b)\u003c/strong\u003e, Stromal score \u003cstrong\u003e(c)\u003c/strong\u003e, and ESTIMATE score \u003cstrong\u003e(d)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/2bb31b7a4bbd17611d0b7a6d.jpg"},{"id":20019910,"identity":"b3150a60-bfb3-4ec0-b1df-ea59163870cf","added_by":"auto","created_at":"2022-04-06 13:34:58","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":804566,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison of 22 immune cells between low- and high-risk groups. \u003cstrong\u003e(a)\u003c/strong\u003e The proportion of 22 subpopulations of immune cells in two groups.\u003cstrong\u003e (b) \u003c/strong\u003eAssociation between the 20 immune-related lncRNA signatures and the infiltration of immune cell subtypes.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/4de267b640d3ec5a0242ccad.jpg"},{"id":20019912,"identity":"0c163b95-f32c-4f81-aff2-b561c2032043","added_by":"auto","created_at":"2022-04-06 13:34:58","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1391694,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of immune-related lncRNA signatures and antiviral genes. APOBEC3G \u003cstrong\u003e(a)\u003c/strong\u003e, PML \u003cstrong\u003e(b)\u003c/strong\u003e, MX1 \u003cstrong\u003e(c)\u003c/strong\u003e, TLR3 \u003cstrong\u003e(d)\u003c/strong\u003e, TRAIL \u003cstrong\u003e(e)\u003c/strong\u003e, XAF1 \u003cstrong\u003e(f)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/f8ede98622e1d77b9916c560.jpg"},{"id":20020169,"identity":"885b348a-5583-4cae-8fb9-18c6f630eac2","added_by":"auto","created_at":"2022-04-06 13:40:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244179,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1460885/v1/db5bb639-1bf5-4673-a337-8a549b6173c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening of tumor microenviroment immune-related lncRNA related to the prognosis of cervical cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eCervical cancer (CC) is the fourth most common type of malignant tumors, with a high incidence and mortality rate among women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Survey results reveal that an estimated 266,000 women die annually from CC, and this number is anticipated to double by 2035 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with of 90% of these deaths occurring in developing countries. As one example, in China, the incidence and mortality rates of CC are showing a significant increase, especially in young women [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Persistent high-risk human papillomavirus (HPV) is considered a leading factor involved with the development and progression of CC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, treatments for CC include surgery, radiotherapy, chemotherapy, and more recently use of potential targeted and biological therapies based on specific molecular features [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Apart from these, the use of immunotherapy targeted production of HPV antigens or adoptive T-cell transfer therapy offer a novel and promising opportunity for recurrent or metastatic patients. However, even with these advances in CC treatment, the prognosis remains rather dismal, with 5-year overall survival (OS) rates for stage Ⅱ patients being only 65%, and this percent that gradually drops to 40% for more severe patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given these current conditions, there is an urgent need to identify the prognostic biomarkers which can promptly and accurately predict the clinical outcomes and provide personalized treatment regimens.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs), a type of non-coding RNA with a sequence of over 200 nucleotides, have been shown to exhibit multiple biological activities. Emerging studies have revealed that lncRNAs participate in the regulation of chromatin remodeling, levels of transcriptional and post-transcriptional activity, as well as splicing and epigenetics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Disorders in lncRNAs can lead to increase in the occurrence of cancer, and are related to tumor immunity, immune cell migration and infiltration, antigen release and presentation, as well as immunotherapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It has been reported that immune-related lncRNAs could be considered as prognostic biomarkers for melanoma, breast cancer, bladder cancer, hepatocellular carcinoma, lung adenocarcinoma, and renal cell carcinoma, however, related research on immune-related lncRNA signatures in CC remains lacking [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With regard to immune-related lncRNA signatures in CC, previous studies have focused on a single abnormal expression of lncRNA, such as XLOC_006390, as being involved in multiple facets to facilitate CC tumorigenesis and metastasis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, joint multiple immune-related lncRNAs in CC have not yet been investigated.\u003c/p\u003e \u003cp\u003eTherefore, considering the predictive value of multiple immune-related lncRNAs as a promising prognostic biomarkers, we conducted a signature model comprising 20 immune-related lncRNAs by univariate and LASSO Cox regression analysis as related to CC. In addition, samples were further divided into low- and high-risk groups as based on the median risk score. Finally, the proportion of 22 immune cells via CIBERSORT algorithms were calculated as a means to assess the associations between risk scores and differential proportions of immune cells or antiviral genes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and datasets\u003c/h2\u003e \u003cp\u003eThe genomic data and corresponding clinical information were acquired from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/repository\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In total, 254 CC samples and 3 adjacent normal tissue samples were included in this research. The detailed clinical data including age, survival stage, survival time, T stage, lymph node status and metastasis at the time of diagnosis were recorded (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients with a survival time less than 30 days were not included in the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of cervical cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u0026nbsp;Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.96\u0026nbsp;(20\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eШ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eT\u0026nbsp;classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eM classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eN\u0026nbsp;classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurvival status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDistinction of immune-related lncRNAs\u003c/h2\u003e \u003cp\u003eImmune-related genes (IRGs) were downloaded from the Immport Shared Data. Pearson\u0026rsquo;s correlation analyse was use to differentiate between expressed IRGs and lncRNAs to identify immune-related lncRNAs in CC samples (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;.001)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Finally, 286 immune-related lncRNAs were identified by constructing an immuno-lncRNAs co-expression network. Expression levels of these immune-related lncRNAs were then applied to further build the prognostic model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the immune-related lncRNAs signatures associated with prognosis\u003c/h2\u003e \u003cp\u003eAccording to univariate Cox regression analysis, the independent prognostic predictors among immune-related lncRNAs were selected based on the \u0026ldquo;survival\u0026rdquo; package. LASSO analysis and the stepwise Cox proportional hazards regression model were used to construct the immune-related lncRNA prognostic model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The formula for calculating the risk scores was: \u0026sum;ɨcoefficient (lncRNAɨ)\u0026times;expression (lncRNAɨ). As based on the median risk score, patients were divided into either high- or low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune and stromal scores\u003c/h2\u003e \u003cp\u003eImmune and stromal scores were calculated as based on the \u0026ldquo;ESTIMATE\u0026rdquo; package.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePotential regulatory pathways analysis\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA) was used to quantify normalized enrichment scores between the low- and high-risk score groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the immune-related lncRNAs signatures as independent prognostic factors in CC\u003c/h2\u003e \u003cp\u003eBoth uni- and multi-variate Cox regression analysis were performed to assess the clinical information, including risk score, age, and stage of disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparisons of the 22 immune cell subtypes in the two risk groups\u003c/h2\u003e \u003cp\u003eThe CIBERSORT package was applied to estimate the proportion of 22 immune cell subtypes in the two risk groups as based on the expression profiles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The number of permutations was set at 1000. Samples with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the CIBERSORT analysis were used for further analysis. Associations between risk scores and immune cell subtypes were assessed with use of Pearson\u0026rsquo;s correlation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.1.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A p-value of \u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of immune-related lncRNAs\u003c/h2\u003e \u003cp\u003eA total of 14086 lncRNAs were identified from the TCGA database, and 2579 IRGs from the Immport database were downloaded. The immune-related lncRNAs were identified by constructing a co-expression network of immunogenic genes and lncRNAs, with a final number of 286 immune-related lncRNAs being extracted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of immune-related lncRNAs signatures\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression analysis was used to determine whether any\u003c/p\u003e \u003cp\u003eassociations were present between the 286 immune-related lncRNAs and OS of CC samples. From this analysis, we identified 41 survival associated immune-related lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Then, 20 immune-related lncRNAs were utilized to build the prognostic model by stepwise LASSO regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-d). Risk score of each patient was calculated according to the expression levels of the 20 lncRNAs and their corresponding coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Risk score= (-0.1518*AC100847.1) + (-0.1755*LINC01943) + (0.8935*AP001528.1) + (0.0161*AP001527.2) +\u003c/p\u003e \u003cp\u003e(-0.1930*AP001318.2) + (-0.0715*AC008035.1)+ (-0.2153*AC108134.3) +\u003c/p\u003e\n\u003cp\u003e(0.5749*ac007998.3) + (0.2186*ac008429.1) + (0.0183*miat) +\u003c/p\u003e\n\u003cp\u003e(0.0780*AC008687.3) + (-0.1504*AC243829.4) + (0.0147*MEG3) + (0.0179\u003c/p\u003e \u003cp\u003e*AC002401.4) + (-0.2278*AC009097.2) + (-0.0865*AL109811.2) + \u003cp\u003e(-0.5642*AC007728.2) + (-0.1477*AC008124.1) + (-0.0017*USP30-AS1) +\u003c/p\u003e \u003cp\u003e(0.0875*DNM3OS).\u003c/p\u003e \u003cp\u003eBased on the median risk score as a cutoff, patients were further divided into either high- or low-risk groups, with each patient\u0026rsquo;s survival outcome and lncRNA expression levels presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. Results from Kaplan-Meier (K-M) survival curves indicated that the OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In addition, areas under the receiver-operating characteristic curves (ROC) were 0.801, 0.800, and 0.789 at 1, 3, and 5 years post diagnosis, respectively, indicating that these 20 immune-related lncRNAs exhibited a reliable capacity for predicting the OS of CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic analysis of risk models in different subgroups of CC patients\u003c/h2\u003e \u003cp\u003eTo further clarify the role of these 20 immune-related lncRNAs signatures in CC progression, data obtained between the signature and clinical features were subjected to correlational analysis. It was found that elderly patients (age\u0026thinsp;\u0026gt;\u0026thinsp;50), high Grade (3/4), and a poor pathological stage were significantly correlated with a high risk score, while low risk scores were significantly associated with a good prognosis. These findings provide support for the accuracy of this risk models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis Based on Risk Score\u003c/h2\u003e \u003cp\u003eWe investigated the different functions of immune-related lncRNAs in high- and low-risk groups. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e contained the top 10 most highly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) items within the high risk group as compared with that of the low risk group. Among the top 10 KEGG pathways, ECM receptor interaction and focal adhesion were significantly related to the progression of CC. The top 10 most highly enriched molecular functions, cellular components and biological processes included oxidative phosphorylation, oxidoreductase activity, and other energy metabolism-related activities, all of which were significant factors linked to the tumorigenesis and progression of CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Independent Prognostic Factors\u003c/h2\u003e \u003cp\u003eBoth uni- and multi-variate Cox regression analysis were performed to investigate the independence of the prognostic signatures. Results showed that both risk score and pathological N stage were related to the prognosis of CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Taken together, these results illustrated that the risk score value served as an independent factor for the prognosis of CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between Risk Models and Immune Status\u003c/h2\u003e \u003cp\u003eThe patients were divided into two groups (high-risk and low-risk) based on the median risk score. Clinical information recorded on these patients included age, immune score, grade of disease, tumor invasion (T), lymph node (N), and metastasis (M). Differences observed between the low- versus high-risk groups tended to be in pathological T stage and immune score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Further analysis revealed that the high-risk group had lower ESTIMATE scores but no difference of stromal score were found between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb-d). Given the above results, we further analyzed the immune components and relationship between the signature and immune cell subtype infiltration of CC. The infiltration of 22 types of immune cells in the low- and high-risk groups were determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The results demonstrated that T cells CD8, T cells follicular helper, T cells regulatory, as well as mast cells resting were negatively correlated with risk scores, while macrophages M0, dendritic cell activated, mast cells activated, along with neutrophils were significantly increased in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). These results suggested that the signature was related to the immune states of CC, while high risk scores were associated with lower immune cell compositions in the CC microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between Differential Antiviral Genes and Immune-related LncRNAs in the Two Groups\u003c/h2\u003e \u003cp\u003ePersistent infections with high-risk HPVs are the major causative factor for the development of CC and it has been reported that HPV infects approxiamately 100\u0026nbsp;million women around the world [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We analyzed the association between the 20 immune-related lncRNAs and antiviral genes, with the results that antiviral genes, including \u003cem\u003eAPOBEC3G, PML, MX1, TLR3, TRAIL\u003c/em\u003e, and \u003cem\u003eXAF1\u003c/em\u003e were all correlated with at least 6 of the 20 immune-related lncRNAs. In addition, compared with that of the low-risk group, the high-risk group showed lower expressions of antiviral genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These results further demonstrated that these 20 lncRNAs signatures affected the existence of HPV and progression of CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCC is one of the most prevalent cancers, producing malignant tumors of genital tract, and a high mortality rate among females. Accordingly, this condition represents a prominent public health issue [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. HPV infection is considered an indispensable factor involved with the development and progression of CC. Findings from epidemiological studies indicate that 80% of sexually active women have at least one opportunity HPV infection within their lifetime [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As most cases of high-risk HPV experience a self-limiting clinical course of approximately 1\u0026ndash;2 years, leaving few cases progressing to precancer or cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It should be noted that although prophylactic vaccines against HPV have shown promising results in recent years, the implementation of an universal HPV vaccination program is not realistic in developing countries. The 5-year survival rate of patients diagnosed with stage ⅡB decreases from 50\u0026ndash;60% to 30\u0026ndash;40% in stage ШB [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], therefore, a clear need exists for the development of an effective prognostic model that offers novel therapeutic targets for CC. Importantly, we found that this risk predication model can not only judge the prognosis, but also provide a better index of the immune status and antiviral ability of these CC patients.\u003c/p\u003e \u003cp\u003eEmerging studies, which demonstrate the existence of viral antigens in CC, suggest that immunotherapy can serve as a promising treatment option. ADXS11-011, a live attenuated bioengineered molecule, could promote the differentiation of cytotoxic T-lymphocytes to kill cancer cells by targeting HPV transformed cells and results from a phase Ⅱ clinical trial study have demonstrated that ADXS11-011 achieved a 36% rate of 12-month survival in recurrent or persistent CC [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additional evidence from a comprehensive serial study have demonstrated that anti-PD1/PD-L1 agents produce an overall response rate of 13.3%-26% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. And adoptive T-cell transfer therapy targeting E7 T-cell receptors has emerged as a promising therapy that could provide durable responses in a number of patients with advanced CC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Importantly, immunotherapy has proved to exert a vital role in the treatment of CC and cell immune responses are potential factors that can influence tumor progression and prognosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Related studies have provided evidence that dendritic cells may play an immunosuppressive role thereby enabling a host tolerance to HPV antigens [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, CD8 T cells and resting mast cells were found to be associated with an overall better survival rate, while activated mast cells are related with poorer survival [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our current study, we also found that dendritic cell activated and mast cells activated were positively correlated with the risk score, while T cells CD8 and mast cells resting were negatively associated with the risk score. Such results may provide a partial explanation for the poor prognosis at the level of tumor immunity.\u003c/p\u003e \u003cp\u003eLncRNAs have been involved with human disease pathogenesis and are recognized as biomarkers and therapeutic targets. Recent studies have provided compelling evidence that lncRNAs act as vital regulators in immune response processes, including T cell development, immune escape [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], antiviral innate immunity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], macrophage M2 polarization [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and maintenance of epidermal homeostasis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, the predictive value of lncRNA PCA3 has been shown to be greater than that of the prostate-specific antigen for prostate cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, HULC, MALAT1 and HOTAIR are novel prognostic markers significantly associated with survival in osteosarcoma, non-samll-cell lung cancer and some gastroenteric tumor [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As therapeutic agents, lncRNAs can regulate post-transcriptional degradation, antisense pairing, or drug delivery [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Despite these exciting results regarding the roles of immune-related lncRNAs as effective biomarkers, their capacity for predicting survival and relative mechanisms as involved with CC are still lacking. To our knowledge, this report provides the first evidence for the prognostic value of a model for CC.\u003c/p\u003e \u003cp\u003eOur established model indicates that the risk score is an independent prognostic factor in CC as demonstrated from results obtained with uni- and multi-variate Cox regression analysis. We divided the patients into two groups as based on their median risk score, and further study was to analyze the association between immune-related lncRNA signatures and clinical characteristics of CC. The results strongly indicated that the risk score was associated with the progression of CC. Importantly, a significant correlation was obtained between the 20 immune-related lncRNA signatures and the expression of antiviral genes, which provides an explanation for the prognostic outcome at the level of antiviral ability. Based on these results, the underlying mechanism of regulation in CC development and treatment will require further exploration.\u003c/p\u003e \u003cp\u003eThis study strongly indicates that the immune-related lncRNAs show a notable prognostic value for CC patients and offer the potential immunotherapy targets. Despite these promising results, there remain some limitations regarding this study that need to be addressed. First, the amount of data available was only validated in the TCGA dataset, therefore additional patient datasets will be required to corroborate these findings. In addition, further analyses and experiments will be needed to substantiate our results before the immune-related lncRNAs can be applied in the clinic.\u003c/p\u003e \u003cp\u003eIn summary, we present the first evidence for construction and validation of a novel prognostic immune-related lncRNA in CC. We also report that the risk score-based groups have different immune states and antiviral ability. Collectively, our data provide new insights into the prognostic and therapeutic evaluation of CC and offer the foundation for further investigations into the regulatory mechanisms involved with CC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecervical cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elong noncoding RNAs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman papillomavirus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune-related genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eencyclopedia of genes and genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene ontology.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZL and YY designed the study, data analysis and wrote the manuscript. YY revised and finalized the manuscript. All authors read and approved the final manuscript.\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 under Grant number\u0026nbsp;81803148.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study is included within the article, and the data are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\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\u003eNot applicable\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\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Dermatology, The First Hospital of China Medical University and National joint Engineering Research Center for Theranostics of Immunological Skin Diseases, The First Hospital of China Medical University and Key Laboratory of Immunodermatology, Ministry of Health and Ministry of Education, Shenyang, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 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J Transl Med. 2009;7:69.\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":"Cervical cancer, Immune gene, LncRNA, Immune microenviroment, Antivirus","lastPublishedDoi":"10.21203/rs.3.rs-1460885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1460885/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) represents a major gynecologic health problem, accounting for the fourth most widespread cancer in women. A factor that may contribute to the occurrence and development of CC is involving disorders of long noncoding RNAs (lncRNAs). The current study aimed to isolate immune-related lncRNA signatures capable of predicting CC prognosis and identify targets for immunotherapy. \u003c/p\u003e\u003cp\u003eMethods: RNA-seq data and clinical characteristic information of CC were acquired from The Cancer Genome Atlas (TCGA) database and immune-related genes were downloaded from ImmPort Shared Data. Prognostic genes were screened via using univariate, LASSO, and multivariate Cox regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess the prognostic signature. The association between risk score and immune cell infiltration or antiviral genes was evaluated by CIBERSORT and Pearson correlation analysis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn this study, we constructed a prognostic risk signature model comprising\u0026nbsp;20 immune-related lncRNAs. With this model, patients in the high-risk group showed a poorer prognosis than those in the low-risk group. Results from univariate and multivariate Cox regression analyses demonstrated that this risk model was an independent prognostic factor. According to CIBERSORT algorithm results, we found different tumor microenviroment immune cell infiltration levels in the low- and high-risk groups and antiviral genes were closely associated with 20 immune-related lncRNAs, while further analysis showed that lower expressions of these genes were present in the high-risk group. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study shows immune-related lncRNAs can serve as effective agents for the prognosis of CC and offer the potential for immunotherapy targets.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Screening of tumor microenviroment immune-related lncRNA related to the prognosis of cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-06 13:34:56","doi":"10.21203/rs.3.rs-1460885/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":"4ebd66e3-11c9-4b45-ab76-7693b3e0767d","owner":[],"postedDate":"April 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-06T13:34:57+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-06 13:34:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1460885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1460885","identity":"rs-1460885","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}