The effect of telomeres in cervical cancer

The effect of telomeres 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 Article The effect of telomeres in cervical cancer Cong Xu, Yonghong Xu, Qing Cao, Guoling Luo, Jingwen Yu, Guangming Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4640574/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 Globally, cervical cancer ranks as a prevalent cancer among women and stands as the fourth leading cause of mortality in gynecological cancers. Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model. Within the discovered TCGA group (p < 0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients. cervical cancer telomere prognosis drug sensitivity immuno therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The incidence of cervical cancer is about twice as high and the death rate is three times greater in low- and middle-income nations than in high-income ones 1 . This is mainly because there is a serious imbalance in health care resources among low-income populations. Cervical cancer is one of the most important malignant tumors that threaten women's lives, especially in developing countries. According to the Global Cancer Report 2020, there are 530,000 new cases of cervical cancer and 250,000 deaths worldwide each year, of which 85% are in low - and middle-income countries 2 . There's a steady decrease in cervical cancer cases in Western developed nations, attributed to the widespread use of the HPV vaccine. Despite this, developing countries with low and middle incomes exhibit a significantly elevated incidence of cervical cancer, primarily due to various factors such as uneven healthcare resource availability, reliance on a sole funding source, and insufficient legislative backing 3 . In order to eradicate cervical cancer worldwide by 2130, the World Health Organization launched a plan in November 2020. The primary goal of this strategy is to lower the global incidence of cervical cancer to less than 4 cases per 100,000 women yearly. Several tactics will be used throughout this period to reduce the incidence of cervical cancer. With this strategy, 90% of girls under the age of 15 will receive immunizations, and 70% of women between the ages of 35 and 45 would receive precise screening 4 . There is a need to develop new treatment approaches because relying just on current diagnostic and therapeutic techniques is insufficient to meet this goal. Enhancing the telomere length maintenance mechanism (TMM) is essential for attaining cell immortality since telomere shortening linked to regular cell proliferation is a major cause of cell death. Repetitive DNA is found in human telomeres, typically in tandem arrays of the hexonucleotides 5'-TTAGGG-3' 5 , 6 . The telomere's tail consists of 25–200 single-stranded DNA nucleotides, whereas the remaining telomere primarily consists of Watson-Crick base pairs, creating double-stranded DNA. Nonetheless, chains abundant in G can engage in Hoogsteen base pairing, creating a flat G-quartet formation, potentially safeguarding telomeres against DNA repair. Another sophisticated formation, the T-ring, plays a role in safeguarding telomeres against the impacts of DNA repair 7 , 8 .Scholarly concern over the regulatory function of telomere genes has grown in recent years. In this work, the regulatory mechanism of telomere genes was taken into consideration when employing bioinformatics techniques to investigate the involvement of telomere genes in cervical cancer 9 . Human cells reproduce endlessly following particular genetic and epigenetic modifications, leading to a pathological situation 10 . On the rare occasions that human cells escape the crisis, these cells almost universally express the ribonucleoprotein telomerase and maintain stable but short telomeres 11 . Although telomerase does not drive the carcinogenic process, it is necessary for the sustained growth of most advanced cancers. The study of the interaction between telomeres and telomerase has become a hot topic in cancer treatment 12 . 2.Method 2.1 Data source CC transcription omics information and clinical characteristics in patients, comprising 306 tumor samples and 3 normal samples, with the cancer genome database (TCGA https://portal.GDC.Cancer . Gov). Clinicopathological information, such as gender, age, stage status, TMN type, survival status, and survival period, as well as mRNA expression of CC patients were acquired. Matrix information and full pathology information for every clinical sample were extracted using Strawberry Perl.And telomere gene data is derived from TelNet website. 2.2Differential expression of telomere related genes and screening prognostic related genes The unpaired T-test was utilized to determine the gene's P-value using the limma package in R. A threshold of < 0.05 was established for the P-value. To investigate the data structure and pattern further, we might examine the differences in the expression of telomere-related genes between normal tissues and tissues from cervical cancer. The heat map and volcano was mapped using "pheatmap", "survminer, survival" to identify the telomere genes that were significantly related to prognosis 13 . Utilizing STRING's protein-protein interaction (PPI) feature ( https://cn.string-db.org ), each clinically significant module was equipped with a PPI network for potential biomarker genes, which was then visualized using R. Additionally, gene and genome ontology enrichment (GO) is facilitated by Kyoto Encyclopedia (KEGG pathway analysis) and the INPUT2.0 website ( http://cbcb.cdutcm.edu.cn/INPUT/ ). 2.3 Development and validation of prognostic features associated with telomeres Next, we employed the "glmnet" R package to do LASSO regression analysis in order to determine the important genetic prognosis of telomeres in cervical cancer. The expression value of considerably differently expressed telomere genes in cervical cancer was represented by Xi, the regression coefficients in lasso analysis were marked as Coefi, and the number of significantly differentially expressed telomere genes in prognosis was represented by n. Using the median risk score as a guide, patients were categorized into high-risk and low-risk categories 14 . The "Survival" software program in R was then used to do a Kaplan-Meier survival analysis on these groups in order to evaluate the sensitivity and specificity of risk. R packets such "reshape2" and "BiocManager" were also used to map the differential expression results of these important genes in the tissues of cervical cancer and normal cervical tissue. 2.4 Roc curve analysis and risk scoring model With the help of the "survival" R package, the prognostic value of telomere-related genes was assessed using univariate and multifactorial Cox regression. The telomere genes linked to the prognosis of cervical cancer were then further examined, and a corresponding forest map was created in order to determine whether changes in telomeres are an independent risk factor. It affects CC patients' chances of survival. Next, we generate the ROC curve using the R software's "timeROC" toolset. The nomogram's prognostic impact was estimated using the area under the ROC curve (AUC). Risk ratings and clinical criteria were combined to construct a nomogram using the "survival" and "rms" packages. 2.5CIBERSORT immune cell composition analysis, difference analysis and genetic correlation analysis The high-risk and low-risk groups' immune cell expression differed significantly from one another. Therefore, it's possible that immune cell dispersion varies among disorders. We conducted a thorough differential expression analysis to assess immune cell profiles in patient groups at high and low risk. The foundation for the development of the CIBERSORT program is the extraction of baseline gene expression from immune cells. A linear model is then used to forecast the quantity of immune cells in a sample, and sequencing tests are employed to evaluate the relevance of the outcomes. Afterwards, we visualized the variations in immune cells between cervical cancer and control samples using the packages limma, reshape2, and ggpubr. The main function of these packs is to correlate immune cells with their genes 15 . 2.6 Drug sensitivity Utilizing the tumor susceptibility to multiple omics (GDSC) database ( https://www.cancerrxgene.org/ ) to obtain gene expression data connected to cancer associated with various medications. The "pRRophetic" R program can be used to assess the individual telomere gene response of cancer patients to various chemotherapy medications 16 . 3.Result 3.1DEG screening and PPI enrichment analysis After data preprocessing, a total of 327 DEGs were identified, of which 240 were up-regulated and 87 were down-regulated. The heat map clearly shows the CC and control groups. And The high and low risk genes were analyzed, and the telomere genes were found to be significantly related to the prognosis. The GO and KEGG analyses revealed the role of DEGs in the pathogenesis of CC. GO analysis (Figure 1 A, B)reveals that marker gene products in biological processes are mainly enriched in DNA replication, DNA − templated DNA replication, regulation of cell cycle phase transition, double − strand break repair and telomere organization processes. Marker gene products are located inside cells catalytic activity acting on DNA, ATP − dependent activity acting on DNA, helicase activity, single − stranded DNA helicase activity, DNA helicase activity and other pathways are significantly enriched. Gene products are enriched in chromosomal region, nuclear chromosome, chromosome, telomeric region, protein − DNA complex, replication fork, etc.༈Figure 2 A༉In addition, as shown in the figure, gene KEGG analysis showed that candidate genes were mainly enriched in Cell cycle, DNA replication, Cellular senescence, double − strand break repair, Fanconi anemia pathway. Pathway enrichment analysis showed that candidate biomarkers were mainly related to cell cycle. Abnormal function of these genes may lead to abnormal cell synthesis and apoptosis cycles in the body, and promote the development of cervical ༈Figure 2 B-D༉cancer 17 . A total of 30 telomere genes most closely related to cervical cancer were screened to form the PPI network complex. These genes are ABCC9, ADK, ALDOA, AMPH, AR, ARF6, ARHGAP27, ARPC5L, ARRDC1, ASF1B, ATAD2, ATAD5, AURKA, AURKB, BAIAP2L1, BLM, BRCA1, BRCA2, BRIP1, and so on. BRMS1, BUB3, C17orf49, CALD1, CAMK1, CCDC137, CCNA2, CCNB1, CCNE1, CCNE2, CCT5 (Fig. 2 E, F) 18 . 3.2 Construction of prognostic markers for cervical cancer We combined the expression groups of telomeres associated with cervical cancer and clinical follow-up information on CC. Through LASSO regression analyses, we identified 27 genes that showed correlation with the prognosis of cervical cancer, among which 13 genes inhibited the development of cervical cancer, namely ALDOA, CALD1, DPP3, DSG2, EIF4B, GAPDH, LYPLA1, ALDOA, DPP3 and DPP3. MAP7, MYO10, PAICS, PKM, TFRC and TLN1 (Figure 3 A). There are 14 genes that promote the development of cervical cancer, including CHAF1B, DDX39A, FOXN1, JUND, LAGE3, MCM5, PAFAH1B3, PCP4, POLA2, RFC5, RNASEH2A, SH3BP1, TP73 and TSPYL2. Next, LASSO analysis was used to narrow down the range of prognostic genes. With the minimum partial likelihood bias and mean square The log (λ) value corresponding to error is taken as the optimal value (graph). Then, according to the coefficient corresponding to the optimal log (λ) value, the prognostic characteristic genes were determined, and finally 16 genes were selected(Figure 3 B) 19 . These 16 genes' differential expression in cervical cancer and healthy tissues was examined ༈Figure 3 C). It is evident from the Lasso regression results that the distinctive genes significantly influence CC and enhance the performance of interpretation and prediction of the occurrence of cervical cancer. 3.3 Risk models for accurate evaluation of patients with CC Kaplan-Meier curve-based survival studies revealed that individuals in the high-risk category experienced notably poorer results(P < 0.01, Fig. 3 D) The findings indicate the precision and predictive power of this risk model for patients with CC. Patients at elevated risk exhibited increased death rates and poorer survival outcomes. This research also employed both univariate and multivariate Cox regression to investigate the link between clinical information and the risk assessment of cervical cancer patients in the TCGA database, along with their prognosis. After in-depth analysis, we found that risk score was indeed an independent factor for the prognosis of cervical cancer patients (P < 0.05), while in contrast, age and grade of cervical cancer patients did not significantly affect their OS (Fig. 4 A,B).Utilizing clinical data and risk scores from cervical cancer patients in the TCGA database, the ROC curve was created, revealing the AUC value of risk score was = 0.778 and the AUC value of age was 0.564. The ROC analysis over time demonstrates the model's forecasting accuracy, exhibiting AUC figures of 0.778, 0.787, and 0.769 for survival rates at 1, 3, and 5 years, in that order (Fig. 4 C,D). 3.4 Column line diagram construction and evaluation Based on the data from the Cox regression analysis, we created a stable nomogram for predicting OS in patients with CC, taking into account riskscore, age, and grade. Using a column chart, we can provide an accurate numerical assessment of survival or risk for each patient with CC, reducing complex regression equations to intuitive graphical representations. In the cohort prediction of TCGA, the AUC values of the 1, 3 and 5 year OS predicted by this model are 0.541 0.63 0.907 respectively 20 . The sub-molecular characterization identification for cervical cancer can be used in clinical diagnosis or prediction of disease risk or prognosis by combining multiple indicators(Figure 5 A,B). 3.5 Correlation analysis between risk score and immune response The heat map illustrates how the proportion of immune-infiltrating cells varies in samples classified as high-risk versus low-risk. T cells regulatory (Tregs), dendritic cells resting (P < 0.01), and mast cells resting (P < 0.001) were the immune cells with the highest infiltration rate in the low-risk group. T cells with CD8, these three cells had significantly lower expression levels in the high expression group compared to the low expression group (P < 0.05). In comparison to the low expression group, there was a significant increase in mast cell activation (P < 0.001) and macrophage M0 (P < 0.05) in the high expression group. It is evident that various immune infiltrates can impact a patient's telomeres, which in turn can alter prognostic markers and immunotherapy targets for cervical cancer 21 , 22 . (Figure 6 A) Next, we mapped the correlated heat maps of differential genes and immune cells to determine the effects of differential genes on immune function in CC patients. The correlation analysis resultsshowed that CHAF1B was significantly negatively correlated with Neutrophils. DSG2 was negatively correlated with B cells memory and T cells gamma delta, and positively correlated with T cells CD4 memory resting. FOXN1 was positively correlated with Dendritic cells resting and Macrophages M1. GAPDH was positively correlated with Mast cells activated and negatively correlated with Mast cells resting. There was significant positive correlation between JUND and Monocytes. There was a significant positive correlation between MAP7 and Mast cells activated and T cells CD4 memory resting. PCP4 was positively correlated with B cells naive. PKM was positively correlated with Mast cells activation. There was a significant positive correlation between RFC5 and T cells CD4 memory resting. SH3BP1 was significantly positively correlated with Dendritic cells resting, Mast cells resting and T cells CD4 memory activated, but significantly correlated with B cells naive, T cells CD4 memory resting was negatively correlated. TFRC and Mast cells activated, T cells CD4 memory resting. TP73 is positively correlated with Dendritic cells resting and has significant significance. TSPYL2 was significantly positively correlated with B cells naive, and negatively correlated with Neutrophils(Figure 6 B, C) 23 , 24 . 3.6 Results of drug sensitivity analysis One of the main issues is that people with cervical cancer are increasingly becoming resistant to the medications used in chemotherapy. For this reason, choosing the right chemotherapy medications is crucial to the customized care of patients with stomach cancer 25 . Subsequently, anti-cancer medications were chosen for this study in order to evaluate drug susceptibility in both high-risk and low-risk populations. Furthermore, it was discovered that patients with high scores had increased drug sensitivity values for PF − 4708671, Pyridostatin, Ribociclib, Venetoclax, and AZD6482. When combined, these findings imply that telomere genes have a role in drug sensitivity 16 . However, further experiments are needed to confirm these results. Moreover, the relationship between these drugs and the risk scores of cervical cancer cells telomere genes was significant and positively correlated. (Figure 7 A-F) 4. discussion Because of the continued lack of significant declines in its incidence, mortality, or disability rates, cervical cancer is becoming an increasingly serious public health concern. While there have been some studies on the causes of cervical cancer, it is important to comprehend its pathogenesis and inducers, which include immunological microenvironment, environmental variables, and genetic factors, given the multifaceted character of cervical cancer. Although a wide range of topics have been studied in the present study on this illness, our knowledge of these mechanisms is still extremely diverse. Consequently, a critical component of the creative development of cervical treatment in the future will be the thorough investigation of the underlying concept 26 . This research involved examining the TCGA database to assess the transcriptomic variances in telomere genes between cervical cancer specimens and control samples. Through analysis and validation using R software, we identified 327 genes with telomere differences primarily linked to CC, noting that their expression levels may increase or decrease in CC. Through PPI, GO, and KEGG enrichment analysis, the functioning of these crucial genes was examined, leading to the exclusion of 16 key gene composition and prognosis models and an assessment of how changes in telomere genes affect cervical cancer patient survival rates. It was also discovered that they have a close connection to the immune system's response. When integrated with immune cells, this confirmation facilitates additional studies on telomeres in cervical cancer 27 . The enrichment analysis of core genes associated with telomeres revealed that these genes are mostly engaged in cell cycle regulation, double-strand break repair, aging of cells, and catalytic activity and replication of DNA. According to research, telomeres eventually deteriorate and exhibit a randomly diminishing pattern during the course of several cell divisions. Telomere shortening frequently results in cell cycle arrest, which sets off the apoptotic process. As a result, research on telomeres is crucial for treating tumors. Previous research has shown that certain age-related processes and human aging illnesses have higher total telomere shortening 12 , 28 . A malfunction in the telomere nucleoprotein complex could expose free chromosome ends to DNA double-strand breaks (DSBS) repair, leading to the fusion of telomere and non-telomere loci. Such irregularities could contribute to the emergence of tumors. The development of cancer is attributed to the stability of replicated telomere arrays, aiding tissue cells in attaining immortality 29 . Attaining this objective is possible through the activation of telomerase or by triggering different telomere elongation (ALT) routes. Consequently, the reduction in telomere length may be viewed as a strategy to inhibit tumors. Alterations in telomeres, triggered by genetics, either elevate or diminish the likelihood and advancement of cancer in distinct manners 30 , 31 . For example, the mechanism of telomere progression in liver cancer has been detailed in the literature. Accumulating research uncovers a process aligning the cell's destiny with the telomere's condition. This paper delves into the destiny of telomeric RNA within cells and the processes contributing to its initial growth 32 . Cervical cancer development is significantly influenced by the immune microenvironment. Presently, it's theorized that tumor cells play a role in various distinct molecules that regulate autoimmune responses. The E7 oncoprotein is capable of diminishing the pro-inflammatory immune signaling route through the interruption of toll-like receptor 9 TLR9 and cGAS-STING pathways. Studies indicate that E6 and E7 disrupt interferon signaling, diminishing the NF-kB pathway and interleukin1β production, all contributing to harm to the host's natural immune response 33 . Oncoproteins are further involved in the downregulation of major histocompatibility complex (MHC) class I by binding to MHC I promoters 33 . This increases the likelihood that the HPV virus linked to cervical cancer may infiltrate the body, which is also a major contributing cause to the dismal prognosis of head and neck cancer. The levels of CCL20 may be lowered by blocking these inflammatory pathways. By inhibiting or lessening the aforementioned pathways, it may be possible to avoid or mitigate tumor spread and recurrence during treatment. As a chemokine, CCL20 helps to prevent infection, hemostasis, cell proliferation, and remodeling. It also attracts antigens in the form of delivery cells, such as Langerhans cells, to the virus-infected area 34 . Immunoinfiltrating data showed that telomere core genes associated with cervical cancer were associated with Mast cells resting, Dendritic cells resting, T cells regulatory (Tregs), T cells CD8, Mast cells activated, Macrophages, Neutrophils, B cells memory, T cells gamma delta, Monocytes and other immune cells were significantly correlated. It has been documented that mast cells contribute to the expansion of blood vessels during the creation and discharge of histamine, a biogenic amine, in various pathological conditions, including allergic responses and conjunctivitis. Histamine may intensify inflammation, leading to heightened capillary permeability and disorders of lymph reflux. Elevated histamine concentrations are linked to multiple tumor types such as cervical, ovarian, vaginal, uterine, vulvar, and colorectal cancers, aiding in the suppression of their proliferation 35 . The role of Tregs in HPV infection is to protect tissue from immune-mediated damage at different anatomical subsites 36 . In addition, macrophages play a key role in the tumor microenvironment, where they have significant effects on blood vessel formation, extracellular matrix remodeling, cancer cell growth, metastasis, and immunosuppressive synergies, as well as on chemotherapeutic drugs and checkpoint blocking immunotherapy resistance. Studies have shown that macrophages have phagocytic function and can produce cytokines such as antibodies, and these immune responses are closely related to the body's immune system. Macrophages have the function of mediating cancer cell phagocytosis and cytotoxic tumor killing, so they have become the main target of cancer therapy 37 . Neutrophils play a significant function in the tumor immunological microenvironment. The body uses neutrophils as its first line of defense against infections and reacts to many inflammatory symptoms, including cancer 38 . In the context of cancer, neutrophils might be reclassified as agents that encourage cancer, suggesting their adaptability and ability to control their activity in diverse inflammatory settings. Latest studies have revealed a complex and nuanced interplay between cancerous cells and healthy cells 39 . 5. Conclusion The telomere genes linked to cervical cancer have been effectively identified by comprehensive bioinformatics research. This study examines the various paths by which these telomere genes can affect cervical cancer, even though a number of previous studies did not support the notion that they were crucial to the onset and progression of CC. These telomere genes may also be utilized in the creation of medications to treat cancer, as they have demonstrated possible anticancer properties. It is anticipated that in the future, these telomere-related factors will serve as novel molecular research targets for the management of CC illness. This study accomplishment establishes a firm theoretical basis for upcoming scholarly discourse and a distinct path for future research endeavors. Declarations Data availability We used TCGA（https://www.cancer.gov/ccg/research/genome-sequencing/tcga）,TelNet（http://www.cancertelsys.org/telnet/） database for providing summary statistics data for the analyses. Funding This work was supported by The National Natural Science Foundation of China, No：82160244 and the key construction disciplines of The First Affiliated Hospital of Dali University. Author Contributions All authors have made significant contributions to this study. Their individual contributions are as follows: Cong Xu: Conceptualized the study, collected and analyzed the data, wrote the original draft of the manuscript. Cong Xu,Yonghong Xu: Participated in study design, assisted in data analysis, provided technical support and methodological guidance, reviewed and revised the manuscript. Cong Xu, Qingcao，Guoling Luo，Jingwen Yu: Provided research materials, participated in data collection, assisted with data processing and analysis, reviewed and revised the manuscript. Guangming Wang: Supervised the entire research process, guided the study design and methodology, reviewed all data analyses and interpretations, led the writing and final revision of the manuscript, and is responsible for manuscript submission and all correspondence. Data Availability Sta tement The datasets generated during and analyzed during thecurrent study are publicly available. Ethical Statement : Given that all data originated from online databases, patients' written informed consent was secured. Moreover, our research relied on open-source data, eliminating any pertinent ethical concerns. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest References Sung H, Ferlay J, Siegel RL, et al . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Telomere Checkpoint in Development and Aging. Int J Mol Sci . 24(21) .10.3390/ijms242115979 (2023). Niebler M, Qian X, Höfler D, et al . Post-translational control of IL-1β via the human papillomavirus type 16 E6 oncoprotein: a novel mechanism of innate immune escape mediated by the E3-ubiquitin ligase E6-AP and p53. PLoS Pathog . 9(8):e1003536. 10.1371/journal.ppat.1003536 (2013). Margul D, Yu C, AlHilli MM. Tumor Immune Microenvironment in Gynecologic Cancers. Cancers (Basel) . 15(15).10.3390/cancers15153849 (2023). Faustino-Rocha AI, Ferreira R, Gama A, Oliveira PA, Ginja M. Antihistamines as promising drugs in cancer therapy. Life Sci . 172:27-41. 10.1016/j.lfs.2016.12.008 (2017). Ao C, Zeng K. The role of regulatory T cells in pathogenesis and therapy of human papillomavirus-related diseases, especially in cancer. Infect Genet Evol . 406-413. 10.1016/j.meegid.2018.08.014 (2018). Mantovani A, Allavena P, Marchesi F, Garlanda C. Macrophages as tools and targets in cancer therapy. Nat Rev Drug Discov . 21(11):799-820. 10.1038/s41573-022-00520-5 (2022). Giese MA, Hind LE, Huttenlocher A. Neutrophil plasticity in the tumor microenvironment. Blood . 133(20):2159-2167. 10.1182/blood-2018-11-844548 (2019). Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol . 14(1):173. 10.1186/s13045-021-01187-y (2021). 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. 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-4640574","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329366585,"identity":"e48159e0-1cf7-4dc0-a3e2-ede2e0e43deb","order_by":0,"name":"Cong Xu","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Xu","suffix":""},{"id":329366586,"identity":"beabdac8-3f95-4026-ae07-9741d830ce2f","order_by":1,"name":"Yonghong Xu","email":"","orcid":"","institution":"Banan Hospital Affiliated to Chongqing Medical University，","correspondingAuthor":false,"prefix":"","firstName":"Yonghong","middleName":"","lastName":"Xu","suffix":""},{"id":329366587,"identity":"989be13f-481e-41e6-8b44-8a1035577771","order_by":2,"name":"Qing Cao","email":"","orcid":"","institution":"Chongqing University Fuling Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Cao","suffix":""},{"id":329366588,"identity":"a654d03f-3b9f-4171-b58d-fcaa109b1c92","order_by":3,"name":"Guoling Luo","email":"","orcid":"","institution":"Chongqing University Fuling Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guoling","middleName":"","lastName":"Luo","suffix":""},{"id":329366589,"identity":"981a5434-fb92-42be-9c74-7460576008b1","order_by":4,"name":"Jingwen Yu","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Yu","suffix":""},{"id":329366590,"identity":"d359ef1d-7df7-4d21-85bd-35c374209de9","order_by":5,"name":"Guangming Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJCCAx8qJOQYDjA3HGBgYCZKB+PBGWcsjBkOMBKvhfkwb1tFYgNQCwNRWgzOr04AapFI7zt+sPEAQ4V1YgP72QP4tdx4u+HgnHMSuTPPAC1iOJOe2MCTl4BXi9mNsxsOvCmTyN1wAOS2tsOJDRI8BoS18LBJpBucfwjU8o8YLed7NxzkaZNIMLgBDgEitNjf4N0ADGQJw5k3gLYkHEs3buPJwa9Fsv/s5g8fKurk+c4nH/7wocZatp/9DH4tDBIJSBwQmw2/eiDgP0BQySgYBaNgFIx0AAAJtVhQqyoy8QAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Dali University","correspondingAuthor":true,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-26 07:02:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4640574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4640574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60812011,"identity":"6cdfd3b7-fec3-4593-bdb5-f0421b46a486","added_by":"auto","created_at":"2024-07-22 11:00:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2152781,"visible":true,"origin":"","legend":"\u003cp\u003eTelomere genes significantly associated with cervical cancer\u003c/p\u003e\n\u003cp\u003e(A) Differential expression and stratified clustering of telomere genes in normal and cervical cancer tissues in TCGA database.\u003c/p\u003e\n\u003cp\u003e(B) Telomere differential gene volcano map describing high and low risk.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/6b75fd522e487e931120f5e1.png"},{"id":60812715,"identity":"d380c8fb-c4b6-4324-b86d-cb48a246abb0","added_by":"auto","created_at":"2024-07-22 11:08:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2180253,"visible":true,"origin":"","legend":"\u003cp\u003eTelomere core genes and GO enrichment analysis associated with cervical cancer\u003c/p\u003e\n\u003cp\u003e(A) KEGG enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(B) BP enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(C) CC enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(D) MF enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(E) Interaction of core genes associated with cervical cancer.\u003c/p\u003e\n\u003cp\u003e(F) The top 10 hub genes in the PPI network.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/03f91d3556a6320666db42c9.png"},{"id":60812009,"identity":"bee6d973-a845-4de0-b63d-d33318d35e9c","added_by":"auto","created_at":"2024-07-22 11:00:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":577582,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of telomere genes on survival associated with cervical cancer\u003c/p\u003e\n\u003cp\u003e(A) LASSO regression coefficient.\u003c/p\u003e\n\u003cp\u003e(B) partial likelihood deviation of the noose at different logarithmic λ values.\u003c/p\u003e\n\u003cp\u003e(C) Expression of telomere core genes associated with cervical cancer between cervical cancer and normal tissues. The red represents the tumor group and the blue represents the normal tissue group.\u003c/p\u003e\n\u003cp\u003e(D) Kaplan-Meier (KM) survival analysis of genes associated with cervical cancer. The lower axis in the KM analysis diagram indicates the number of samples corresponding to different risks and survival times.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/5d5a7a57c549521f5d0916e4.png"},{"id":60812008,"identity":"4e403e94-5154-435c-9f9b-01970ac83b79","added_by":"auto","created_at":"2024-07-22 11:00:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1052807,"visible":true,"origin":"","legend":"\u003cp\u003eRisk score model prognostic analysis,\u003c/p\u003e\n\u003cp\u003e(A) Univariate Cox regression analysis of risk scoring model.\u003c/p\u003e\n\u003cp\u003e(B) Multivariate cox regression analysis of risk scoring model.\u003c/p\u003e\n\u003cp\u003e(C) Time-dependent ROC curves for validation of cervical cancer-associated telomere genes.\u003c/p\u003e\n\u003cp\u003e(D) ROC curves for validation of clinical information and risk scores for cervical cancer-associated telomere genes.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/cdb7e3c1ed23af59e6957ec0.png"},{"id":60812002,"identity":"c8921499-dc5a-4839-b8f8-c357384e356c","added_by":"auto","created_at":"2024-07-22 11:00:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1215281,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and verification of the nomogram\u003c/p\u003e\n\u003cp\u003e(A) The nomogram predicts the overall survival rate of cervical cancer patients.\u003c/p\u003e\n\u003cp\u003e(B) the calibration curve predicts the 1, 3, and 5-year overall survival rate.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/0671b327489c37a629a75cf7.png"},{"id":60811999,"identity":"c28a6970-e182-4301-ade2-f52723f36413","added_by":"auto","created_at":"2024-07-22 11:00:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1094165,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive immunoassay.\u003c/p\u003e\n\u003cp\u003e(A) Spearman analyzed the differential expression of 20 immune cells and each gene. (B) Difference analysis of 20 types of immune cells in the TCGA cohort. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001 and ns, the difference was not statistically significant.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/5272f8208dd01d46189c659d.png"},{"id":60811997,"identity":"04e76f09-f425-42af-a7e5-a2b99924860f","added_by":"auto","created_at":"2024-07-22 11:00:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":573550,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis of telomere genes associated with cervical cancer\u003c/p\u003e\n\u003cp\u003e(A)-(F) Drug sensitivity analysis. Correlation between telomere genes associated with cervical cancer and drug sensitivity.\u003c/p\u003e","description":"","filename":"Figure7png.png","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/dfbee863f2b0bad7876ffe79.png"},{"id":66847251,"identity":"890fb988-9861-474d-b59b-fdfe0f1b30bd","added_by":"auto","created_at":"2024-10-17 06:17:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9508858,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4640574/v1/073bb511-be22-4d5b-b8e9-a3b0a8141b3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of telomeres in cervical cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe incidence of cervical cancer is about twice as high and the death rate is three times greater in low- and middle-income nations than in high-income ones\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This is mainly because there is a serious imbalance in health care resources among low-income populations. Cervical cancer is one of the most important malignant tumors that threaten women's lives, especially in developing countries. According to the Global Cancer Report 2020, there are 530,000 new cases of cervical cancer and 250,000 deaths worldwide each year, of which 85% are in low - and middle-income countries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. There's a steady decrease in cervical cancer cases in Western developed nations, attributed to the widespread use of the HPV vaccine. Despite this, developing countries with low and middle incomes exhibit a significantly elevated incidence of cervical cancer, primarily due to various factors such as uneven healthcare resource availability, reliance on a sole funding source, and insufficient legislative backing\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In order to eradicate cervical cancer worldwide by 2130, the World Health Organization launched a plan in November 2020. The primary goal of this strategy is to lower the global incidence of cervical cancer to less than 4 cases per 100,000 women yearly. Several tactics will be used throughout this period to reduce the incidence of cervical cancer. With this strategy, 90% of girls under the age of 15 will receive immunizations, and 70% of women between the ages of 35 and 45 would receive precise screening\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. There is a need to develop new treatment approaches because relying just on current diagnostic and therapeutic techniques is insufficient to meet this goal.\u003c/p\u003e \u003cp\u003eEnhancing the telomere length maintenance mechanism (TMM) is essential for attaining cell immortality since telomere shortening linked to regular cell proliferation is a major cause of cell death. Repetitive DNA is found in human telomeres, typically in tandem arrays of the hexonucleotides 5'-TTAGGG-3'\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The telomere's tail consists of 25\u0026ndash;200 single-stranded DNA nucleotides, whereas the remaining telomere primarily consists of Watson-Crick base pairs, creating double-stranded DNA. Nonetheless, chains abundant in G can engage in Hoogsteen base pairing, creating a flat G-quartet formation, potentially safeguarding telomeres against DNA repair. Another sophisticated formation, the T-ring, plays a role in safeguarding telomeres against the impacts of DNA repair\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.Scholarly concern over the regulatory function of telomere genes has grown in recent years. In this work, the regulatory mechanism of telomere genes was taken into consideration when employing bioinformatics techniques to investigate the involvement of telomere genes in cervical cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Human cells reproduce endlessly following particular genetic and epigenetic modifications, leading to a pathological situation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. On the rare occasions that human cells escape the crisis, these cells almost universally express the ribonucleoprotein telomerase and maintain stable but short telomeres\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although telomerase does not drive the carcinogenic process, it is necessary for the sustained growth of most advanced cancers. The study of the interaction between telomeres and telomerase has become a hot topic in cancer treatment\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2.Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eCC transcription omics information and clinical characteristics in patients, comprising 306 tumor samples and 3 normal samples, with the cancer genome database (TCGA\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.GDC.Cancer\u003c/span\u003e\u003cspan address=\"https://portal.GDC.Cancer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Gov). Clinicopathological information, such as gender, age, stage status, TMN type, survival status, and survival period, as well as mRNA expression of CC patients were acquired. Matrix information and full pathology information for every clinical sample were extracted using Strawberry Perl.And telomere gene data is derived from TelNet website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Differential expression of telomere related genes and screening prognostic related genes\u003c/h2\u003e \u003cp\u003eThe unpaired T-test was utilized to determine the gene's P-value using the limma package in R. A threshold of \u0026lt;\u0026thinsp;0.05 was established for the P-value. To investigate the data structure and pattern further, we might examine the differences in the expression of telomere-related genes between normal tissues and tissues from cervical cancer. The heat map and volcano was mapped using \"pheatmap\", \"survminer, survival\" to identify the telomere genes that were significantly related to prognosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUtilizing STRING's protein-protein interaction (PPI) feature (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), each clinically significant module was equipped with a PPI network for potential biomarker genes, which was then visualized using R. Additionally, gene and genome ontology enrichment (GO) is facilitated by Kyoto Encyclopedia (KEGG pathway analysis) and the INPUT2.0 website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbcb.cdutcm.edu.cn/INPUT/\u003c/span\u003e\u003cspan address=\"http://cbcb.cdutcm.edu.cn/INPUT/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Development and validation of prognostic features associated with telomeres\u003c/h2\u003e \u003cp\u003eNext, we employed the \"glmnet\" R package to do LASSO regression analysis in order to determine the important genetic prognosis of telomeres in cervical cancer. The expression value of considerably differently expressed telomere genes in cervical cancer was represented by Xi, the regression coefficients in lasso analysis were marked as Coefi, and the number of significantly differentially expressed telomere genes in prognosis was represented by n. Using the median risk score as a guide, patients were categorized into high-risk and low-risk categories\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The \"Survival\" software program in R was then used to do a Kaplan-Meier survival analysis on these groups in order to evaluate the sensitivity and specificity of risk. R packets such \"reshape2\" and \"BiocManager\" were also used to map the differential expression results of these important genes in the tissues of cervical cancer and normal cervical tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Roc curve analysis and risk scoring model\u003c/h2\u003e \u003cp\u003eWith the help of the \"survival\" R package, the prognostic value of telomere-related genes was assessed using univariate and multifactorial Cox regression. The telomere genes linked to the prognosis of cervical cancer were then further examined, and a corresponding forest map was created in order to determine whether changes in telomeres are an independent risk factor. It affects CC patients' chances of survival. Next, we generate the ROC curve using the R software's \"timeROC\" toolset. The nomogram's prognostic impact was estimated using the area under the ROC curve (AUC). Risk ratings and clinical criteria were combined to construct a nomogram using the \"survival\" and \"rms\" packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5CIBERSORT immune cell composition analysis, difference analysis and genetic correlation analysis\u003c/h2\u003e \u003cp\u003eThe high-risk and low-risk groups' immune cell expression differed significantly from one another. Therefore, it's possible that immune cell dispersion varies among disorders. We conducted a thorough differential expression analysis to assess immune cell profiles in patient groups at high and low risk. The foundation for the development of the CIBERSORT program is the extraction of baseline gene expression from immune cells. A linear model is then used to forecast the quantity of immune cells in a sample, and sequencing tests are employed to evaluate the relevance of the outcomes. Afterwards, we visualized the variations in immune cells between cervical cancer and control samples using the packages limma, reshape2, and ggpubr. The main function of these packs is to correlate immune cells with their genes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Drug sensitivity\u003c/h2\u003e \u003cp\u003eUtilizing the tumor susceptibility to multiple omics (GDSC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain gene expression data connected to cancer associated with various medications. The \"pRRophetic\" R program can be used to assess the individual telomere gene response of cancer patients to various chemotherapy medications\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1DEG screening and PPI enrichment analysis\u003c/h2\u003e \u003cp\u003eAfter data preprocessing, a total of 327 DEGs were identified, of which 240 were up-regulated and 87 were down-regulated. The heat map clearly shows the CC and control groups. And The high and low risk genes were analyzed, and the telomere genes were found to be significantly related to the prognosis. The GO and KEGG analyses revealed the role of DEGs in the pathogenesis of CC. GO analysis (Figure\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B)reveals that marker gene products in biological processes are mainly enriched in DNA replication, DNA\u0026thinsp;\u0026minus;\u0026thinsp;templated DNA replication, regulation of cell cycle phase transition, double\u0026thinsp;\u0026minus;\u0026thinsp;strand break repair and telomere organization processes. Marker gene products are located inside cells catalytic activity acting on DNA, ATP\u0026thinsp;\u0026minus;\u0026thinsp;dependent activity acting on DNA, helicase activity, single\u0026thinsp;\u0026minus;\u0026thinsp;stranded DNA helicase activity, DNA helicase activity and other pathways are significantly enriched. Gene products are enriched in chromosomal region, nuclear chromosome, chromosome, telomeric region, protein\u0026thinsp;\u0026minus;\u0026thinsp;DNA complex, replication fork, etc.༈Figure\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA༉In addition, as shown in the figure, gene KEGG analysis showed that candidate genes were mainly enriched in Cell cycle, DNA replication, Cellular senescence, double\u0026thinsp;\u0026minus;\u0026thinsp;strand break repair, Fanconi anemia pathway. Pathway enrichment analysis showed that candidate biomarkers were mainly related to cell cycle. Abnormal function of these genes may lead to abnormal cell synthesis and apoptosis cycles in the body, and promote the development of cervical ༈Figure\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D༉cancer\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 30 telomere genes most closely related to cervical cancer were screened to form the PPI network complex. These genes are ABCC9, ADK, ALDOA, AMPH, AR, ARF6, ARHGAP27, ARPC5L, ARRDC1, ASF1B, ATAD2, ATAD5, AURKA, AURKB, BAIAP2L1, BLM, BRCA1, BRCA2, BRIP1, and so on. BRMS1, BUB3, C17orf49, CALD1, CAMK1, CCDC137, CCNA2, CCNB1, CCNE1, CCNE2, CCT5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction of prognostic markers for cervical cancer\u003c/h2\u003e \u003cp\u003eWe combined the expression groups of telomeres associated with cervical cancer and clinical follow-up information on CC. Through LASSO regression analyses, we identified 27 genes that showed correlation with the prognosis of cervical cancer, among which 13 genes inhibited the development of cervical cancer, namely ALDOA, CALD1, DPP3, DSG2, EIF4B, GAPDH, LYPLA1, ALDOA, DPP3 and DPP3. MAP7, MYO10, PAICS, PKM, TFRC and TLN1 (Figure\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). There are 14 genes that promote the development of cervical cancer, including CHAF1B, DDX39A, FOXN1, JUND, LAGE3, MCM5, PAFAH1B3, PCP4, POLA2, RFC5, RNASEH2A, SH3BP1, TP73 and TSPYL2. Next, LASSO analysis was used to narrow down the range of prognostic genes. With the minimum partial likelihood bias and mean square\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe log (λ) value corresponding to error is taken as the optimal value (graph). Then, according to the coefficient corresponding to the optimal log (λ) value, the prognostic characteristic genes were determined, and finally 16 genes were selected(Figure\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These 16 genes' differential expression in cervical cancer and healthy tissues was examined ༈Figure\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). It is evident from the Lasso regression results that the distinctive genes significantly influence CC and enhance the performance of interpretation and prediction of the occurrence of cervical cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk models for accurate evaluation of patients with CC\u003c/h2\u003e \u003cp\u003eKaplan-Meier curve-based survival studies revealed that individuals in the high-risk category experienced notably poorer results(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) The findings indicate the precision and predictive power of this risk model for patients with CC. Patients at elevated risk exhibited increased death rates and poorer survival outcomes. This research also employed both univariate and multivariate Cox regression to investigate the link between clinical information and the risk assessment of cervical cancer patients in the TCGA database, along with their prognosis. After in-depth analysis, we found that risk score was indeed an independent factor for the prognosis of cervical cancer patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while in contrast, age and grade of cervical cancer patients did not significantly affect their OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,B).Utilizing clinical data and risk scores from cervical cancer patients in the TCGA database, the ROC curve was created, revealing the AUC value of risk score was =\u0026thinsp;0.778 and the AUC value of age was 0.564. The ROC analysis over time demonstrates the model's forecasting accuracy, exhibiting AUC figures of 0.778, 0.787, and 0.769 for survival rates at 1, 3, and 5 years, in that order (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC,D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Column line diagram construction and evaluation\u003c/h2\u003e \u003cp\u003eBased on the data from the Cox regression analysis, we created a stable nomogram for predicting OS in patients with CC, taking into account riskscore, age, and grade. Using a column chart, we can provide an accurate numerical assessment of survival or risk for each patient with CC, reducing complex regression equations to intuitive graphical representations. In the cohort prediction of TCGA, the AUC values of the 1, 3 and 5 year OS predicted by this model are 0.541 0.63 0.907 respectively\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The sub-molecular characterization identification for cervical cancer can be used in clinical diagnosis or prediction of disease risk or prognosis by combining multiple indicators(Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA,B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation analysis between risk score and immune response\u003c/h2\u003e \u003cp\u003eThe heat map illustrates how the proportion of immune-infiltrating cells varies in samples classified as high-risk versus low-risk. T cells regulatory (Tregs), dendritic cells resting (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and mast cells resting (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the immune cells with the highest infiltration rate in the low-risk group. T cells with CD8, these three cells had significantly lower expression levels in the high expression group compared to the low expression group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In comparison to the low expression group, there was a significant increase in mast cell activation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and macrophage M0 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the high expression group. It is evident that various immune infiltrates can impact a patient's telomeres, which in turn can alter prognostic markers and immunotherapy targets for cervical cancer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. (Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we mapped the correlated heat maps of differential genes and immune cells to determine the effects of differential genes on immune function in CC patients. The correlation analysis resultsshowed that CHAF1B was significantly negatively correlated with Neutrophils. DSG2 was negatively correlated with B cells memory and T cells gamma delta, and positively correlated with T cells CD4 memory resting. FOXN1 was positively correlated with Dendritic cells resting and Macrophages M1. GAPDH was positively correlated with Mast cells activated and negatively correlated with Mast cells resting. There was significant positive correlation between JUND and Monocytes. There was a significant positive correlation between MAP7 and Mast cells activated and T cells CD4 memory resting. PCP4 was positively correlated with B cells naive. PKM was positively correlated with Mast cells activation. There was a significant positive correlation between RFC5 and T cells CD4 memory resting. SH3BP1 was significantly positively correlated with Dendritic cells resting, Mast cells resting and T cells CD4 memory activated, but significantly correlated with B cells naive, T cells CD4 memory resting was negatively correlated. TFRC and Mast cells activated, T cells CD4 memory resting. TP73 is positively correlated with Dendritic cells resting and has significant significance. TSPYL2 was significantly positively correlated with B cells naive, and negatively correlated with Neutrophils(Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Results of drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eOne of the main issues is that people with cervical cancer are increasingly becoming resistant to the medications used in chemotherapy. For this reason, choosing the right chemotherapy medications is crucial to the customized care of patients with stomach cancer\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Subsequently, anti-cancer medications were chosen for this study in order to evaluate drug susceptibility in both high-risk and low-risk populations. Furthermore, it was discovered that patients with high scores had increased drug sensitivity values for PF\u0026thinsp;\u0026minus;\u0026thinsp;4708671, Pyridostatin, Ribociclib, Venetoclax, and AZD6482. When combined, these findings imply that telomere genes have a role in drug sensitivity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, further experiments are needed to confirm these results. Moreover, the relationship between these drugs and the risk scores of cervical cancer cells telomere genes was significant and positively correlated. (Figure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-F)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. discussion","content":"\u003cp\u003eBecause of the continued lack of significant declines in its incidence, mortality, or disability rates, cervical cancer is becoming an increasingly serious public health concern. While there have been some studies on the causes of cervical cancer, it is important to comprehend its pathogenesis and inducers, which include immunological microenvironment, environmental variables, and genetic factors, given the multifaceted character of cervical cancer. Although a wide range of topics have been studied in the present study on this illness, our knowledge of these mechanisms is still extremely diverse. Consequently, a critical component of the creative development of cervical treatment in the future will be the thorough investigation of the underlying concept\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis research involved examining the TCGA database to assess the transcriptomic variances in telomere genes between cervical cancer specimens and control samples. Through analysis and validation using R software, we identified 327 genes with telomere differences primarily linked to CC, noting that their expression levels may increase or decrease in CC. Through PPI, GO, and KEGG enrichment analysis, the functioning of these crucial genes was examined, leading to the exclusion of 16 key gene composition and prognosis models and an assessment of how changes in telomere genes affect cervical cancer patient survival rates. It was also discovered that they have a close connection to the immune system's response. When integrated with immune cells, this confirmation facilitates additional studies on telomeres in cervical cancer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe enrichment analysis of core genes associated with telomeres revealed that these genes are mostly engaged in cell cycle regulation, double-strand break repair, aging of cells, and catalytic activity and replication of DNA. According to research, telomeres eventually deteriorate and exhibit a randomly diminishing pattern during the course of several cell divisions. Telomere shortening frequently results in cell cycle arrest, which sets off the apoptotic process. As a result, research on telomeres is crucial for treating tumors. Previous research has shown that certain age-related processes and human aging illnesses have higher total telomere shortening\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA malfunction in the telomere nucleoprotein complex could expose free chromosome ends to DNA double-strand breaks (DSBS) repair, leading to the fusion of telomere and non-telomere loci. Such irregularities could contribute to the emergence of tumors. The development of cancer is attributed to the stability of replicated telomere arrays, aiding tissue cells in attaining immortality\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Attaining this objective is possible through the activation of telomerase or by triggering different telomere elongation (ALT) routes. Consequently, the reduction in telomere length may be viewed as a strategy to inhibit tumors. Alterations in telomeres, triggered by genetics, either elevate or diminish the likelihood and advancement of cancer in distinct manners\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For example, the mechanism of telomere progression in liver cancer has been detailed in the literature.\u003c/p\u003e \u003cp\u003eAccumulating research uncovers a process aligning the cell's destiny with the telomere's condition. This paper delves into the destiny of telomeric RNA within cells and the processes contributing to its initial growth\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCervical cancer development is significantly influenced by the immune microenvironment. Presently, it's theorized that tumor cells play a role in various distinct molecules that regulate autoimmune responses. The E7 oncoprotein is capable of diminishing the pro-inflammatory immune signaling route through the interruption of toll-like receptor 9 TLR9 and cGAS-STING pathways. Studies indicate that E6 and E7 disrupt interferon signaling, diminishing the NF-kB pathway and interleukin1β production, all contributing to harm to the host's natural immune response\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Oncoproteins are further involved in the downregulation of major histocompatibility complex (MHC) class I by binding to MHC I promoters\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This increases the likelihood that the HPV virus linked to cervical cancer may infiltrate the body, which is also a major contributing cause to the dismal prognosis of head and neck cancer. The levels of CCL20 may be lowered by blocking these inflammatory pathways. By inhibiting or lessening the aforementioned pathways, it may be possible to avoid or mitigate tumor spread and recurrence during treatment. As a chemokine, CCL20 helps to prevent infection, hemostasis, cell proliferation, and remodeling. It also attracts antigens in the form of delivery cells, such as Langerhans cells, to the virus-infected area\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Immunoinfiltrating data showed that telomere core genes associated with cervical cancer were associated with Mast cells resting, Dendritic cells resting, T cells regulatory (Tregs), T cells CD8, Mast cells activated, Macrophages, Neutrophils, B cells memory, T cells gamma delta, Monocytes and other immune cells were significantly correlated. It has been documented that mast cells contribute to the expansion of blood vessels during the creation and discharge of histamine, a biogenic amine, in various pathological conditions, including allergic responses and conjunctivitis. Histamine may intensify inflammation, leading to heightened capillary permeability and disorders of lymph reflux. Elevated histamine concentrations are linked to multiple tumor types such as cervical, ovarian, vaginal, uterine, vulvar, and colorectal cancers, aiding in the suppression of their proliferation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of Tregs in HPV infection is to protect tissue from immune-mediated damage at different anatomical subsites\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, macrophages play a key role in the tumor microenvironment, where they have significant effects on blood vessel formation, extracellular matrix remodeling, cancer cell growth, metastasis, and immunosuppressive synergies, as well as on chemotherapeutic drugs and checkpoint blocking immunotherapy resistance. Studies have shown that macrophages have phagocytic function and can produce cytokines such as antibodies, and these immune responses are closely related to the body's immune system. Macrophages have the function of mediating cancer cell phagocytosis and cytotoxic tumor killing, so they have become the main target of cancer therapy\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Neutrophils play a significant function in the tumor immunological microenvironment. The body uses neutrophils as its first line of defense against infections and reacts to many inflammatory symptoms, including cancer\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In the context of cancer, neutrophils might be reclassified as agents that encourage cancer, suggesting their adaptability and ability to control their activity in diverse inflammatory settings. Latest studies have revealed a complex and nuanced interplay between cancerous cells and healthy cells\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe telomere genes linked to cervical cancer have been effectively identified by comprehensive bioinformatics research. This study examines the various paths by which these telomere genes can affect cervical cancer, even though a number of previous studies did not support the notion that they were crucial to the onset and progression of CC. These telomere genes may also be utilized in the creation of medications to treat cancer, as they have demonstrated possible anticancer properties. It is anticipated that in the future, these telomere-related factors will serve as novel molecular research targets for the management of CC illness. This study accomplishment establishes a firm theoretical basis for upcoming scholarly discourse and a distinct path for future research endeavors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used\u0026nbsp;TCGA（https://www.cancer.gov/ccg/research/genome-sequencing/tcga）,TelNet（http://www.cancertelsys.org/telnet/）\u0026nbsp;database for providing summary statistics data for the analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;The National Natural Science Foundation of China, No：82160244 and the key construction disciplines of The First Affiliated Hospital of Dali University.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors have made significant contributions to this study. Their individual contributions are as follows:\u003c/p\u003e\n\u003cp\u003eCong Xu: Conceptualized the study, collected and analyzed the data, wrote the original draft of the manuscript.\u003c/p\u003e\n\u003cp\u003eCong Xu,Yonghong Xu: Participated in study design, assisted in data analysis, provided technical support and methodological guidance, reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eCong Xu, Qingcao，Guoling Luo，Jingwen Yu: Provided research materials, participated in data collection, assisted with data processing and analysis, reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eGuangming Wang: Supervised the entire research process, guided the study design and methodology, reviewed all data analyses and interpretations, led the writing and final revision of the manuscript, and is responsible for manuscript submission and all correspondence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Sta\u003c/strong\u003etement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analyzed during thecurrent study are publicly\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eavailable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven that all data originated from online databases, patients\u0026apos; written informed consent was secured. Moreover, our research relied on open-source data, eliminating any pertinent ethical concerns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eor financial relationships that could be construed as a potential conflict of interest\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, \u003cem\u003eet al\u003c/em\u003e. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 71(3), 209-249. 10.3322/caac.21660 (2021)\u003c/li\u003e\n\u003cli\u003eWang R, Pan W, Jin L, \u003cem\u003eet al\u003c/em\u003e. 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Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model.\u003c/p\u003e \u003cp\u003eWithin the discovered TCGA group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients.\u003c/p\u003e","manuscriptTitle":"The effect of telomeres in cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 11:00:38","doi":"10.21203/rs.3.rs-4640574/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":"5be9484c-eba4-4b4e-a215-4cd9ab1aba1d","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-17T06:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 11:00:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4640574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4640574","identity":"rs-4640574","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}