Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer

Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer Zhengchao Yan, Sijuan Tang, Naqiu Yin, Ying Wan, Lili Su, Minhui He, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8553093/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 Transient Receptor Potential (TRP) channels are hypothesized to be associated with cancer progression. This study aimed to develop a prognostic model for cervical cancer (CESC) utilizing genes related to TRGs. Methods The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) libraries were employed for determining the training and validation cohorts, respectively. Transcriptome profiles, clinical data, and copy-number variation (CNV) datasets have been obtained from people suffering from cervical squamous cell carcinoma (CESC). Lasso-Cox regression analysis was used to determine the -risk score based on predicting gene expression levels, and survival analysis was used to ascertain the overall difference in survival between the high- and low-risk groups. Single-cell sequencing RNA information from the TISCH database was analyzed using the Seurat software. The software suites GSVA, ClusterProfiler, and IOBR were utilized for functional phenotypic analysis. The patients were split into two groups using consensus clustering. The clinicopathological features were then compared, and an investigation of biological function was carried out. Applying the Kaplan-Meier curve along with the log-rank test, the predictive value of genes was ascertained. The immune situation was the focus of the ensuing inquiry. Additionally, we looked at the connections between the tumor microenvironment adjustment, gene functional enrichment analysis, and TRGs. Results Ten TRP channel genes (TRGs) were included in a predictive risk model. Patients classified into various risk groupings exhibited notable differences in molecular characteristics and clinical symptoms. In particular, the high-risk group had a dire outlook and a higher cancer mutation burden (TMB). Single-cell RNA sequencing (scRNA-seq) analysis results pointed out that high-risk and low-risk cell populations differed significantly in numerous variables. In order to deeper comprehend the molecular regulatory processes underpinning risk subtypes, our study established an aggressive endogenous RNA (ceRNA) protective network. When regarded as a whole, TRG-related gene targeting might serve as a potential therapy approach for cervical cancer (CC). Conclusion We have successfully established a high-precision prognostic model for predicting overall survival and treatment efficacy using TRP channel-related genes. Cervical Cancer TRGs Prognosis Model Molecular Subtypes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Cervical cancer is a malignant neoplasm affecting the female cervix and constitutes one of the prevalent malignancies in gynecological practice. Persistent infection with the human papillomavirus (HPV), particular high-risk subtypes like HPV 16 and HPV 18, is the primary manifestation. Among female cancers, cervical cancer is associated with the fourth- highest incidence and fatalities nationwide[ 1 ]. In addition to the creation of cervical cancer vaccinations and the widespread adoption of screening programs, major reductions in the disease's incidence and mortality have been attained in comparison to earlier times. However, there is a growing trend toward younger age at onset, this putting young women's health and quality of life at dangerous levels[ 2 ]. Benefiting from advancements in early detection methodologies, surgical approaches, and molecular targeted therapies, the overall mortality rate has remained stable over the past five years across all genders. Despite these progresses, multiple aspects of cervical cancer’s pathophysiological mechanisms remain inadequately elucidated, and the functional roles of numerous biomarkers associated with distinct stages of cervical carcinogenesis are yet to be clarified. Although therapeutic protocols have been optimized, the overall survival rate of patients with CESC remains less than ideal, largely attributable to disease recurrence, metastasis, and drug resistance. Consequently, the identification of novel therapeutic targets is of critical importance for retarding disease progression and enhancing prognostic outcomes. In 1969, Cosens and companions reported a visual genetic change in Drosophila melanogaster, eventually led to the initial identification of transient receptor potential (TRP) channels[ 3 ]. These channels are crucial for perceiving external stimuli (such as temperature, chemicals, and mechanical force) and modulating cellular functions[ 4 ]. Despite decades of research on TRP channels, their specific roles in physiological and pathological processes remain partially unclear. Mounting evidence indicates that abnormal expression or functional impairment of TRP channels are detected in various diseases[ 5 ]. Notably, it is predicted that modulating TRP channel activity could lead to fresh approaches and opportunities for the treatment and prevention of crucial disease. Transient receptor potential (TRP) channel-related genes (TRGs) are aberrantly expressed during the tumorigenesis and metastasis of various types of cancer, according to mounting evidence. This dysregulation may be crucial in promoting the spread and proliferation of cancer cells. A steadily increasing amount of knowledge is showing that dysregulated expression of TRPV1 is a significant contributor to several types of carcinoma of the female reproductive system, such as endometrial, breast, and cervical cancer[ 6 ]. Further, TRP channel-related genes impact prostate cancer treatment outcomes[ 7 ]. Given that TRGs are widely expressed in the liver, they not only serve as key regulators of intracellular cation concentrations but also act as initiators or intermediates in specific signaling pathways that contribute to the pathogenesis of liver diseases[ 8 ]. Based on these findings, we established a TRP-related predictive model, with the aim of elucidating the mechanisms through which TRP channel-related genes modulate the survival prognosis of cervical cancer (CC) patients and providing novel insights for the targeted treatment of this malignancy. 2. Materials and Methods 2.1 Data Collection and Processing The Cancer Genome Atlas (TCGA) public database's official portal ( https://portal.gdc.cancer.gov ), which is administered by the National Cancer Institute (NCI) in the United States, submitted helpful data on cervical squamous cell carcinoma (CESC) regarding this study[ 9 ]. The dataset encompassed 3 adjacent normal tissue samples and 306 CESC patient tumor samples. The compiled clinical information included key details such as Tumor, Node, Metastasis (TNM) staging, along with patient age, gender, and tumor grade (refer to Table S1). During TCGA data processing, the RNA-seq data were normalized using the log2(TPM + 1) transformation method.To screen for genes whose expression levels are associated with sodium overload, we conducted a search on the GeneCards platform ( https://www.genecards.org/ ), a comprehensive human gene database. A total of 2206 relevant genes were discovered through this search, including genes the fact that are directly involved in sodium transport processes (which include ion channels, exchangers, and pumps) as well as a number of downstream regulatory factors and genes relating to the physiological effects of disturbed sodium homeostasis[ 10 ].For further experimental validation, we utilized datasets from the Gene Expression Omnibus (GEO) repository, accessible at http://www.ncbi.nlm.nih.gov/geo . 300 tumor samples' clinical and RNA sequencing data have been maintained in the pertinent Gene Expression Omnibus (GEO) databases; Table S2 provides a summary of the query genes. 2.2 Locating Differentially Expressed Genes Relevant with CESC The “limma” software package within R (version 4.3.1) was applied to detect significant differences in TRG expression levels between CESC specimens and their corresponding adjacent normal tissues.Prior to incorporating TRGs into a one-way model, genes with prognostic significance were filtered from the TCGA dataset using the following criteria. Our work utilised the R language's vioplot package to generate on its own volcano plots and heatmaps for displaying the variations in gene expression patterns in order to shed light on the variation in the expression of TRG between cervical squamous cell carcinoma (CESC) tissues and surrounding normal tissues. 2.3 Molecular Isoforms of Multiple Differentially Expressed Genes (TRGs) Concurrent combined with Sodium Channels: Consensus Clustering Genes from the TRG set that exhibited notable prognostic significance were filtered out through univariate Cox regression analysis. In the CESC patient cohort, two distinct molecular subtypes were classified using the ConsensusClusterPlus package in R. The optimal cluster number was chosen by evaluating cumulative distribution function (CDF) curves and consensus clustering heatmaps, with the final number of clusters set at K = 2. For visualizing the analytical results, we employed the ggplot2 package (version 3.5.2) in R. To investigate distinctions between overall survival (OS) among the different TRG-based subtypes and confirm if these subtypes may function as independent prognostic indicators, the survival package (version 3.8.3) inside R was utilized. The OS outcomes between the two patient groupings were compared using survival curves generated by Kaplan-Meier analysis. Clinical factors (such as tumor grade, patient age, and survival status) were analyzed between the two groups using the chi-square test. Additionally, heatmaps showing the connections between clinically significant characteristics and gene expression profiles across the TRG subgroups were created by R's ComplexHeatmap package (version 2.22.0). 2.4 Evaluation and Inspection of Prognostic Importance This study executed multivariate Cox proportional hazards regression analysis to investigate the relationship between gene expression profiles and clinical prognostic data in the CESC cohort from The Cancer Genome Atlas (TCGA) database in order to elucidate the link between and independent prognostic value of the transient receptor prospective channel genes (TRGs) with Overall Survival (OS) for people with cervical squamous cell carcinoma (CESC). In order to reduce the effect of accidental errors on model performance, the CESC gene expression data and related clinical data that were obtained from the TCGA database were sampled at random and partitioned 1000 times. Each partition has been split into a set to be trained and a test set. Using the R package glmnet, the reduction in dimensionality investigation of TRGs performed performed by Utilizing the feature selection advantage of the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. A smaller list of candidate genes with strong predictive significance was ultimately removed by shrinking coefficients applying a penalty function. Ten core target genes and their satisfactory regression coefficients remain intact after a LASSO Cox prognostic prediction model was built using these genes. 2.5 Single-Cell Omics At the single-cell resolution, single-cell omics techniques can dissect the cellular composition, functional heterogeneity, and intercellular communication networks within the tumor microenvironment (TME) of cervical cancer. This offers an indispensable foundation for investigating the onset and progression of cervical cancer, unraveling drug resistance-related mechanisms, and exploring candidate therapeutic targets. Single-cell RNA sequencing (scRNA-seq) data of CESC tissues were obtained from the Gene Expression Omnibus scRNA-seq dataset (GSE168652)[ 11 ], which has been deposited in the public Tumor Immune Single Cell Hub (TISCH) database ( http://tisch.comp-genomics.org/home/ )[ 12 ]. 2.6 Enrichment Analysis The R language's limma library (version 3.62.2) was utilized for performing differential expression analysis. The filtered differentially expressed genes underwent enrichment analysis utilising Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Furthermore, the R package GSVA (version 2.0.7) was used to implement Gene Set Variation Analysis (GSVA). The Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb ) [ 13 ] supplies the gene sets that were employed in this investigation. In meanwhile, the pathway variants of the two groups were explored using Gene Set Enrichment Analysis (GSEA). 2.7 Immune Cell Infiltration Analysis The ESTIMATE algorithm was employed to compute the ESTIMATE score, which was used to characterize the immune infiltration status among different groups[ 14 ]. Further, the CIBERSORT and ssGSEA algorithms were applied to evaluate the immune cell infiltration profiles of each group. Based on machine learning principles, the CIBERSORT algorithm allows for high-throughput analysis of multiple cell types. By utilizing tools like limma, this algorithm focused on quantifying 22 distinct immune cell subsets and comparing their distribution patterns between the two risk subgroups. 2.8 Gene Mutation Analysis The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ) was employed to extract physical SNV and CNV data. The top 15 most commonly mutated genes were exposed to mutation analysis using the R package maftools (version 2.22.0), with the results represented as waterfall plots. 2.9 Construction of Predictive Nomogram To project the survival outcomes of CESC patients, a forecasting nomogram was created using the R package rms, incorporating clinically important variables and risk scores. 2.10 Construction of the ceRNA Regulatory Network The competing endogenous RNA (ceRNA) argument states that some long non-coding RNAs (lncRNAs) could contend with specific microRNAs (miRNAs) for binding sites, thereby exerting an indirect regulatory effect on mRNA expression[ 15 ]. Guided by this theoretical framework, we constructed the ceRNA regulatory network through the following steps. Firstly, four databases-miRDB ( https://mirdb.org/expression.html ), miRWalk ( http://mirwalk.umm.uni-heidelberg.de/search/?species=human&gene=&mirna= ), miRanda ( http://mirtoolsgallery.tech/mirtoolsgallery/node/23 ), and TargetScan ( https://www.targetscan.org/vert_80/)wer e employed to predict miRNAs that target model-related genes. Only those target genes co-predicted by all four databases were recognized as credible candidates. Next, we intersected differentially expressed mRNAs (DE mRNAs) with the predicted target mRNAs, and the overlapping mRNAs were selected for subsequent studies. Subsequently, further integrative analyses were conducted on lncRNA-miRNA and miRNA-mRNA pairs to establish the lncRNA-miRNA-mRNA ceRNA regulatory network, which was ultimately visualized using Cytoscape software ( https://cytoscape.org/ ). 2.11 Statistical Methods All statistical analyses in this study have been conducted utilising R software (version 4.4.1). P less than 0.05 and false discovery rate (FDR) < 0.05 were the criteria used to determine statistical significance. A prediction model dependent on transient receptor potential channel-related genes (TRGs) was constructed leveraging the LASSO-Cox regression algorithm. The statistical differences between the two groups were determined using the Wilcoxon test. A p-value of less than 0.05 was considered sufficient for defining statistically significant differences. 3. Results 3.1 Identification of Differentially Expressed TRGs in CESC We looked at variations among 2206 TRG expression between specimens of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (n = 306) in comparison to normal tissue samples (n = 3). The volcano map shows that 487 TRGs have been noticed to be genes that are differentially expressed between CESC samples and nearest normal counterparts. (Figure. 1A) and heatmap (Figure. 1B). 65 TRGs with a prognostic association were found using univariate Cox regression analysis (Fig. 1 C). Additionally, nearly all of these genes showed mutual relationships, following correlation analysis (Fig. 1 D). 3.2 Identification of Two Molecular Subtypes via Consensus Clustering Univariate Cox regression analysis identified 65 TRGs in total. When evaluating clustering stability across k values ranging from 2 to 9 based on expression similarity, k = 2 showed the optimal performance and was thus chosen for subsequent exploration of the clinical relevance of TRGs (Figs. 2 A and 2 B). Based on their gene expression profiles, people who have cervical squamous cell carcinoma (CESC) were divided into two subgroups (Fig. 2 B). Patients in subgroup B had a considerably better prognosis than those in subgroup A, based upon survival analysis (Fig. 2 C). The two subgroups showed varied distribution patterns as determined by principal component analysis (PCA) results (Fig. 2 D). The differences in TRG expression between two distinct subtypes are displayed in Fig. 2 E. Additionally, a heatmap showed how subtype A and subtype B differed in the distribution of important clinical features (such age, TNM stage, and tumor grade) (Fig. 2 F). 3.3 Enrichment Analysis of Subtypes We used gene set variation analysis (GSVA) to examine the biological characteristics of sodium channel gene subtypes. Four signaling channels— activated CD4 T cells, mast cells, activated B cells, and immature B cells—were found to be significantly enriched (Fig. 3 A). In gene set enrichment analysis (GSEA), subtype B was predominantly enriched in pathways including Activated B cell[ 16 ], Activated CD4 T cell[ 17 ], CD56bright natural killer cell[ 18 ], and Mast cell[ 19 ]; these pathways are potentially linked to tumor metastasis and immune escape (Fig. 3 B). Interestingly, the results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) study of enrichment showed that the HIF-1 signaling pathway was involved (Fig. 3 C). In cervical cancer, VAMP8 overexpression stimulates autophagy and tumor development. The HIF-1 pathway, a crucial VAMP8 regulatory axis, enhances angiogenesis and hypoxic response, which affects the growth and spread of cervical cancer[ 20 ]. Additionally, we performed Gene Ontology (GO) analysis (Fig. 3 D). 3.4 The element sodium Channel-Related Gene Prophetic Model's Mutation Landscape Tumor Mutation Burden (TMB) was meticulously investigated. The larger somatic mutation percentage in the high-risk group (82.71%) was much greater than that in the group with low risk, based upon waterfall plot analysis. This difference was particularly pronounced in several key tumor-associated genes, including PIK3CA (31% vs. 28%), KMT2C (20% vs. 17%), KMT2D (15% vs. 12%) and FLG (17% vs. 9%) (Figs. 4 A and 4 B). 3.5 Immunological Analysis of Sodium Channel-Related Genes The immune infiltration patterns of the large expression and poorly expressed groups were next scrutinized. First, we compared the immunological, stromal, and ESTIMATE scores of the two groups. The results showed that all three of the low-expression group's scores were significantly higher than that of the high-expression group. (Fig. 5 A). Next, we investigated the relationships between the expression levels of the 10 genes and these markedly diverse immune cell types (Fig. 5 B). The immune cell infiltration patterns of the two groups were subsequently compared using the CIBERSORT method. In cervical cancer patients from the low-risk and high-risk groups, 22 different immune cell subsets were subjected to differential analysis; among these, naive B cells and a number of other immune cell types shown notable differences ( p < 0.05) (Fig. 5 C). 3.6 Construction of a Prognostic Model Related to Sodium Channel Genes The present investigation sequentially utilized multivariate Cox regression analysis and LASSO regression analysis to reduce the potential risk of model overfitting after cervical cancer-related data had been randomly divided into a training set and a test set. The biomarkers with the highest predictive value were successfully found and chosen via this analytical technique, which also created a prognosis index for forecasting clinical outcomes (Fig. 6A, 6B). Based on the correlation nomogram, the 10 genes were found to have the strongest predictive power (Fig. 6C). Therefore, the corresponding coefficient-based risk score was calculated using the following formula: Risk score = (0.5 × SPINT2) + (0.34 × TFRC) + (0.24 × TNF) + (0.41 × GJB2) − (0.26 × CAMP) − (3.39 × MYBPC3) + (0.72 × LEPR) − (0.97 × FOXP3) − (0.54 × DUOX1) − (3.91 × TNNI3K). Multivariate Cox regression analysis was used to ascertain if the risk score functioned as an accurate indicator for CESC independent of other clinicopathological factors. The findings exhibited an independent association between the risk score and overall survival (OS) (Fig. 6D, P < 0.01). 3.7 ROC Curve and Survival Analysis By employing the Prognostic Model Survival analysis demonstrated that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group, and time-dependent ROC curve validated the predictive value of the risk score (Figure. 7A). The area under the curve (AUC) values of OS at 1, 3, and 5 years in the test cohort were 0.733, 0.801, and 0.786, respectively (Figure. 7D). External validation was performed using the GSE44001 dataset. After grouping according to the criteria of the TCGA cohort, the OS of the high-risk group remained lower than that of the low-risk group (consistent with the results of the TCGA cohort, Fig. 7 C), with AUC values of 0.620, 0.684, and 0.595 for 1-, 3-, and 5-year. These results confirmed the effectiveness of the risk model in predicting clinical outcomes (Fig. 7 F). 3.8 Construction of the Nomogram In the current study, a comprehensive prognostic nomogram incorporating tumor grade and risk score was established (Fig. 8 A) to promote the clinical translation of the risk model. For patients with CESC, the nomogram was constructed using the rms package to visually forecast the 1-year, 3-year, and 5-year overall survival (OS) probabilities. Important clinicopathological characteristics were coupled with the risk score (Fig. 8 B). The results verified that the OS of patients with cervical carcinoma (CC) could be consistently predicted by this method of estimation. 3.9 TRGs and Single-Cell Analysis in Cervical Cancer Tissues We sought to investigate the cell types enriched based on single-cell RNA sequencing (scRNA-seq) data, given the aberrant distribution of the ten-gene signature in CESC tissues. 21 cell clusters and 7 cell types were found in cervical cancer tissues via analysis of the scRNA-seq data from the GSE168652 dataset (Figs. 9 A and 9 B). Figure 9 C shows the distribution of each gene among the seven cell types; boxplots (Fig. 9 D) make this pattern easier to see. 3.10 Construction of the ceRNA Regulatory Network The miRanda, miRDB, miRWalk, and TargetScan databases were used to estimate the target miRNAs as well as associated lncRNAs of the ten genes (Fig. 10 ). 4. Discussion Cervical cancer (CESC) remains a major threat to women's reproductive health worldwide, with persistently high mortality rates among advanced-stage patients[ 21 , 22 ]. The primary underlying reason is the lack of effective prognostic assessment tools and targeted therapeutic strategies. As key regulators of intracellular calcium homeostasis, transient receptor potential (TRP) channels have been demonstrated to be involved in multiple core malignant biological behaviors of tumors, including proliferation, migration, and apoptosis[ 23 ]. However, systematic research on TRP channel-related genes (TRPGs) as prognostic biomarkers for cervical cancer remains scarce.In this study, 10 core TRPGs were successfully identified through multi-step screening, and a prognostic risk model with excellent performance was constructed. These findings confirm that TRPGs are valuable biomarkers for prognostic evaluation of cervical cancer, and the constructed model provides a novel tool for achieving accurate survival prediction. In this study, we developed a model that predicts for cervical cancer and successfully screened a group of TRP-related genes with predictive power. The findings showed that this model outperformed traditional clinical characteristics like age and stage to forecast OS of cervical cancer patients in both the training and validation cohorts. These results indicate the TRP-related genes can function as independent prognostic variables and play important roles in the development of cervical cancer. TRP channels have been associated to the formation and occurrence of a number of cancers, which is consistent with earlier research[ 24 ]. For example, TFRC stimulates colorectal cancer cell migration and proliferation via associated signaling pathways[ 25 ]; FOXP3 inhibits the growth of breast cancer cells by regulating calcium influx[ 26 ]. In the field of cervical cancer research, studies focusing on the overall prognostic value of TRP-related genes remain relatively limited. Notably, multivariate Cox regression analysis verified that the risk score derived from these TRPGs signatures was an independent prognostic factor independent of age, tumor stage, and differentiation grade, suggesting that it can complement traditional clinical characteristics to further improve the accuracy of prognostic assessment. Decision curve analysis (DCA) confirmed the nomogram's clinical net benefit, and the nomogram created by combining the risk score and important clinical characteristics demonstrated a good concordance between predicted values and actual survival outcomes via calibration curves. These findings demonstrate that the TRPGs-based prognostic model has significant clinical translation potential, which helps physicians identify high-risk patients early and develop personalised treatment plans[ 6 ]. For example, high-risk patients identified by the model can benefit from intensified adjuvant therapy or close follow-up, whereas low-risk patients can avoid unnecessary overtreatment, thus optimizing the balance between therapeutic efficacy and quality of life. Additionally, this study investigated the connections between TRPs and the immunological landscape and discovered an advantageous relationship between risk scores and immune cell functioning. T cell activation, proliferation, and differentiation are all affected by TRP genes. While targeted inhibition of TRPC6 can restore Th1-type immune responses and enhance the infiltration and cytotoxic function of CD8⁺ cytotoxic T lymphocytes (CTLs), high expression of TRPC6 can induce the predominant differentiation of Th2 cells in cervical cancer, forming an immunosuppressive microenvironment[ 27 ]. In the present study, we deciphered the expression patterns of TRP-related genes across distinct cell subsets in cervical cancer tissues using scRNA-seq data[ 28 ]. The findings suggested that different kinds of tissues, which include cancer cells, autoimmune cells, and stromal cells, expressed TRP-related genes systematically. A number of key TRP-related genes in the prognostic model were highly expressed in cancer cells, suggesting that they may directly regulate the initiation and progression of cervical cancer cells. Furthermore, we found that TRP-related genes were also expressed in immune cells such as T cells, B cells, and macrophages, indicating that these genes may be involved in the modulation of the tumor immune microenvironment (TIME) in cervical cancer[ 29 ]. Tumor progression and immunotherapy response are significantly affected by the tumor immunological microenvironment. For instance, M2-type macrophages promote tumor growth by reducing anti-tumor immunity, while CD8⁺ T cell infiltration has been associated to a good prognosis among people with cervical cancer[ 30 ]. For instance, research has shown that TRPM7 is highly expressed in cervical cancer tissues and promotes the epithelial-mesenchymal transition (EMT), which is consistent with our study's finding that TFRC is a core risk gene[ 31 ]. Similarly, FOXP3, another candidate gene in the signature, has been confirmed to enhance cell division, proliferation and migration through exogenous expression, exerting pro-tumorigenic activity[ 32 ]. However, unlike single-gene biomarker studies, our model integrates multiple TRPGs, which circumvents the limitations caused by the variability of individual genes, thereby improving the robustness and reliability of prognostic prediction. Furthermore, our work supplements the TRP-related gene prognostic model established in the study by Jiang et al. Compared with other molecular prognostic models for cervical cancer, the present study adopted dual-cohort validation using both TCGA and GEO datasets, which mitigated the risk of overfitting and enhanced the generalization ability of the model. More importantly, most existing models focus on a single biological process (e.g., ferroptosis, autophagy). In contrast, TRP channels are involved in multiple tumor-associated pathways including calcium signaling pathways, epithelial-mesenchymal transition (EMT), and immune regulation, enabling the signature constructed in this study to more comprehensively reflect the complex molecular mechanisms underlying cervical cancer progression[ 33 ]. Functional enrichment analysis additionally confirmed that the core TRPGs were abundant for the HIF-1 signaling pathway, which somewhat is speculated for overseeing tumor cell migration and proliferation[ 34 ]. Furthermore, immune infiltration analysis confirmed that the tumor microenvironment (TME) differed significantly between the high- and low-risk groups; in particular, the high-risk group indicated higher proportions of natural killer (NK) cells and lower infiltration levels of naive B cells. These findings imply that TRPGs could affect tumor immune evasion through modification of the TME's composition[ 35 ]. This finding is consistent with a recent study verifying that TRP channels can regulate immune cell functions, indicating that our model not only holds prognostic predictive value, but also provides a novel perspective for elucidating the cross-regulatory mechanisms between TRP channels and tumor immunity. Despite the promising findings achieved in this study, several limitations remain to be addressed in subsequent research. First, all the data utilized in this study were derived from public databases (TCGA and GEO), and the sample size of the validation cohort was relatively small. Future studies should incorporate multicenter, large-sample prospective cohorts to further validate the model performance. Second, the screening of core TRPGs was based on bioinformatics analysis, and the specific molecular mechanisms underlying their regulation of cervical cancer progression still require experimental verification (e.g., in vitro cell line experiments and in vivo animal models). Third, the model established herein adopted overall survival as the primary endpoint; future research could incorporate other clinical outcomes such as progression-free survival and chemotherapy response rate to enhance the clinical utility of the model. Fourth, this study did not explore the potential of TRPGs as therapeutic targets. Subsequent studies may conduct in-depth investigations into the therapeutic efficacy of TRP channel inhibitors in cervical cancer cells with high expression of core risk genes. In summary, a novel TRP channel-related gene signature for prognostic prediction in cervical cancer was effectively developed and verified in this investigation. It shows significant predictive efficacy and practical application. This signature not only provides a novel tool for risk stratification and individualized treatment in cervical cancer patients but also offers new insights into elucidating the molecular mechanisms of tumor progression mediated by TRP channels. With further validation and mechanistic exploration, this prognostic model is expected to be translated into clinical applications, thereby improving the therapeutic outcomes of cervical cancer patients. 5. Conclusion In summary, we devised a TRP-based molecular clustering system and prognostic signature that facilitates survival prediction, immunotherapy guidance, and clinical outcome determination. This study is expected to deepen the understanding of the functions of sodium channel-related genes in cervical cancer and accelerate the development of more effective therapeutic strategies against this disease. Declarations Acknowledge ： This work was supported by the Guangxi Medical and health key discipline construction project and Guangxi Medical and health key cultivation discipline construction project. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author information Author notes Zhengchao Yan and Sijuan Tang should be considered joint first author. Authors and Affiliations Department of Clinical Laboratory, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin 541002, China. Zhengchao Yan, Jianlin Chen, Naqiu Yin, Yufei Pan, Yulong He, Ying Wan, Lili Su, Minhui He, Yue Li, Jianbin Yang & Liwu Zhang Department of Laboratory Medicine, The Second Affiliated Hospital of Guilin Medical University, Guilin, China Sijuan Tang Department of Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China Contributions: Zhengchao Yan and Sijuan Tang contributed equally to this work. Naqiu Yin, Yufei Pan and Yulong He conceived and designed the study. Ying Wan, Lili Su, Yue Li and Minhui He contributed to performing the experiments and developing methodology. Zhengchao Yan and Sijuan Tang contributed to the writing, reviewing, and revision of the paper. Jianlin Chen and Liwu Zhang acquired funding and supervised the study. All authors read and approved the final version of the manuscript. Corresponding author: Correspondence to Jianlin Chen and Liwu Zhang. Data availability The dataset GSE44001 analyzed in this study is available in the GEO repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44001]. and The Cancer Genome Atlas Program (TCGA) [https://portal.gdc.cancer.gov/]，including the TCGA-CESC project (https://portal.gdc.cancer.gov/projects/TCGA-CESC).The data supporting the findings of this study are available in the GeneCards platform (https://www.genecards.org/Search/Keyword?queryString=sodium%20overload). To facilitate efficient access to raw data, we added clear Point of Contact information: Point of Contact:Zhengchao Yan. Email Address: [email protected] Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests.Conflict of interest Supplementary information: Table S1: Clinical data of cervical cancer patients; Table S2: Sodium channel-related genes References Feng Q, Yang W, Su G, Wu F, Xing C. Development and validation of a novel sodium-overload related genes signature for prognostic prediction in breast cancer: integrating bioinformatics and experimental approaches. Front Immunol. 2025;16:1653903. Buskwofie A, David-West G, Clare CA. A Review of Cervical Cancer: Incidence and Disparities. J Natl Med Assoc. 2020;112(2):229–32. Cosens DJ, Manning A. Abnormal electroretinogram from a Drosophila mutant. Nature. 1969;224(5216):285–7. Hof T, Chaigne S, Récalde A, Sallé L, Brette F, Guinamard R. Transient receptor potential channels in cardiac health and disease. Nat Rev Cardiol. 2019;16(6):344–60. Koivisto AP, Belvisi MG, Gaudet R, Szallasi A. Advances in TRP channel drug discovery: from target validation to clinical studies. 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Pongapin and Karanjin, furanoflavanoids of Pongamia pinnata, induce G2/M arrest and apoptosis in cervical cancer cells by differential reactive oxygen species modulation, DNA damage, and nuclear factor kappa-light-chain-enhancer of activated B cell signaling. Phytother Res. 2019;33(4):1084–94. Jaumdally SZ, Liebenberg LJP, Gumbi PP, Little F, Jaspan HB, Gamieldien H, Tiemessen CT, Coetzee D, Martin DP, Williamson C, et al. Partner HIV Serostatus Impacts Viral Load, Genital HIV Shedding, and Immune Activation in HIV-Infected Individuals. J Acquir Immune Defic Syndr. 2019;82(1):51–60. Khatri VP, Baiocchi RA, Bernstein ZP, Caligiuri MA. Immunotherapy with low-dose interleukin-2: rationale for prevention of immune-deficiency-associated cancer. Cancer J Sci Am. 1997;3(Suppl 1):S129–136. Mo X, Wang N, He Z, Kang W, Wang L, Han X, Yang L. The sub-molecular characterization identification for cervical cancer. Heliyon. 2023;9(6):e16873. Wang Y, Wu D, Gai J, Cai Y, Hua K, Zhu Z, Xin W. Elevated VAMP8 expression promotes cervical cancer progression by enhancing autophagy via HIF-1 pathway. BMC Med. 2025;23(1):544. Liu C, Li X, Huang Q, Zhang M, Lei T, Wang F, Zou W, Huang R, Hu X, Wang C, et al. Single-cell RNA-sequencing reveals radiochemotherapy-induced innate immune activation and MHC-II upregulation in cervical cancer. Signal Transduct Target Ther. 2023;8(1):44. Imamura A, Oike T, Sato H, Yoshimoto Y, Ando K, Ohno T. Comparative Analysis of the Antitumor Immune Profiles of Paired Radiotherapy-naive and Radiotherapy-treated Cervical Cancer Tissues. Anticancer Res. 2022;42(7):3341–8. Marini M, Titiz M, Souza Monteiro de Araújo D, Geppetti P, Nassini R, De Logu F. TRP Channels in Cancer: Signaling Mechanisms and Translational Approaches. Biomolecules 2023, 13(10). Jardin I, Nieto J, Salido GM, Rosado JA. TRPC6 channel and its implications in breast cancer: an overview. Biochim Biophys Acta Mol Cell Res. 2020;1867(12):118828. Wang X, Zhou Y, Ning L, Chen J, Chen H, Li X. Knockdown of ANXA10 induces ferroptosis by inhibiting autophagy-mediated TFRC degradation in colorectal cancer. Cell Death Dis. 2023;14(9):588. Usman AN, Ahmad M, Sinrang AW, Natsir S, Takko AB, Ariyandy A, Ilhamuddin I, Eragradini AR, Hasan II, Hasyim S. FOXP3 regulatory T cells on prognosis of breast cancer. Breast Dis. 2023;42(1):213–8. Wan Q, Zheng A, Liu X, Chen Y, Han L. Expression of transient receptor potential channel 6 in cervical cancer. Onco Targets Ther. 2012;5:171–6. Li J, Wan C, Li X, Quan C, Li X, Wu X. Characterization of tumor microenvironment and tumor immunology based on the double-stranded RNA-binding protein related genes in cervical cancer. J Transl Med. 2023;21(1):647. Wang Z, Wang B, Feng Y, Ye J, Mao Z, Zhang T, Xu M, Zhang W, Jiao X, Zhang Q et al. Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer. J Immunother Cancer 2024, 12(8). Mastrogeorgiou M, Chatzikalil E, Theocharis S, Papoudou-Bai A, Péoc'h M, Mobarki M, Karpathiou G. The immune microenvironment of cancer of the uterine cervix. Histol Histopathol. 2024;39(10):1245–71. Fang X, Hu P, Gao Y, Chen C, Xu J. Transferrin receptor modulated by microRNA-497-5p suppresses cervical cancer cell malignant phenotypes. Adv Clin Exp Med. 2024;33(3):273–82. Ni H, Zhang H, Li L, Huang H, Guo H, Zhang L, Li C, Xu JX, Nie CP, Li K et al. T cell-intrinsic STING signaling promotes regulatory T cell induction and immunosuppression by upregulating FOXP3 transcription in cervical cancer. J Immunother Cancer 2022, 10(9). Jiang S, Lin X, Wu Q, Zheng J, Cui Z, Cai X, Li Y, Zheng C, Sun Y. Transient receptor potential channels' genes forecast cervical cancer outcomes and illuminate its impact on tumor cells. Front Genet. 2024;15:1391842. Xu X, Liu T, Wu J, Wang Y, Hong Y, Zhou H. Transferrin receptor-involved HIF-1 signaling pathway in cervical cancer. Cancer Gene Ther. 2019;26(11–12):356–65. Rossetti RAM, Lorenzi NPC, Yokochi K, Rosa M, Benevides L, Margarido PFR, Baracat EC, Carvalho JP, Villa LL, Lepique AP. B lymphocytes can be activated to act as antigen presenting cells to promote anti-tumor responses. PLoS ONE. 2018;13(7):e0199034. 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-8553093","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580106304,"identity":"7b23f0b9-0e8d-4a10-b792-664f6e9f2985","order_by":0,"name":"Zhengchao Yan","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Zhengchao","middleName":"","lastName":"Yan","suffix":""},{"id":580106305,"identity":"9adf85ea-70eb-4196-83c9-412cd7ce31d9","order_by":1,"name":"Sijuan Tang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sijuan","middleName":"","lastName":"Tang","suffix":""},{"id":580106306,"identity":"bd1655d9-74b8-49d8-8876-7abbaea6f1a7","order_by":2,"name":"Naqiu Yin","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Naqiu","middleName":"","lastName":"Yin","suffix":""},{"id":580106307,"identity":"078d5779-1772-44d9-8b22-1ac277bf603c","order_by":3,"name":"Ying Wan","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wan","suffix":""},{"id":580106308,"identity":"6926e451-2af0-4e0c-9bfc-fd5d49c0dda4","order_by":4,"name":"Lili Su","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Su","suffix":""},{"id":580106309,"identity":"e1f1fb8a-9017-4534-a4dc-c1d9c06cb7eb","order_by":5,"name":"Minhui He","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Minhui","middleName":"","lastName":"He","suffix":""},{"id":580106310,"identity":"d92bc2f0-2307-42f5-a98b-db75e2a516ea","order_by":6,"name":"Yue Li","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":580106311,"identity":"7996d811-7bd0-4ebf-9094-8782d75d83ac","order_by":7,"name":"Jianbin Yang","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Jianbin","middleName":"","lastName":"Yang","suffix":""},{"id":580106312,"identity":"11cb09b8-a5a6-4824-bfe0-077651e8e93f","order_by":8,"name":"Yulong He","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"He","suffix":""},{"id":580106313,"identity":"ad891660-960e-4595-89aa-4701be94a36d","order_by":9,"name":"Yufei Pan","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Pan","suffix":""},{"id":580106314,"identity":"846e635f-3399-48f0-852f-f36a33f7684d","order_by":10,"name":"Liwu Zhang","email":"","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Liwu","middleName":"","lastName":"Zhang","suffix":""},{"id":580106315,"identity":"816d618c-8c53-42c8-91ec-e5351cbbecd8","order_by":11,"name":"Jianlin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie2RMQrCMBSGUwRdAnFMEXoD4UHAReniRRoEu3Z0NBTiFXKMTs4NGTyDuDR4gRzAwdTNqW8UzDe/j/f+/xGSSPwkTg0V7Oh8oZQPOMW2EJpjwahrBccpmc5NcCI3tV5SjMCUbQUFJ7u714STslifJxTeW/WkUMvuIfXQkIPY9BMKkM+W7ahcgJNeXhGKXlGYxcOs5hSr5Ab2MX6GVGKWWDKMJctYMiCyMOPsUL3GV968D6eymFTimu87EbAzZiqRSCT+mjcW5klbl4InXAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanxishan Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-08 15:24:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8553093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8553093/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101785006,"identity":"b9198d7d-d999-4a51-b21c-ee97a32789ce","added_by":"auto","created_at":"2026-02-03 15:33:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1317497,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of sodium channel-related genes in CESC. (A) TRG expression in CESC is presented as a volcano plot, with red denoting upregulated genes and green denoting downregulated genes. (B) A heatmap showing the TRG expression levels. (C) A forest plot produced from 65 TRGs' univariate Cox regression analysis. (D) Gene correlation analysis and a prognostic network diagram.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/eaa88f75398f9a1e59d9dcef.png"},{"id":101785009,"identity":"fdc9a848-6406-4941-b63e-50869c18f540","added_by":"auto","created_at":"2026-02-03 15:33:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1366799,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering analysis is used to identify subtypes linked to the sodium channel gene.(A, B) TCGA cervical cancer data were used to identify two subgroups (A and B), which were found to be the ideal number of clusters.(C) Kaplan-Meier (K-M) survival curves for subgroups A and B's overall survival (OS).(D) Cervical cancer samples arranged by risk score in a principal component analysis (PCA) plot.(E) TRG expression varies across subtypes A and B.(F) A heatmap displaying the distribution of each subgroup's sodium channel-related gene expression, age, tumor grade, and TNM stage in the TCGA database.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/d56d59faa3ae28a225bc0d1d.png"},{"id":101880912,"identity":"9b1dd978-6bb7-4871-bdd2-4ad445022f19","added_by":"auto","created_at":"2026-02-04 15:07:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":551343,"visible":true,"origin":"","legend":"\u003cp\u003e(A) GSVA shows pathways that differ significantly between the two sodium channel gene subtypes. (B) GSEA, or gene set enrichment analysis. (C) KEGG enrichment analysis of genes that differ in expression between the two subtypes. (D) Analysis of genes with variations in expression between the two subtypes using Gene Ontology (GO) enrichment.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/9bba26e1409cecc245fd62c4.png"},{"id":101785015,"identity":"22e148dd-0e2a-462b-ab1e-08cc7bca2ad4","added_by":"auto","created_at":"2026-02-03 15:33:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":360024,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) Waterfall plots of the high- and low-risk groups related to TRGs.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/e84eb1490727c11354306cfb.png"},{"id":101942813,"identity":"55c39e19-7b32-4b27-b2a8-df491987213b","added_by":"auto","created_at":"2026-02-05 09:38:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":410008,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis. (A) Boxplots showing any distinctions in ESTIMATE scores between the high- and low-expression groups; (B) Scatter plots conveying the correlation between differentially expressed immune cells and TRG expression levels; and (C) Variances between the groups with high and low expression levels of immune cell infiltration.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/172624b2aa033894ff89074f.png"},{"id":101880563,"identity":"c3c9934c-a839-4738-8749-1be19f8edba1","added_by":"auto","created_at":"2026-02-04 15:03:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278675,"visible":true,"origin":"","legend":"\u003cp\u003eThe TCGA database was used to create risk markers for patients with cervical cancer. (A, B) LASSO regression in conjunction with Cox regression for feature creation. (C) The correlation nomogram and the ten genes that make up the signature. (D) Cox regression-based multivariate analysis of clinical and pathological characteristics and overall survival (OS).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/94011f8e4fcb4e3ee1b9ae35.png"},{"id":101785012,"identity":"f25acef9-1a47-415d-a7a8-be1d77683ff8","added_by":"auto","created_at":"2026-02-03 15:33:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":455808,"visible":true,"origin":"","legend":"\u003cp\u003eEmergence and authentication of a sodium channel-related gene signature. (A, B, and C) Kaplan-Meier survival curves for the high- and low-risk groups in the GEO cohort, training set, and validation set, respectively. (D, E, F) ROC curve analysis.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/5e40acdb051b3659a649666f.png"},{"id":101785010,"identity":"c88a13a0-bffc-476b-95b7-5f9fa09ade1c","added_by":"auto","created_at":"2026-02-03 15:33:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic model for patients with cervical cancer was developed and confirmed.(A) Applying clinical data and the prognostic nomogram to estimate the 1-year, 3-year, and 5-year survival rates of patients with cervical cancer.(B) Calibration curves for predicting survival probabilities after one, three, and five years.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/89fdb467f0d60abc8baf34a1.png"},{"id":102295100,"identity":"225a10ef-f1d9-41fa-8e51-34f252c420d8","added_by":"auto","created_at":"2026-02-10 10:08:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":464474,"visible":true,"origin":"","legend":"\u003cp\u003eTRP channel-related genes in different cell types from the GSE168652 cohort.(A) Cell clusters found in CESC tissues employing the GSE168652 dataset.(B) Cell types discovered in CESC tissues applying the GSE168652 dataset.(C) Expression profiles of eight TRP channel-related genes from the GSE168652 cohort in various kinds of cells.(D) Boxplots exhibiting the expression of eight genes connected with TRP channels in multiple kinds of cells from the GSE168652 cohort.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/715aa6f7467cc0ce8e7f08f3.png"},{"id":101785014,"identity":"b87184c4-0eaa-4363-9f94-6efe49dc6023","added_by":"auto","created_at":"2026-02-03 15:33:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":462023,"visible":true,"origin":"","legend":"\u003cp\u003eThe lncRNA-miRNA-mRNA ceRNA regulatory network.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/1506ef405c7d4abe192dc5fe.png"},{"id":105751658,"identity":"04fefc75-7da4-4a93-8aa8-a29f2b5feff5","added_by":"auto","created_at":"2026-03-30 15:35:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6759133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8553093/v1/e882cf28-56a6-4013-a291-1772d373c936.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical cancer is a malignant neoplasm affecting the female cervix and constitutes one of the prevalent malignancies in gynecological practice. Persistent infection with the human papillomavirus (HPV), particular high-risk subtypes like HPV 16 and HPV 18, is the primary manifestation. Among female cancers, cervical cancer is associated with the fourth- highest incidence and fatalities nationwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to the creation of cervical cancer vaccinations and the widespread adoption of screening programs, major reductions in the disease's incidence and mortality have been attained in comparison to earlier times. However, there is a growing trend toward younger age at onset, this putting young women's health and quality of life at dangerous levels[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Benefiting from advancements in early detection methodologies, surgical approaches, and molecular targeted therapies, the overall mortality rate has remained stable over the past five years across all genders. Despite these progresses, multiple aspects of cervical cancer\u0026rsquo;s pathophysiological mechanisms remain inadequately elucidated, and the functional roles of numerous biomarkers associated with distinct stages of cervical carcinogenesis are yet to be clarified. Although therapeutic protocols have been optimized, the overall survival rate of patients with CESC remains less than ideal, largely attributable to disease recurrence, metastasis, and drug resistance. Consequently, the identification of novel therapeutic targets is of critical importance for retarding disease progression and enhancing prognostic outcomes.\u003c/p\u003e \u003cp\u003eIn 1969, Cosens and companions reported a visual genetic change in Drosophila melanogaster, eventually led to the initial identification of transient receptor potential (TRP) channels[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These channels are crucial for perceiving external stimuli (such as temperature, chemicals, and mechanical force) and modulating cellular functions[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite decades of research on TRP channels, their specific roles in physiological and pathological processes remain partially unclear. Mounting evidence indicates that abnormal expression or functional impairment of TRP channels are detected in various diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, it is predicted that modulating TRP channel activity could lead to fresh approaches and opportunities for the treatment and prevention of crucial disease.\u003c/p\u003e \u003cp\u003eTransient receptor potential (TRP) channel-related genes (TRGs) are aberrantly expressed during the tumorigenesis and metastasis of various types of cancer, according to mounting evidence. This dysregulation may be crucial in promoting the spread and proliferation of cancer cells. A steadily increasing amount of knowledge is showing that dysregulated expression of TRPV1 is a significant contributor to several types of carcinoma of the female reproductive system, such as endometrial, breast, and cervical cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Further, TRP channel-related genes impact prostate cancer treatment outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given that TRGs are widely expressed in the liver, they not only serve as key regulators of intracellular cation concentrations but also act as initiators or intermediates in specific signaling pathways that contribute to the pathogenesis of liver diseases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Based on these findings, we established a TRP-related predictive model, with the aim of elucidating the mechanisms through which TRP channel-related genes modulate the survival prognosis of cervical cancer (CC) patients and providing novel insights for the targeted treatment of this malignancy.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Processing\u003c/h2\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) public database's official portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is administered by the National Cancer Institute (NCI) in the United States, submitted helpful data on cervical squamous cell carcinoma (CESC) regarding this study[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The dataset encompassed 3 adjacent normal tissue samples and 306 CESC patient tumor samples. The compiled clinical information included key details such as Tumor, Node, Metastasis (TNM) staging, along with patient age, gender, and tumor grade (refer to Table S1). During TCGA data processing, the RNA-seq data were normalized using the log2(TPM\u0026thinsp;+\u0026thinsp;1) transformation method.To screen for genes whose expression levels are associated with sodium overload, we conducted a search on the GeneCards platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a comprehensive human gene database. A total of 2206 relevant genes were discovered through this search, including genes the fact that are directly involved in sodium transport processes (which include ion channels, exchangers, and pumps) as well as a number of downstream regulatory factors and genes relating to the physiological effects of disturbed sodium homeostasis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].For further experimental validation, we utilized datasets from the Gene Expression Omnibus (GEO) repository, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 300 tumor samples' clinical and RNA sequencing data have been maintained in the pertinent Gene Expression Omnibus (GEO) databases; Table S2 provides a summary of the query genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 Locating Differentially Expressed Genes Relevant with CESC\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;limma\u0026rdquo; software package within R (version 4.3.1) was applied to detect significant differences in TRG expression levels between CESC specimens and their corresponding adjacent normal tissues.Prior to incorporating TRGs into a one-way model, genes with prognostic significance were filtered from the TCGA dataset using the following criteria. Our work utilised the R language's vioplot package to generate on its own volcano plots and heatmaps for displaying the variations in gene expression patterns in order to shed light on the variation in the expression of TRG between cervical squamous cell carcinoma (CESC) tissues and surrounding normal tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Molecular Isoforms of Multiple Differentially Expressed Genes (TRGs) Concurrent combined with Sodium Channels: Consensus Clustering\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGenes from the TRG set that exhibited notable prognostic significance were filtered out through univariate Cox regression analysis. In the CESC patient cohort, two distinct molecular subtypes were classified using the ConsensusClusterPlus package in R. The optimal cluster number was chosen by evaluating cumulative distribution function (CDF) curves and consensus clustering heatmaps, with the final number of clusters set at K\u0026thinsp;=\u0026thinsp;2. For visualizing the analytical results, we employed the ggplot2 package (version 3.5.2) in R. To investigate distinctions between overall survival (OS) among the different TRG-based subtypes and confirm if these subtypes may function as independent prognostic indicators, the survival package (version 3.8.3) inside R was utilized. The OS outcomes between the two patient groupings were compared using survival curves generated by Kaplan-Meier analysis. Clinical factors (such as tumor grade, patient age, and survival status) were analyzed between the two groups using the chi-square test. Additionally, heatmaps showing the connections between clinically significant characteristics and gene expression profiles across the TRG subgroups were created by R's ComplexHeatmap package (version 2.22.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4 Evaluation and Inspection of Prognostic Importance\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study executed multivariate Cox proportional hazards regression analysis to investigate the relationship between gene expression profiles and clinical prognostic data in the CESC cohort from The Cancer Genome Atlas (TCGA) database in order to elucidate the link between and independent prognostic value of the transient receptor prospective channel genes (TRGs) with Overall Survival (OS) for people with cervical squamous cell carcinoma (CESC). In order to reduce the effect of accidental errors on model performance, the CESC gene expression data and related clinical data that were obtained from the TCGA database were sampled at random and partitioned 1000 times. Each partition has been split into a set to be trained and a test set. Using the R package glmnet, the reduction in dimensionality investigation of TRGs performed performed by Utilizing the feature selection advantage of the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. A smaller list of candidate genes with strong predictive significance was ultimately removed by shrinking coefficients applying a penalty function. Ten core target genes and their satisfactory regression coefficients remain intact after a LASSO Cox prognostic prediction model was built using these genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Single-Cell Omics\u003c/h2\u003e \u003cp\u003eAt the single-cell resolution, single-cell omics techniques can dissect the cellular composition, functional heterogeneity, and intercellular communication networks within the tumor microenvironment (TME) of cervical cancer. This offers an indispensable foundation for investigating the onset and progression of cervical cancer, unraveling drug resistance-related mechanisms, and exploring candidate therapeutic targets. Single-cell RNA sequencing (scRNA-seq) data of CESC tissues were obtained from the Gene Expression Omnibus scRNA-seq dataset (GSE168652)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which has been deposited in the public Tumor Immune Single Cell Hub (TISCH) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/home/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe R language's limma library (version 3.62.2) was utilized for performing differential expression analysis. The filtered differentially expressed genes underwent enrichment analysis utilising Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Furthermore, the R package GSVA (version 2.0.7) was used to implement Gene Set Variation Analysis (GSVA). The Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] supplies the gene sets that were employed in this investigation. In meanwhile, the pathway variants of the two groups were explored using Gene Set Enrichment Analysis (GSEA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immune Cell Infiltration Analysis\u003c/h2\u003e \u003cp\u003eThe ESTIMATE algorithm was employed to compute the ESTIMATE score, which was used to characterize the immune infiltration status among different groups[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further, the CIBERSORT and ssGSEA algorithms were applied to evaluate the immune cell infiltration profiles of each group. Based on machine learning principles, the CIBERSORT algorithm allows for high-throughput analysis of multiple cell types. By utilizing tools like limma, this algorithm focused on quantifying 22 distinct immune cell subsets and comparing their distribution patterns between the two risk subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene Mutation Analysis\u003c/h2\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to extract physical SNV and CNV data. The top 15 most commonly mutated genes were exposed to mutation analysis using the R package maftools (version 2.22.0), with the results represented as waterfall plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Construction of Predictive Nomogram\u003c/h2\u003e \u003cp\u003eTo project the survival outcomes of CESC patients, a forecasting nomogram was created using the R package rms, incorporating clinically important variables and risk scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Construction of the ceRNA Regulatory Network\u003c/h2\u003e \u003cp\u003eThe competing endogenous RNA (ceRNA) argument states that some long non-coding RNAs (lncRNAs) could contend with specific microRNAs (miRNAs) for binding sites, thereby exerting an indirect regulatory effect on mRNA expression[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Guided by this theoretical framework, we constructed the ceRNA regulatory network through the following steps. Firstly, four databases-miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/expression.html\u003c/span\u003e\u003cspan address=\"https://mirdb.org/expression.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/search/?species=human\u0026amp;gene=\u0026amp;mirna=\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/search/?species=human\u0026amp;gene=\u0026amp;mirna=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), miRanda (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirtoolsgallery.tech/mirtoolsgallery/node/23\u003c/span\u003e\u003cspan address=\"http://mirtoolsgallery.tech/mirtoolsgallery/node/23\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_80/)wer\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_80/)wer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee employed to predict miRNAs that target model-related genes. Only those target genes co-predicted by all four databases were recognized as credible candidates. Next, we intersected differentially expressed mRNAs (DE mRNAs) with the predicted target mRNAs, and the overlapping mRNAs were selected for subsequent studies. Subsequently, further integrative analyses were conducted on lncRNA-miRNA and miRNA-mRNA pairs to establish the lncRNA-miRNA-mRNA ceRNA regulatory network, which was ultimately visualized using Cytoscape software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistical Methods\u003c/h2\u003e \u003cp\u003eAll statistical analyses in this study have been conducted utilising R software (version 4.4.1). P less than 0.05 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were the criteria used to determine statistical significance. A prediction model dependent on transient receptor potential channel-related genes (TRGs) was constructed leveraging the LASSO-Cox regression algorithm. The statistical differences between the two groups were determined using the Wilcoxon test. A p-value of less than 0.05 was considered sufficient for defining statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Differentially Expressed TRGs in CESC\u003c/h2\u003e \u003cp\u003eWe looked at variations among 2206 TRG expression between specimens of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (n\u0026thinsp;=\u0026thinsp;306) in comparison to normal tissue samples (n\u0026thinsp;=\u0026thinsp;3). The volcano map shows that 487 TRGs have been noticed to be genes that are differentially expressed between CESC samples and nearest normal counterparts. (Figure. 1A) and heatmap (Figure. 1B). 65 TRGs with a prognostic association were found using univariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, nearly all of these genes showed mutual relationships, following correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of Two Molecular Subtypes via Consensus Clustering\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression analysis identified 65 TRGs in total. When evaluating clustering stability across k values ranging from 2 to 9 based on expression similarity, k\u0026thinsp;=\u0026thinsp;2 showed the optimal performance and was thus chosen for subsequent exploration of the clinical relevance of TRGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Based on their gene expression profiles, people who have cervical squamous cell carcinoma (CESC) were divided into two subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Patients in subgroup B had a considerably better prognosis than those in subgroup A, based upon survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The two subgroups showed varied distribution patterns as determined by principal component analysis (PCA) results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The differences in TRG expression between two distinct subtypes are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE. Additionally, a heatmap showed how subtype A and subtype B differed in the distribution of important clinical features (such age, TNM stage, and tumor grade) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Enrichment Analysis of Subtypes\u003c/h2\u003e \u003cp\u003eWe used gene set variation analysis (GSVA) to examine the biological characteristics of sodium channel gene subtypes. Four signaling channels\u0026mdash; activated CD4 T cells, mast cells, activated B cells, and immature B cells\u0026mdash;were found to be significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In gene set enrichment analysis (GSEA), subtype B was predominantly enriched in pathways including Activated B cell[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Activated CD4 T cell[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], CD56bright natural killer cell[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and Mast cell[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; these pathways are potentially linked to tumor metastasis and immune escape (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eInterestingly, the results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) study of enrichment showed that the HIF-1 signaling pathway was involved (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In cervical cancer, VAMP8 overexpression stimulates autophagy and tumor development. The HIF-1 pathway, a crucial VAMP8 regulatory axis, enhances angiogenesis and hypoxic response, which affects the growth and spread of cervical cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, we performed Gene Ontology (GO) analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The element sodium Channel-Related Gene Prophetic Model's Mutation Landscape\u003c/h2\u003e \u003cp\u003eTumor Mutation Burden (TMB) was meticulously investigated. The larger somatic mutation percentage in the high-risk group (82.71%) was much greater than that in the group with low risk, based upon waterfall plot analysis. This difference was particularly pronounced in several key tumor-associated genes, including PIK3CA (31% vs. 28%), KMT2C (20% vs. 17%), KMT2D (15% vs. 12%) and FLG (17% vs. 9%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Immunological Analysis of Sodium Channel-Related Genes\u003c/h2\u003e \u003cp\u003eThe immune infiltration patterns of the large expression and poorly expressed groups were next scrutinized. First, we compared the immunological, stromal, and ESTIMATE scores of the two groups. The results showed that all three of the low-expression group's scores were significantly higher than that of the high-expression group. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Next, we investigated the relationships between the expression levels of the 10 genes and these markedly diverse immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The immune cell infiltration patterns of the two groups were subsequently compared using the CIBERSORT method. In cervical cancer patients from the low-risk and high-risk groups, 22 different immune cell subsets were subjected to differential analysis; among these, naive B cells and a number of other immune cell types shown notable differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Construction of a Prognostic Model Related to Sodium Channel Genes\u003c/h2\u003e \u003cp\u003eThe present investigation sequentially utilized multivariate Cox regression analysis and LASSO regression analysis to reduce the potential risk of model overfitting after cervical cancer-related data had been randomly divided into a training set and a test set. The biomarkers with the highest predictive value were successfully found and chosen via this analytical technique, which also created a prognosis index for forecasting clinical outcomes (Fig.\u0026nbsp;6A, 6B). Based on the correlation nomogram, the 10 genes were found to have the strongest predictive power (Fig.\u0026nbsp;6C). Therefore, the corresponding coefficient-based risk score was calculated using the following formula: Risk score = (0.5 \u0026times; SPINT2) + (0.34 \u0026times; TFRC) + (0.24 \u0026times; TNF) + (0.41 \u0026times; GJB2) \u0026minus; (0.26 \u0026times; CAMP) \u0026minus; (3.39 \u0026times; MYBPC3) + (0.72 \u0026times; LEPR) \u0026minus; (0.97 \u0026times; FOXP3) \u0026minus; (0.54 \u0026times; DUOX1) \u0026minus; (3.91 \u0026times; TNNI3K). Multivariate Cox regression analysis was used to ascertain if the risk score functioned as an accurate indicator for CESC independent of other clinicopathological factors. The findings exhibited an independent association between the risk score and overall survival (OS) (Fig.\u0026nbsp;6D, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 ROC Curve and Survival Analysis By employing the Prognostic Model\u003c/h2\u003e \u003cp\u003eSurvival analysis demonstrated that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group, and time-dependent ROC curve validated the predictive value of the risk score (Figure. 7A). The area under the curve (AUC) values of OS at 1, 3, and 5 years in the test cohort were 0.733, 0.801, and 0.786, respectively (Figure. 7D). External validation was performed using the GSE44001 dataset. After grouping according to the criteria of the TCGA cohort, the OS of the high-risk group remained lower than that of the low-risk group (consistent with the results of the TCGA cohort, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), with AUC values of 0.620, 0.684, and 0.595 for 1-, 3-, and 5-year. These results confirmed the effectiveness of the risk model in predicting clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Construction of the Nomogram\u003c/h2\u003e \u003cp\u003eIn the current study, a comprehensive prognostic nomogram incorporating tumor grade and risk score was established (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) to promote the clinical translation of the risk model. For patients with CESC, the nomogram was constructed using the rms package to visually forecast the 1-year, 3-year, and 5-year overall survival (OS) probabilities. Important clinicopathological characteristics were coupled with the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The results verified that the OS of patients with cervical carcinoma (CC) could be consistently predicted by this method of estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.9 TRGs and Single-Cell Analysis in Cervical Cancer Tissues\u003c/h2\u003e \u003cp\u003eWe sought to investigate the cell types enriched based on single-cell RNA sequencing (scRNA-seq) data, given the aberrant distribution of the ten-gene signature in CESC tissues. 21 cell clusters and 7 cell types were found in cervical cancer tissues via analysis of the scRNA-seq data from the GSE168652 dataset (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eC shows the distribution of each gene among the seven cell types; boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eD) make this pattern easier to see.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Construction of the ceRNA Regulatory Network\u003c/h2\u003e \u003cp\u003eThe miRanda, miRDB, miRWalk, and TargetScan databases were used to estimate the target miRNAs as well as associated lncRNAs of the ten genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCervical cancer (CESC) remains a major threat to women's reproductive health worldwide, with persistently high mortality rates among advanced-stage patients[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The primary underlying reason is the lack of effective prognostic assessment tools and targeted therapeutic strategies. As key regulators of intracellular calcium homeostasis, transient receptor potential (TRP) channels have been demonstrated to be involved in multiple core malignant biological behaviors of tumors, including proliferation, migration, and apoptosis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, systematic research on TRP channel-related genes (TRPGs) as prognostic biomarkers for cervical cancer remains scarce.In this study, 10 core TRPGs were successfully identified through multi-step screening, and a prognostic risk model with excellent performance was constructed. These findings confirm that TRPGs are valuable biomarkers for prognostic evaluation of cervical cancer, and the constructed model provides a novel tool for achieving accurate survival prediction.\u003c/p\u003e \u003cp\u003eIn this study, we developed a model that predicts for cervical cancer and successfully screened a group of TRP-related genes with predictive power. The findings showed that this model outperformed traditional clinical characteristics like age and stage to forecast OS of cervical cancer patients in both the training and validation cohorts. These results indicate the TRP-related genes can function as independent prognostic variables and play important roles in the development of cervical cancer. TRP channels have been associated to the formation and occurrence of a number of cancers, which is consistent with earlier research[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For example, TFRC stimulates colorectal cancer cell migration and proliferation via associated signaling pathways[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; FOXP3 inhibits the growth of breast cancer cells by regulating calcium influx[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the field of cervical cancer research, studies focusing on the overall prognostic value of TRP-related genes remain relatively limited.\u003c/p\u003e \u003cp\u003eNotably, multivariate Cox regression analysis verified that the risk score derived from these TRPGs signatures was an independent prognostic factor independent of age, tumor stage, and differentiation grade, suggesting that it can complement traditional clinical characteristics to further improve the accuracy of prognostic assessment. Decision curve analysis (DCA) confirmed the nomogram's clinical net benefit, and the nomogram created by combining the risk score and important clinical characteristics demonstrated a good concordance between predicted values and actual survival outcomes via calibration curves. These findings demonstrate that the TRPGs-based prognostic model has significant clinical translation potential, which helps physicians identify high-risk patients early and develop personalised treatment plans[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, high-risk patients identified by the model can benefit from intensified adjuvant therapy or close follow-up, whereas low-risk patients can avoid unnecessary overtreatment, thus optimizing the balance between therapeutic efficacy and quality of life.\u003c/p\u003e \u003cp\u003eAdditionally, this study investigated the connections between TRPs and the immunological landscape and discovered an advantageous relationship between risk scores and immune cell functioning. T cell activation, proliferation, and differentiation are all affected by TRP genes. While targeted inhibition of TRPC6 can restore Th1-type immune responses and enhance the infiltration and cytotoxic function of CD8⁺ cytotoxic T lymphocytes (CTLs), high expression of TRPC6 can induce the predominant differentiation of Th2 cells in cervical cancer, forming an immunosuppressive microenvironment[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, we deciphered the expression patterns of TRP-related genes across distinct cell subsets in cervical cancer tissues using scRNA-seq data[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The findings suggested that different kinds of tissues, which include cancer cells, autoimmune cells, and stromal cells, expressed TRP-related genes systematically. A number of key TRP-related genes in the prognostic model were highly expressed in cancer cells, suggesting that they may directly regulate the initiation and progression of cervical cancer cells. Furthermore, we found that TRP-related genes were also expressed in immune cells such as T cells, B cells, and macrophages, indicating that these genes may be involved in the modulation of the tumor immune microenvironment (TIME) in cervical cancer[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Tumor progression and immunotherapy response are significantly affected by the tumor immunological microenvironment. For instance, M2-type macrophages promote tumor growth by reducing anti-tumor immunity, while CD8⁺ T cell infiltration has been associated to a good prognosis among people with cervical cancer[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor instance, research has shown that TRPM7 is highly expressed in cervical cancer tissues and promotes the epithelial-mesenchymal transition (EMT), which is consistent with our study's finding that TFRC is a core risk gene[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, FOXP3, another candidate gene in the signature, has been confirmed to enhance cell division, proliferation and migration through exogenous expression, exerting pro-tumorigenic activity[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, unlike single-gene biomarker studies, our model integrates multiple TRPGs, which circumvents the limitations caused by the variability of individual genes, thereby improving the robustness and reliability of prognostic prediction. Furthermore, our work supplements the TRP-related gene prognostic model established in the study by Jiang et al.\u003c/p\u003e \u003cp\u003eCompared with other molecular prognostic models for cervical cancer, the present study adopted dual-cohort validation using both TCGA and GEO datasets, which mitigated the risk of overfitting and enhanced the generalization ability of the model. More importantly, most existing models focus on a single biological process (e.g., ferroptosis, autophagy). In contrast, TRP channels are involved in multiple tumor-associated pathways including calcium signaling pathways, epithelial-mesenchymal transition (EMT), and immune regulation, enabling the signature constructed in this study to more comprehensively reflect the complex molecular mechanisms underlying cervical cancer progression[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis additionally confirmed that the core TRPGs were abundant for the HIF-1 signaling pathway, which somewhat is speculated for overseeing tumor cell migration and proliferation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, immune infiltration analysis confirmed that the tumor microenvironment (TME) differed significantly between the high- and low-risk groups; in particular, the high-risk group indicated higher proportions of natural killer (NK) cells and lower infiltration levels of naive B cells. These findings imply that TRPGs could affect tumor immune evasion through modification of the TME's composition[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This finding is consistent with a recent study verifying that TRP channels can regulate immune cell functions, indicating that our model not only holds prognostic predictive value, but also provides a novel perspective for elucidating the cross-regulatory mechanisms between TRP channels and tumor immunity.\u003c/p\u003e \u003cp\u003eDespite the promising findings achieved in this study, several limitations remain to be addressed in subsequent research. First, all the data utilized in this study were derived from public databases (TCGA and GEO), and the sample size of the validation cohort was relatively small. Future studies should incorporate multicenter, large-sample prospective cohorts to further validate the model performance. Second, the screening of core TRPGs was based on bioinformatics analysis, and the specific molecular mechanisms underlying their regulation of cervical cancer progression still require experimental verification (e.g., in vitro cell line experiments and in vivo animal models). Third, the model established herein adopted overall survival as the primary endpoint; future research could incorporate other clinical outcomes such as progression-free survival and chemotherapy response rate to enhance the clinical utility of the model. Fourth, this study did not explore the potential of TRPGs as therapeutic targets. Subsequent studies may conduct in-depth investigations into the therapeutic efficacy of TRP channel inhibitors in cervical cancer cells with high expression of core risk genes.\u003c/p\u003e \u003cp\u003eIn summary, a novel TRP channel-related gene signature for prognostic prediction in cervical cancer was effectively developed and verified in this investigation. It shows significant predictive efficacy and practical application. This signature not only provides a novel tool for risk stratification and individualized treatment in cervical cancer patients but also offers new insights into elucidating the molecular mechanisms of tumor progression mediated by TRP channels. With further validation and mechanistic exploration, this prognostic model is expected to be translated into clinical applications, thereby improving the therapeutic outcomes of cervical cancer patients.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, we devised a TRP-based molecular clustering system and prognostic signature that facilitates survival prediction, immunotherapy guidance, and clinical outcome determination. This study is expected to deepen the understanding of the functions of sodium channel-related genes in cervical cancer and accelerate the development of more effective therapeutic strategies against this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis work was supported by the Guangxi Medical and health key discipline construction project and Guangxi Medical and health key cultivation discipline construction project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor notes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhengchao Yan and Sijuan Tang should be considered joint first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Clinical Laboratory, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin\u0026nbsp;541002, China.\u003c/p\u003e\n\u003cp\u003eZhengchao Yan, Jianlin Chen, Naqiu Yin, Yufei Pan, Yulong He, Ying Wan, Lili Su, Minhui He, Yue Li, Jianbin Yang \u0026amp; Liwu Zhang\u003c/p\u003e\n\u003cp\u003eDepartment of Laboratory Medicine, The Second Affiliated Hospital of Guilin Medical University, Guilin, China\u003c/p\u003e\n\u003cp\u003eSijuan Tang\u003c/p\u003e\n\u003cp\u003eDepartment of Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions:\u0026nbsp;\u003c/strong\u003eZhengchao Yan and Sijuan Tang contributed equally to this work. Naqiu Yin, Yufei Pan and Yulong He conceived and designed the study.\u0026nbsp;Ying Wan, Lili Su, Yue Li and Minhui He contributed to performing the experiments and developing methodology.\u0026nbsp;Zhengchao Yan and Sijuan Tang contributed to the writing, reviewing, and revision of the paper. Jianlin Chen and Liwu Zhang acquired funding and supervised the study. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u0026nbsp;\u003c/strong\u003eCorrespondence to Jianlin Chen and Liwu Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset GSE44001 analyzed in this study is available in the GEO repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44001]. and The Cancer Genome Atlas Program (TCGA) [https://portal.gdc.cancer.gov/]，including the TCGA-CESC project (https://portal.gdc.cancer.gov/projects/TCGA-CESC).The data supporting the findings of this study are available in the GeneCards platform (https://www.genecards.org/Search/Keyword?queryString=sodium%20overload). To facilitate efficient access to raw data, we added clear Point of Contact information: Point of Contact:Zhengchao Yan. Email Address: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\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 no competing interests.Conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information:\u0026nbsp;\u003c/strong\u003eTable S1: Clinical data of cervical cancer patients; Table S2: Sodium channel-related genes\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng Q, Yang W, Su G, Wu F, Xing C. 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PLoS ONE. 2018;13(7):e0199034.\u003c/span\u003e\u003c/li\u003e\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, TRGs, Prognosis Model, Molecular Subtypes","lastPublishedDoi":"10.21203/rs.3.rs-8553093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8553093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTransient Receptor Potential (TRP) channels are hypothesized to be associated with cancer progression. This study aimed to develop a prognostic model for cervical cancer (CESC) utilizing genes related to TRGs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) libraries were employed for determining the training and validation cohorts, respectively. Transcriptome profiles, clinical data, and copy-number variation (CNV) datasets have been obtained from people suffering from cervical squamous cell carcinoma (CESC). Lasso-Cox regression analysis was used to determine the -risk score based on predicting gene expression levels, and survival analysis was used to ascertain the overall difference in survival between the high- and low-risk groups. Single-cell sequencing RNA information from the TISCH database was analyzed using the Seurat software. The software suites GSVA, ClusterProfiler, and IOBR were utilized for functional phenotypic analysis. The patients were split into two groups using consensus clustering. The clinicopathological features were then compared, and an investigation of biological function was carried out. Applying the Kaplan-Meier curve along with the log-rank test, the predictive value of genes was ascertained. The immune situation was the focus of the ensuing inquiry. Additionally, we looked at the connections between the tumor microenvironment adjustment, gene functional enrichment analysis, and TRGs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTen TRP channel genes (TRGs) were included in a predictive risk model. Patients classified into various risk groupings exhibited notable differences in molecular characteristics and clinical symptoms. In particular, the high-risk group had a dire outlook and a higher cancer mutation burden (TMB). Single-cell RNA sequencing (scRNA-seq) analysis results pointed out that high-risk and low-risk cell populations differed significantly in numerous variables. In order to deeper comprehend the molecular regulatory processes underpinning risk subtypes, our study established an aggressive endogenous RNA (ceRNA) protective network. When regarded as a whole, TRG-related gene targeting might serve as a potential therapy approach for cervical cancer (CC).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe have successfully established a high-precision prognostic model for predicting overall survival and treatment efficacy using TRP channel-related genes.\u003c/p\u003e","manuscriptTitle":"Through Integrated Bioinformatics Analysis to Explore the Prognostic Role of TRP Channel Genes in Cervical Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:33:07","doi":"10.21203/rs.3.rs-8553093/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":"67a286e3-d97b-448e-9ddf-d83d7698fe47","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T06:56:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:33:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8553093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8553093","identity":"rs-8553093","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}