Analysis of Prognostic and Immunological Relevance of Autophagy-Related lncRNAs in Uterine Corpus Endometrial Carcinoma Based on TCGA Database

Analysis of Prognostic and Immunological Relevance of Autophagy-Related lncRNAs in Uterine Corpus Endometrial Carcinoma Based on TCGA Database | 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 Analysis of Prognostic and Immunological Relevance of Autophagy-Related lncRNAs in Uterine Corpus Endometrial Carcinoma Based on TCGA Database Xiaochao Fu, Ning Liu, Yanyan Gao, Di Wang, Xu Wu, Ji Sun, Zhibin Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7822538/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 Objective To identify autophagy-related long non-coding RNAs (ARLs) that affect the prognosis of uterine corpus endometrial carcinoma (UCEC) based on The Cancer Genome Atlas (TCGA) database, and to construct a prognostic prediction model for UCEC patients. Methods Transcriptome data and clinical information of UCEC were obtained from the TCGA database. Differentially expressed ARLs in UCEC were identified using differential expression analysis and correlation analysis. Cox analysis and least absolute shrinkage and selection operator (LASSO) algorithm were applied to determine prognostically significant ARLs, based on which a prognostic model for UCEC was constructed. The prognostic performance of the model was evaluated using Kaplan-Meier survival curve analysis, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Additionally, the relationships between the model and tumor microenvironment, immune infiltrating cells, and immune checkpoint genes were explored. Moreover, consensus clustering analysis was used to define molecular subtypes based on prognostic ARLs. Finally, functional enrichment analysis was performed on key prognostic ARLs. Results A predictive model containing 12 prognostic signature ARLs was constructed. Patients in the high-risk group had shorter overall survival, and immune function in tumor tissues was downregulated in the high-risk group. Furthermore, UCEC was classified into 5 significantly distinct molecular subtypes, among which subtypes 1 and 3 were significantly associated with autophagy and had better prognosis. Finally, functional enrichment analysis confirmed that knockdown of CDKN2B-AS1 and XPC-AS1 regulates autophagic activity through ciliary movement on the cell surface. Conclusion This study constructed a UCEC prognostic model composed of 12 autophagy-related lncRNAs, which is associated with immune function regulation and molecular subtypes of UCEC. CDKN2B-AS1 and XPC-AS1, as key prognostic markers, have the potential to be applied in risk stratification and precision therapy for UCEC patients. Uterine corpus endometrial carcinoma Autophagy Immunity Long non-coding RNA Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Uterine corpus endometrial carcinoma (UCEC) accounts for 90% cases of uterine cancer (UC) and primarily arises from abnormal endometrial hyperplasia [ 1 ] . It is the most common gynecological malignancy and ranks sixth among all cancers affecting women. Driven by the aging population and rising obesity rates, the incidence of UCEC has been on a steady rise in high-income countries, a trend also observed in low- and middle-income countries. It is estimated that the incidence and mortality rates of UCEC will continue their upward trajectory over the next few decades [ 2 ] . Marked prognostic differences were observed among UCEC patients. By stage, the five-year survival rate of UCEC patients ranges from 95% in those with disease localized to the primary site to 18% in those with distant metastasis [ 3 ] . Additionally, UCEC exhibits histological heterogeneity, and The Cancer Genome Atlas (TCGA) classified it into four molecular subtypes: polymerase ε mutation (POLEmut), high microsatellite instability (MSI-H), TP53 gene abnormality (P53abn), and no specific molecular profile (NSMP) [ 2 ] . Patients with these four subtypes exhibit significant prognostic differences. Developing targeted and immunotherapeutic regimens tailored to the distinct molecular characteristics of different stages and subtypes facilitates improved prognosis for UCEC patients and advances precision medicine. However, beyond using clinicopathological staging and TCGA molecular subtypes as bases for risk stratification and treatment response assessment in UCEC patients, in-depth exploration of subtle molecular differences in UCEC further supports personalized diagnosis and treatment for patients and optimization of current therapeutic approaches. Autophagy is a highly conserved form of programmed cell death. This process involves double-membraned cytoplasmic vesicles known as autophagosomes sequestering unfolded proteins or damaged organelles, which are then transported to lysosomes for degradation, with the resulting macromolecules released back into the cytoplasm for recycling [ 4 ] . Autophagy and its related pathways played a crucial role in the occurrence and progression of multiple malignancies, including clear cell renal cell carcinoma [ 5 ] , colorectal cancer [ 6 ] , ovarian cancer [ 7 ] , wherein they exerted tumor-suppressive, tumor-promoting, or bidirectional effects. Long non-coding RNAs (lncRNAs) are non-protein-coding transcripts exceeding 200 nucleotides in length. As regulatory factors, they participate in gene expression as well as a variety of physiological and pathological processes. Accumulating evidence indicated that lncRNAs exerted complex yet precise regulatory roles in cancer initiation and progression by acting as either oncogenes or tumor suppressor genes. Multiple studies confirmed that programmed death-related lncRNAs facilitated predicting UCEC prognosis and guiding treatment responses, such as those associated with necroptosis [ 8 ] , cuproptosis [ 9 ] and ferroptosis [ 10 ] . However, for autophagy, a key form of cell death, there remains a lack of research on the regulatory functions of autophagy-related lncRNAs in UCEC progression and their role in prognosis prediction. This study utilized autophagy-related long non-coding RNAs (ARLs) to develop a predictive model for forecasting the prognosis of UCEC patients and conducted immunological correlation analyses. Methods Data acquisition and procession Transcriptomic data and clinical information of UCEC were downloaded from TCGA database, including 35 cases of normal endometrial tissues and 554 cases of UCEC tissues. Perl software was used to split the transcriptomic data into two datasets: messenger RNA (mRNA) and long non-coding RNA (lncRNA). A total of 367 autophagy-related genes (ARGs) were obtained from published literature [ 11 , 12 ] , and the "limma" R package was employed to extract the expression levels of these ARGs in the TCGA-UCEC dataset. Identification and differential expression analysis of autophagy-related lncRNAs The "limma" R package was used to screen the lncRNA expression matrix between UCEC samples and normal endometrial samples. Differentially expressed autophagy-related lncRNAs (DEARLs) were identified using the criteria of |log2Fold Change| >1 and false discovery rate (FDR) < 0.05. The top 50 ARLs with the most significant expression differences were visualized via heatmaps. The "limma" R package was further employed to analyze the correlation between the ARG expression matrix and lncRNA dataset. Using the screening criteria of R² >0.5 and p < 0.001, the ARL expression matrix was finally obtained. Identification of Prognosis-Associated ARLs First, the prognostic value of DEARLs was evaluated via univariate Cox regression analysis. DEARLs with P < 0.05 in the univariate analysis were included in the least absolute shrinkage and selection operator (LASSO) regression algorithm, yielding prognostic signature DEARLs. Patients in the TCGA-UCEC dataset were randomly divided into a training cohort (n = 261) and a validation cohort (n = 261) at a 1:1 ratio. They were further stratified into high-risk and low-risk groups based on the median risk score. Log-rank tests were performed using the "survminer" R package to compare survival differences between the two groups in the training and validation cohorts, respectively. Immune-Infiltrating Cell and Tumor Microenvironment Analysis Seven algorithms, including CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER, and EPIC, were used to assess immune cell scores across different samples, aiming to analyze the correlation between risk scores and immune-infiltrating cells. The ESTIMATE algorithm was employed to calculate three scores for each sample: immune score, stromal score, and ESTIMATE score (sum of stromal score and immune score for a single sample). These scores were used to compare tumor microenvironment differences between the high- and low-risk groups. The "GSVA" R package was utilized to compute single-sample gene set enrichment analysis (ssGSEA) scores for each sample, which helped compare immune function differences between the two groups. Additionally, the "limma" R package was used to compare the expression differences of immune checkpoint genes between the high- and low-risk groups. Consensus Clustering Analysis UCEC patients were stratified into distinct molecular subtypes using the "ConsensusClusterPlus" R package, with 1000 iterations and an 80% resampling rate. The gene expression patterns of these distinct UCEC molecular subtypes were evaluated via principal component analysis (PCA). Meanwhile, the "ggalluvial" R package was used to visualize the association between different subtype classifications and high/low-risk groups. Kaplan-Meier curves were plotted to analyze survival differences among different subgroups, thereby assessing their prognostic significance. Additionally, differences in immune-infiltrating cells, tumor microenvironment, and immune checkpoint gene expression were compared across UCEC samples of distinct molecular subtypes. Functional Enrichment Analysis Key markers in the prognostic model were the focus of analysis. Patients were divided into high- and low-expression groups based on the median expression level of these markers. Differentially expressed genes between the two groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the "ClusterProfiler" R package. The "ClusterProfiler" R package was also used to analyze HALLMARK pathways significantly enriched between the high- and low-expression groups of key markers. The screening criteria were set as p < 0.05, FDR 1. Statistical Analysis Statistical analyses were performed using R software (v. 4.4.1). Chi-square tests were used for comparing categorical variables, while Student’s t-tests or one-way analysis of variance (ANOVA) were applied for continuous variables. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent prognostic factors associated with overall survival (OS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the predictive specificity, accuracy, and clinical utility of the prognostic model, respectively. Statistical significance was defined as P < 0.05. Results Identification of ARLncRNAs in UCEC Among 554 TCGA-UCEC samples, 16,773 lncRNAs and 19,896 mRNAs were identified, respectively. A gene set containing 367 ARGs (involved in autophagy) was obtained from published literature. Spearman correlation analysis was performed on lncRNAs in the TCGA database with the inclusion criteria of r > 0.5 and P 1 and FDR < 0.05. Among these, 416 were upregulated and 259 were downregulated (Fig. 1 B). The top 50 DEARLs with the most significant expression differences were visualized via a heatmap (Fig. 1 C). Prognosis-Associated DEARLs in the TCGA-UCEC dataset To validate the prognostic value of DEARLs, survival data of UCEC patients from the TCGA database were used. Patients were grouped based on the median expression level of each DEARL, and univariate Cox proportional hazards regression analysis identified 53 DEARLs associated with UCEC prognosis (Fig. 2 A). Among these 53 DEARLs, 48 were prognostic risk lncRNAs, while only XPC-AS1, AL390195.2, AC027237.3, USP30-AS1, and LINC01765 were prognostic protective genes. A Sankey diagram illustrated the relationships between prognosis-associated DEARLs, ARGs, and their prognostic roles in UCEC (Fig. 2 B). TCGA-UCEC samples were randomly divided into a training cohort and a validation cohort at a 1:1 ratio. Based on the 53 DEARLs in the training cohort, a prognostic risk assessment model featuring 19 DEARLs was established using the optimal penalty parameter (λ) of the LASSO regression model. The cross-validation fitting (cvfit) and λ curves are shown in Figs. 2 C and D . Further multivariate Cox regression analysis confirmed that the expression of 12 of these DEARLs were independent risk factors for UCEC patient prognosis. In this model, the risk score for each UCEC patient was calculated using the following formula: Risk score = AC105219.1 × 0.67056 + AC004233.3 × 0.42015 + XPC-AS1 × (-3.24191) + AC139149.1 × 0.71979 + AC103563.2 × 0.37281 + AL359715.2 × 0.630363515195829 + AC068189.1 × 0.83445 + AC244517.7 × 0.49014 + AC027237.3 × (-0.55668) + CDKN2B-AS1 × 1.57889 + AL390195.2 × (-0.4353425797735) + AC130352.1 × 1.49236 (Note: lncRNA names represent their expression levels in the TCGA database) (Fig. 2 E). Construction and Evaluation of the ARL-Associated Prognostic Model Samples from the training, validation, and total cohorts were stratified into high- and low-risk groups based on the median risk score. Patient status distribution plots and risk score curves showed the grouping of samples in different cohorts ( Figs. 3 A, B and C ). Kaplan-Meier survival analysis revealed that OS of UCEC patients in the high-risk group was significantly shorter than that in the low-risk group across all three cohorts (Log-rank P < 0.001; Figs. 3 D, E and F ). A heatmap displayed the expression distribution of the 12 DEARLs in high- and low-risk groups across different cohorts (Figs. 3 G, H and I ). To facilitate prognosis risk prediction for UCEC patients using this risk score formula, a nomogram was constructed based on the expression levels and risk coefficients of the 12 DEARLs (Fig. 4 A). The nomogram converted the expression levels of the 12 DEARLs into corresponding scores, and the total score of all genes was mapped to 1-year, 3-year, and 5-year OS rates. ROC curves showed that the model exhibited good predictive specificity for the survival of UCEC patients in the training and validation cohorts (Figs. 4 B and C ). Calibration curves demonstrated high consistency between the predicted and actual outcomes (Figs. 4 D and F ). Decision curves indicated good clinical utility of the model (Figs. 4 E and G ). These results confirmed that the prognostic model based on the expression of 12 DEARLs had good predictive value for the survival outcomes of UCEC patients. Immune Analysis Based on the DEARL Prognostic Model To further explore the relationship between the ARL-associated prognostic model and anti-tumor immunity in UCEC patients, seven algorithms (CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER, and EPIC) were applied to calculate immune cell scores of TCGA-UCEC samples. Spearman tests were used to analyze the correlation between immune-infiltrating cells (based on different algorithms) and risk scores (Fig. 5 A). Bubble plots showed that the risk score of the predictive model was negatively correlated with various immune-infiltrating cells. Comparison of immune cell proportions between high- and low-risk groups revealed that the proportion of activated dendritic cells was increased in the high-risk group, while the proportions of CD8 + T cells, immature and plasmacytoid dendritic cells, mast cells, natural killer cells, helper T cells, tumor-infiltrating lymphocytes, and regulatory T cells were higher in the low-risk group (Fig. 5 C). Immune function comparison indicated that multiple immune functions were impaired in the high-risk group, including antigen-presenting cell co-stimulation, CC chemokine receptors, immune checkpoints, cytolytic activity, human leukocyte antigen, T cell co-inhibition and co-stimulation, and type I/II interferon responses (Fig. 5 B). To clarify the relationship between risk scores and the tumor microenvironment, stromal scores, immune scores, and ESTIMATE scores were compared. Results showed that all three scores were significantly lower in the high-risk group (P < 0.001; Figs. 5 D, E and F ). Additionally, comparison of immune checkpoint gene expression between high- and low-risk groups revealed that among 47 immune checkpoint genes, most (e.g., CD44, CD40LG, CD200, CD86, CD28, CD276, CD48, CD70, CD27, CD244, LGALS9, TMIGD2, TIGIT, ICOS, ICOSLG, TNFSF15, TNFRSF9, TNFRSF14, LAIR1, IDO1, LAG3, CTLA4, HAVCR2, NRP1, and BTLA) were upregulated in the low-risk group, while only CD40 was upregulated in the high-risk group (Fig. 5 G). These results demonstrated that the DEARL-based risk score was significantly correlated with the proportion of immune-infiltrating cells and components of the tumor microenvironment in UCEC tissues. Consensus Clustering Analysis Consensus clustering analysis of UCEC patients was performed using the "ConsensusClusterPlus" R package. Consensus clustering matrix plots (Fig. 6 A), tracking plots (Fig. 6 B), cumulative distribution function (CDF) curves (Fig. 6 C), and changes in the area under the CDF curve (Fig. 6 D) indicated that k = 5 was the optimal classification, dividing UCEC patients into 5 distinct subtypes. Compared with patients of other subtypes, those of clusters 1 and 3 exhibited higher survival rates, suggesting an association between UCEC subtypes and clinical outcomes (Log-rank P < 0.001; Fig. 6 E). To clarify the relationship between each molecular cluster and the DEARL-based risk score, Sankey diagrams (Fig. 6 F), PCA plots (Fig. 6 G), and t-SNE plots (Fig. 6 H) showed that most samples of Subtypes 1 and 3 belonged to the low-risk group, while samples of the other 3 subtypes were mainly in the high-risk group. Immune scores (Fig. 7 A), stromal scores (Fig. 7 B), and ESTIMATE scores (Fig. 7 C) of samples from clusters 1 and 3 were significantly different from those of other subtypes (P < 0.05). A heatmap illustrated the correlation between different clusters and immune cells based on immune cell scores from the 7 algorithms (Fig. 7 D). Comparison of immune checkpoint gene expressions across molecular clusters revealed that 27 immune checkpoint genes showed differential expression among subtypes (Fig. 7 E). These results suggested that UCEC molecular subtypes were inherently associated with the ARL prognostic model, and the classification was related to patient clinical outcomes and immune function regulation. Survival Analysis and Functional Enrichment Analysis of Key Prognostic Factors Among the prognostic model constructed with 12 independent prognostic factors, cyclin-dependent kinase inhibitor 2B-antisense 1 (CDKN2B-AS1) and Xeroderma pigmentosum complementation group C-antisense 1 (XPC-AS1) were the most critical prognostic risk factor and protective factor, respectively. The prognostic risk factor CDKN2B-AS1 was upregulated in UCEC tissues (Fig. 8 A). Patients with high CDKN2B-AS1 expression had significantly shorter progression-free survival (PFS, Fig. 8 B), overall survival (OS, Fig. 8 C), disease-free survival (DFS, Fig. 8 D), and disease specific survival (DSS, Fig. 8 E) than those with low expression (all Log-rank P < 0.05). GO enrichment analysis revealed that upregulated CDKN2B-AS1 expression was significantly associated with cilia movement-related cellular processes in biological processes (Fig. 8 F). KEGG enrichment analysis indicated that CDKN2B-AS1 was involved in mechanisms such as the neuroactive ligand-receptor signaling pathway, cAMP signaling pathway, cornified envelope formation, and motor proteins (Fig. 8 G). GSEA results showed that upregulated CDKN2B-AS1 expression enhanced the transforming growth factor-beta (TGF-beta) pathway and negatively regulated MYC targets V2 (Figs. 8 H and I ). The prognostic protective factor XPC-AS1 was significantly downregulated in UCEC tissues, and its downregulation was associated with shorter OS in patients (Fig. 9 A and B ). GO enrichment analysis suggested that XPC-AS1 was also involved in cilia movement-related biological processes, which was similar to the biological function of CDKN2B-AS1 (Fig. 9 C). Additionally, KEGG enrichment analysis indicated that XPC-AS1-related differentially expressed genes were enriched in neuroactive ligand-receptor interaction and motor protein pathways (Fig. 9 D). GSEA enrichment analysis suggested that XPC-AS1 was also involved in the regulation of MYC-related pathways (Fig. 9 E). These results showed that the most critical prognostic risk factor and protective factor in the DEARL-based prognostic model were collectively involved in cilia movement mechanisms. Discussion Previously, UCEC was simply classified into Type I and II based on estrogen exposure, and this classification has some predictive value for pathological grading and patient prognosis. Subsequently, the TCGA classification, established based on molecular characteristics, classified UCEC into four distinct molecular subtypes. Beyond enabling more accurate prediction of patients’ clinical outcomes, it also facilitated guiding treatment decisions for UCEC patients. However, the molecular classification of UCEC still has potential for further subdivision. On the one hand, 3–6% of patients may belong to two molecular subtypes simultaneously [ 13 ] , a scenario that impairs the prediction of prognosis and treatment response. For instance, UCEC patients who exhibited the characteristics of both POLEmut and P53abn molecular subtypes had a prognosis similar to that of POLEmut subtype patients, and thus should be managed as POLEmut subtype patients [ 14 ] . Similarly, patients with both dMMR and P53abn features should be classified as the dMMR subtype [ 15 ] . On the other hand, 40–50% of UCEC patients who simultaneously exhibited p53 wild-type, mismatch repair proficiency, and absence of POLE mutations are defined as the NSMP subtype [ 16 ] . Further exploration of their potential pathogenic mechanisms and key biomarkers holds significant value for risk stratification and treatment selection in these patients. This study conducted an in-depth investigation into the relationship between ARLs and the prognosis of UCEC patients. Screening via differential expression analysis and correlation analysis with ARGs yielded 675 DEARLs. Further, Cox regression and LASSO regression were employed to identify 12 DEARLs as independent risk factors for UCEC prognosis, and a prognostication model with clinical utility was developed. Immune and clustering analyses clarified differences in immune function between distinct molecular subtypes and between high- and low-risk groups. Most samples of clusters 1 and 3 were categorized into the low-risk group, exhibiting relatively favorable prognosis. Additionally, antigen presentation and immune cytotoxicity functions were upregulated in these samples. Finally, functional enrichment analysis focused on key prognostic markers (CDKN2B-AS1 and XPC-AS1) in the prognostication model, revealing that the expression of these two genes was closely associated with the regulation of ciliary movement and cellular motility. Autophagy is a process that degrades and recycles intracellular components to meet cellular metabolic and self-renewal needs. It plays a crucial role in the quality control of cytoplasmic components and the maintenance of cellular homeostasis, and is closely associated with cell differentiation and development, immune function, as well as tumor initiation and progression. In normal endometrium, autophagy is involved in the cyclic changes of the endometrium during the menstrual cycle. Activation of autophagy in endometrial glandular cells induces cell death, which plays a vital role in menstrual preparation and tissue regeneration [ 17 ] . Autophagy exerts a dual role in the development of UCEC. UCEC cells with mutations in certain ARGs (e.g., ATG4C, RB1CC1/FIP200, ULK4) exhibit reduced autophagy, which contributed to the development of Type I UCEC and poor patient prognosis [ 18 ] . However, Sivridis et al. observed a significant increase in the number of "stone-like structures" (a marker of autophagic activity) in Type II UCEC tissues compared to normal endometrial tissues, and this increase was associated with poor patient prognosis [ 19 ] . With accumulating studies confirming the aberrant expression of numerous lncRNAs in various tumors, the association between lncRNAs and autophagy has attracted particular attention. LncRNAs primarily mediate the regulation of autophagy through mechanisms such as microRNAs (miRNAs), RNA-RNA interactions, and RNA-protein regulation [ 20 ] . Furthermore, lncRNAs were involved in multiple stages of autophagy. They first mediated autophagy initiation by regulating the expression of ULK1, mTOR, and Beclin-1, and subsequently promoted autophagy elongation by modulating the expression of ATG3, ATG5, ATG4, ATG12, and ATG7 [ 21 ] . These mechanisms underpin the regulation of the malignant phenotype of tumor cells by ARLs. In gastric cancer, the lncRNA SNHG11, which is highly expressed, post-transcriptionally upregulates ATG12 expression via miR-1276, thereby enhancing autophagy and proliferation. It further activates the Wnt/β-catenin signaling pathway [ 22 ] . Yu et al. found that knockdown of lncRNA SNHG3 activates autophagy, which in turn inhibits the migration and invasion of breast cancer cells [ 23 ] . This study identified prognosis-associated ARLs in UCEC for the first time and developed a prognostic prediction model based on these genes. This model contains not only prognostic protective factors such as XPC-AS1 but also prognostic risk factors including CDKN2B-AS1. Functional enrichment analysis in this study revealed that both of these two ARLs are associated with ciliary movement and cellular motility. Furthermore, numerous studies have confirmed that ciliary movement is associated with cell cycle regulation and signaling pathway networks in multiple types of tumors [ 24 ] . Among these, the interaction between autophagy and cilia in tumors is of great significance. On one hand, the autophagic activity of tumor cells can regulate cilia formation, thereby contributing to tumorigenesis. On the other hand, the loss of cilia impairs autophagic activity [ 24 ] . An in vitro and in vivo study confirmed that CDKN2B-AS1 is significantly overexpressed in UCEC. Knockdown of this gene inhibits the proliferation and invasion of tumor cells, as well as the growth of xenografts in nude mice [ 25 ] . The development of these prognostic markers lays the foundation for the further subdivision of UCEC molecular subtypes; however, further in-depth research is still needed to explore their underlying mechanisms. Additionally, this study also analyzed the involvement of ARLs in the regulation of UCEC immune function. In the ARL-based model, immune cell infiltration was reduced in high-risk group samples, and immune functions including chemokine receptors and cytolytic activity were downregulated. Among different molecular subtypes, Subtypes 1 and 3 also exhibited similar changes in immune function. These results confirm that ARLs have the potential to serve as immune markers for UCEC. Multiple studies have demonstrated that ARLs not only facilitate the development of prognostic models for various tumors but also are closely associated with immune cell infiltration, tumor microenvironment changes, and the regulation of tumor immune function [ 26 – 28 ] . This is because lncRNAs are involved in key mechanisms of tumor immunity. Ye et al. found via multi-omics analysis that CDKN2B-AS1 was primarily expressed in dendritic cells (DCs) of malignant gliomas and was associated with the DNA repair pathway in malignant cells as well as antigen-presenting genes in DCs [ 29 ] . This suggests its potential as a key regulator of immune responses and tumor suppressor genes. This study has certain limitations. First, it is a bioinformatics analysis based on the TCGA database, and in vitro and in vivo experiments are still required to verify the biological functions and immunomodulatory effects of ARLs. Second, external multi-center cohort studies can better validate the value of the ARL-based prognostic model for the prognosis of UCEC patients. Conclusion In conclusion, this study developed a UCEC prognostic model composed of 12 ARLs, which is associated with the immune function regulation and molecular subtypes of UCEC. CDKN2B-AS1 and XPC-AS1 have been identified as key prognostic markers that affect tumor progression, and hold potential for application in risk stratification and precision treatment of UCEC patients. Declarations Ethics approval and consent The data used in this article were all from online databases and therefore did not involve ethics. funding This work got support from the Scientific Research Program of Traditional Chinese Medicine in Hebei Province (NO.2024254). The funding sponsor reviewed and approved the study design and the final manuscript. Author contributions WL and JY designed the study. XF, NL and YG executed data analysis and visualization. DW, XW and JS made the visualization. XF wrote the manuscript. WL and ZJ made a revision on this manuscript. WL and XF were responsible for confirming whether the data were reliable and authentic. The manuscript has been read by all named authors and obtained their approval. Competing interests No financial conflicts of interest to disclose. Data availability statement Data is available upon reasonable request from the corresponding author. References MAKKER V, MACKAY H, RAY-COQUARD I, et al. Endometrial cancer [J]. 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SNP rs615552 and lncRNA CDKN2B-AS1 influence brain cancer pathogenesis through multi-omic mechanisms [J]. Scientific reports, 2025, 15(1): 27490. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:36:34","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90329,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/038e4d5df2db131eb8453f79.html"},{"id":96181307,"identity":"f78f0d9b-1ac9-4255-ad98-be0487b5957f","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5611866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutophagy-related long non-coding RNAs (ARLs) with differential expression in the TCGA-UCEC dataset. A\u003c/strong\u003e Distribution of autophagy-related long non-coding RNAs (ARLs). \u003cstrong\u003eB\u003c/strong\u003e Volcano plot shows 675 differentially expressed ARLs screened based on the criteria of |log₂Fold Change| \u0026gt; 1 and false discovery rate (FDR) \u0026lt; 0.05. \u003cstrong\u003eC\u003c/strong\u003e Heatmap presents the top 50 ARLs with significant differential expression between endometrial cancer (UCEC) and normal endometrial tissues.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/85190707fe98951d1808a910.png"},{"id":96252193,"identity":"58f1e752-df7d-4453-a4d9-a244b9d48060","added_by":"auto","created_at":"2025-11-19 07:40:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2549160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognosis-related differentially expressed autophagy-related long non-coding RNAs (ARLs). A\u003c/strong\u003e Forest plot shows 53 ARLs with differential prognosis and expression. \u003cstrong\u003eB, C\u003c/strong\u003e 19 differentially expressed ARLs with prognostic signatures screened via LASSO regression analysis. \u003cstrong\u003eD\u003c/strong\u003e Bar plot shows the survival coefficients of 12 independent prognostic factors identified by multivariate analysis.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/3471cd14847b117ed8b24c8f.png"},{"id":96181308,"identity":"85daec5f-d261-4f7b-8378-b5b08dcf9ab5","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1588208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves and related analyses.\u003c/strong\u003e \u003cstrong\u003eA, B and C\u003c/strong\u003e Distribution of patient survival status and risk scores in the training cohort(A), validation cohort(B), and total cohort(C), respectively. \u003cstrong\u003eD, E and F\u003c/strong\u003e Kaplan-Meier survival curves illustrating differences in overall survival rates between high- and low-risk groups in the training cohort(D), validation cohort(E), and total cohort(F), respectively.\u003cstrong\u003e G, H and I \u003c/strong\u003eHeatmaps displaying the expression of the 12 independent prognostic factors in high- and low-risk groups within the training cohort(G), validation cohort(H), and total cohort(I), respectively.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/a9a56cbd5a0b7fda3028335a.png"},{"id":96181312,"identity":"c298fdee-8636-44a2-985e-66fee1d256cb","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":865818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Evaluation of the Autophagy-Related lncRNA-Based Prognostic Model. A\u003c/strong\u003e Nomogram constructed based on the expression of 12 independent prognostic factors. \u003cstrong\u003eB, C\u003c/strong\u003e ROC curves for the training cohort (B) and validation cohort(C), respectively. \u003cstrong\u003eD, E\u003c/strong\u003e Calibration curves for the training cohort(D) and validation cohort(E), respectively. \u003cstrong\u003eF, G\u003c/strong\u003e Decision curves for the training cohort (F) and validation cohort(G), respectively.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/11b738f5a083b2261e02cf4c.png"},{"id":96249810,"identity":"8c3c1c38-083f-4515-a7ed-5bbc18280908","added_by":"auto","created_at":"2025-11-19 07:36:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1566992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Analysis Between High- and Low-Risk Groups.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003eThe bubble plot showing the correlation between immune-infiltrating cells and the risk scores of the prognostic model. \u003cstrong\u003eB\u003c/strong\u003e The box plot showing differences in 13 immune functions between high- and low-risk groups. \u003cstrong\u003eC\u003c/strong\u003eThe box plot showing differences in 16 types of immune cell infiltration between high- and low-risk groups. \u003cstrong\u003eD, E and F\u003c/strong\u003e The box plots showing differences in immune scores(D), stromal scores(E), and ESTIMATE scores(F) between high- and low-risk groups, respectively. \u003cstrong\u003eG\u003c/strong\u003e The box plot showing differences in the expression of immune checkpoint genes between high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/9a5fe7a354143af254379b8e.png"},{"id":96181317,"identity":"c7556abc-3af5-4038-81cc-cccf7623a21c","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1373127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus Clustering Analysis. A-D\u003c/strong\u003e The determination of the optimal K value derived from the consensus clustering analysis. \u003cstrong\u003eE\u003c/strong\u003e \u003cstrong\u003eKaplan-\u003c/strong\u003eMeier curves Illustrateing survival differences among the five subtypes. \u003cstrong\u003eF, G and H \u003c/strong\u003eSankey plot (F), PCA map(G) and tSNE map (H) illustraing the association between molecular subtypes and risk groups.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/d69ce0b75d477f3178c6b2ec.png"},{"id":96181309,"identity":"5c006b9c-e296-43bd-987b-eca990ccef41","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2836826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Analysis Between UCEC Molecular Subtypes. A, B and C\u003c/strong\u003e The box plots showing differences in immune scores(A), stromal scores(B), and ESTIMATE scores(C) among molecular subtypes, respectively. \u003cstrong\u003eD\u003c/strong\u003eHeatmap showing the correlation between immune-infiltrating cells and molecular subtypes. \u003cstrong\u003eE\u003c/strong\u003e The box plot illustrating differences in the expression of immune checkpoint genes among molecular subtypes.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/227a59e912fa56597225a0ec.png"},{"id":96181313,"identity":"be8befc4-9cab-40c5-bc59-4aa4cd6098f1","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1458922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic and Molecular Functional Analysis of CDKN2B-AS1. A\u003c/strong\u003e The expression of CDKN2B-AS1 in UCEC. \u003cstrong\u003eB-E\u003c/strong\u003e Kaplan-Meier survival analyses for CDKN2B-AS1 in the term of PFS(B), OS(C), DFS(D), and DSS(E). \u003cstrong\u003eF\u003c/strong\u003e GO enrichment analysis. \u003cstrong\u003eG\u003c/strong\u003e KEGG enrichment analysis.\u003cstrong\u003eH, I\u003c/strong\u003e GSEA.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/0299e1cf9d1872235c09bd36.png"},{"id":96181314,"identity":"7f4e8964-b898-4495-93b8-f7df306e2582","added_by":"auto","created_at":"2025-11-18 12:36:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":865018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic and Molecular Functional Analysis of XPC-AS1. A\u003c/strong\u003e The expression of XPC-AS1 in UCEC. \u003cstrong\u003eB\u003c/strong\u003e Kaplan-Meier curves for XPC-AS1 in the term of OS. \u003cstrong\u003eC\u003c/strong\u003e GO enrichment analysis. \u003cstrong\u003eD\u003c/strong\u003e KEGG enrichment analysis. \u003cstrong\u003eE\u003c/strong\u003e GSEA.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/0fe277a80b5f4751639e41d8.png"},{"id":100356140,"identity":"95c18cfe-1aba-474b-bebb-420d4a9399e9","added_by":"auto","created_at":"2026-01-16 06:53:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20216649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7822538/v1/c14ffe96-62e4-428d-b074-e25354d3fed2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Prognostic and Immunological Relevance of Autophagy-Related lncRNAs in Uterine Corpus Endometrial Carcinoma Based on TCGA Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC) accounts for 90% cases of uterine cancer (UC) and primarily arises from abnormal endometrial hyperplasia\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It is the most common gynecological malignancy and ranks sixth among all cancers affecting women. Driven by the aging population and rising obesity rates, the incidence of UCEC has been on a steady rise in high-income countries, a trend also observed in low- and middle-income countries. It is estimated that the incidence and mortality rates of UCEC will continue their upward trajectory over the next few decades\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Marked prognostic differences were observed among UCEC patients. By stage, the five-year survival rate of UCEC patients ranges from 95% in those with disease localized to the primary site to 18% in those with distant metastasis\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Additionally, UCEC exhibits histological heterogeneity, and The Cancer Genome Atlas (TCGA) classified it into four molecular subtypes: polymerase ε mutation (POLEmut), high microsatellite instability (MSI-H), TP53 gene abnormality (P53abn), and no specific molecular profile (NSMP)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Patients with these four subtypes exhibit significant prognostic differences. Developing targeted and immunotherapeutic regimens tailored to the distinct molecular characteristics of different stages and subtypes facilitates improved prognosis for UCEC patients and advances precision medicine. However, beyond using clinicopathological staging and TCGA molecular subtypes as bases for risk stratification and treatment response assessment in UCEC patients, in-depth exploration of subtle molecular differences in UCEC further supports personalized diagnosis and treatment for patients and optimization of current therapeutic approaches.\u003c/p\u003e\u003cp\u003eAutophagy is a highly conserved form of programmed cell death. This process involves double-membraned cytoplasmic vesicles known as autophagosomes sequestering unfolded proteins or damaged organelles, which are then transported to lysosomes for degradation, with the resulting macromolecules released back into the cytoplasm for recycling\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Autophagy and its related pathways played a crucial role in the occurrence and progression of multiple malignancies, including clear cell renal cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, wherein they exerted tumor-suppressive, tumor-promoting, or bidirectional effects. Long non-coding RNAs (lncRNAs) are non-protein-coding transcripts exceeding 200 nucleotides in length. As regulatory factors, they participate in gene expression as well as a variety of physiological and pathological processes. Accumulating evidence indicated that lncRNAs exerted complex yet precise regulatory roles in cancer initiation and progression by acting as either oncogenes or tumor suppressor genes. Multiple studies confirmed that programmed death-related lncRNAs facilitated predicting UCEC prognosis and guiding treatment responses, such as those associated with necroptosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, cuproptosis\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e and ferroptosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, for autophagy, a key form of cell death, there remains a lack of research on the regulatory functions of autophagy-related lncRNAs in UCEC progression and their role in prognosis prediction.\u003c/p\u003e\u003cp\u003eThis study utilized autophagy-related long non-coding RNAs (ARLs) to develop a predictive model for forecasting the prognosis of UCEC patients and conducted immunological correlation analyses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData acquisition and procession\u003c/h2\u003e\u003cp\u003eTranscriptomic data and clinical information of UCEC were downloaded from TCGA database, including 35 cases of normal endometrial tissues and 554 cases of UCEC tissues. Perl software was used to split the transcriptomic data into two datasets: messenger RNA (mRNA) and long non-coding RNA (lncRNA). A total of 367 autophagy-related genes (ARGs) were obtained from published literature \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and the \"limma\" R package was employed to extract the expression levels of these ARGs in the TCGA-UCEC dataset.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification and differential expression analysis of autophagy-related lncRNAs\u003c/h3\u003e\n\u003cp\u003eThe \"limma\" R package was used to screen the lncRNA expression matrix between UCEC samples and normal endometrial samples. Differentially expressed autophagy-related lncRNAs (DEARLs) were identified using the criteria of |log2Fold Change| \u0026gt;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The top 50 ARLs with the most significant expression differences were visualized via heatmaps.\u003c/p\u003e\u003cp\u003eThe \"limma\" R package was further employed to analyze the correlation between the ARG expression matrix and lncRNA dataset. Using the screening criteria of R\u0026sup2; \u0026gt;0.5 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, the ARL expression matrix was finally obtained.\u003c/p\u003e\n\u003ch3\u003eIdentification of Prognosis-Associated ARLs\u003c/h3\u003e\n\u003cp\u003eFirst, the prognostic value of DEARLs was evaluated via univariate Cox regression analysis. DEARLs with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the least absolute shrinkage and selection operator (LASSO) regression algorithm, yielding prognostic signature DEARLs.\u003c/p\u003e\u003cp\u003ePatients in the TCGA-UCEC dataset were randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;261) and a validation cohort (n\u0026thinsp;=\u0026thinsp;261) at a 1:1 ratio. They were further stratified into high-risk and low-risk groups based on the median risk score. Log-rank tests were performed using the \"survminer\" R package to compare survival differences between the two groups in the training and validation cohorts, respectively.\u003c/p\u003e\n\u003ch3\u003eImmune-Infiltrating Cell and Tumor Microenvironment Analysis\u003c/h3\u003e\n\u003cp\u003eSeven algorithms, including CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER, and EPIC, were used to assess immune cell scores across different samples, aiming to analyze the correlation between risk scores and immune-infiltrating cells. The ESTIMATE algorithm was employed to calculate three scores for each sample: immune score, stromal score, and ESTIMATE score (sum of stromal score and immune score for a single sample). These scores were used to compare tumor microenvironment differences between the high- and low-risk groups. The \"GSVA\" R package was utilized to compute single-sample gene set enrichment analysis (ssGSEA) scores for each sample, which helped compare immune function differences between the two groups. Additionally, the \"limma\" R package was used to compare the expression differences of immune checkpoint genes between the high- and low-risk groups.\u003c/p\u003e\n\u003ch3\u003eConsensus Clustering Analysis\u003c/h3\u003e\n\u003cp\u003eUCEC patients were stratified into distinct molecular subtypes using the \"ConsensusClusterPlus\" R package, with 1000 iterations and an 80% resampling rate. The gene expression patterns of these distinct UCEC molecular subtypes were evaluated via principal component analysis (PCA). Meanwhile, the \"ggalluvial\" R package was used to visualize the association between different subtype classifications and high/low-risk groups. Kaplan-Meier curves were plotted to analyze survival differences among different subgroups, thereby assessing their prognostic significance. Additionally, differences in immune-infiltrating cells, tumor microenvironment, and immune checkpoint gene expression were compared across UCEC samples of distinct molecular subtypes.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eKey markers in the prognostic model were the focus of analysis. Patients were divided into high- and low-expression groups based on the median expression level of these markers. Differentially expressed genes between the two groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the \"ClusterProfiler\" R package. The \"ClusterProfiler\" R package was also used to analyze HALLMARK pathways significantly enriched between the high- and low-expression groups of key markers. The screening criteria were set as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and |normalized enrichment score (NES)| \u0026gt;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R software (v. 4.4.1). Chi-square tests were used for comparing categorical variables, while Student\u0026rsquo;s t-tests or one-way analysis of variance (ANOVA) were applied for continuous variables. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent prognostic factors associated with overall survival (OS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the predictive specificity, accuracy, and clinical utility of the prognostic model, respectively. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of ARLncRNAs in UCEC\u003c/h2\u003e\u003cp\u003eAmong 554 TCGA-UCEC samples, 16,773 lncRNAs and 19,896 mRNAs were identified, respectively. A gene set containing 367 ARGs (involved in autophagy) was obtained from published literature. Spearman correlation analysis was performed on lncRNAs in the TCGA database with the inclusion criteria of r\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, leading to the screening of 1,901 ARLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, 675 DEARLs were identified between normal and tumor tissues in TCGA-UCEC samples using the criteria of |log₂FC| \u0026gt;1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among these, 416 were upregulated and 259 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The top 50 DEARLs with the most significant expression differences were visualized via a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrognosis-Associated DEARLs in the TCGA-UCEC dataset\u003c/h2\u003e\u003cp\u003eTo validate the prognostic value of DEARLs, survival data of UCEC patients from the TCGA database were used. Patients were grouped based on the median expression level of each DEARL, and univariate Cox proportional hazards regression analysis identified 53 DEARLs associated with UCEC prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong these 53 DEARLs, 48 were prognostic risk lncRNAs, while only XPC-AS1, AL390195.2, AC027237.3, USP30-AS1, and LINC01765 were prognostic protective genes. A Sankey diagram illustrated the relationships between prognosis-associated DEARLs, ARGs, and their prognostic roles in UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTCGA-UCEC samples were randomly divided into a training cohort and a validation cohort at a 1:1 ratio. Based on the 53 DEARLs in the training cohort, a prognostic risk assessment model featuring 19 DEARLs was established using the optimal penalty parameter (λ) of the LASSO regression model. The cross-validation fitting (cvfit) and λ curves are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003eand D\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFurther multivariate Cox regression analysis confirmed that the expression of 12 of these DEARLs were independent risk factors for UCEC patient prognosis. In this model, the risk score for each UCEC patient was calculated using the following formula: Risk score\u0026thinsp;=\u0026thinsp;AC105219.1 \u0026times; 0.67056\u0026thinsp;+\u0026thinsp;AC004233.3 \u0026times; 0.42015\u0026thinsp;+\u0026thinsp;XPC-AS1 \u0026times; (-3.24191)\u0026thinsp;+\u0026thinsp;AC139149.1 \u0026times; 0.71979\u0026thinsp;+\u0026thinsp;AC103563.2 \u0026times; 0.37281\u0026thinsp;+\u0026thinsp;AL359715.2 \u0026times; 0.630363515195829\u0026thinsp;+\u0026thinsp;AC068189.1 \u0026times; 0.83445\u0026thinsp;+\u0026thinsp;AC244517.7 \u0026times; 0.49014\u0026thinsp;+\u0026thinsp;AC027237.3 \u0026times; (-0.55668)\u0026thinsp;+\u0026thinsp;CDKN2B-AS1 \u0026times; 1.57889\u0026thinsp;+\u0026thinsp;AL390195.2 \u0026times; (-0.4353425797735)\u0026thinsp;+\u0026thinsp;AC130352.1 \u0026times; 1.49236 (Note: lncRNA names represent their expression levels in the TCGA database) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eConstruction and Evaluation of the ARL-Associated Prognostic Model\u003c/h2\u003e\u003cp\u003eSamples from the training, validation, and total cohorts were stratified into high- and low-risk groups based on the median risk score. Patient status distribution plots and risk score curves showed the grouping of samples in different cohorts \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B \u003cb\u003eand C\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis revealed that OS of UCEC patients in the high-risk group was significantly shorter than that in the low-risk group across all three cohorts (Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E \u003cb\u003eand F\u003c/b\u003e). A heatmap displayed the expression distribution of the 12 DEARLs in high- and low-risk groups across different cohorts (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H \u003cb\u003eand I\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo facilitate prognosis risk prediction for UCEC patients using this risk score formula, a nomogram was constructed based on the expression levels and risk coefficients of the 12 DEARLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The nomogram converted the expression levels of the 12 DEARLs into corresponding scores, and the total score of all genes was mapped to 1-year, 3-year, and 5-year OS rates. ROC curves showed that the model exhibited good predictive specificity for the survival of UCEC patients in the training and validation cohorts (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB \u003cb\u003eand C\u003c/b\u003e). Calibration curves demonstrated high consistency between the predicted and actual outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD \u003cb\u003eand F\u003c/b\u003e). Decision curves indicated good clinical utility of the model (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE \u003cb\u003eand G\u003c/b\u003e). These results confirmed that the prognostic model based on the expression of 12 DEARLs had good predictive value for the survival outcomes of UCEC patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImmune Analysis Based on the DEARL Prognostic Model\u003c/h2\u003e\u003cp\u003eTo further explore the relationship between the ARL-associated prognostic model and anti-tumor immunity in UCEC patients, seven algorithms (CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER, and EPIC) were applied to calculate immune cell scores of TCGA-UCEC samples. Spearman tests were used to analyze the correlation between immune-infiltrating cells (based on different algorithms) and risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Bubble plots showed that the risk score of the predictive model was negatively correlated with various immune-infiltrating cells. Comparison of immune cell proportions between high- and low-risk groups revealed that the proportion of activated dendritic cells was increased in the high-risk group, while the proportions of CD8\u0026thinsp;+\u0026thinsp;T cells, immature and plasmacytoid dendritic cells, mast cells, natural killer cells, helper T cells, tumor-infiltrating lymphocytes, and regulatory T cells were higher in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eImmune function comparison indicated that multiple immune functions were impaired in the high-risk group, including antigen-presenting cell co-stimulation, CC chemokine receptors, immune checkpoints, cytolytic activity, human leukocyte antigen, T cell co-inhibition and co-stimulation, and type I/II interferon responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). To clarify the relationship between risk scores and the tumor microenvironment, stromal scores, immune scores, and ESTIMATE scores were compared. Results showed that all three scores were significantly lower in the high-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E \u003cb\u003eand F\u003c/b\u003e). Additionally, comparison of immune checkpoint gene expression between high- and low-risk groups revealed that among 47 immune checkpoint genes, most (e.g., CD44, CD40LG, CD200, CD86, CD28, CD276, CD48, CD70, CD27, CD244, LGALS9, TMIGD2, TIGIT, ICOS, ICOSLG, TNFSF15, TNFRSF9, TNFRSF14, LAIR1, IDO1, LAG3, CTLA4, HAVCR2, NRP1, and BTLA) were upregulated in the low-risk group, while only CD40 was upregulated in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). These results demonstrated that the DEARL-based risk score was significantly correlated with the proportion of immune-infiltrating cells and components of the tumor microenvironment in UCEC tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eConsensus Clustering Analysis\u003c/h2\u003e\u003cp\u003eConsensus clustering analysis of UCEC patients was performed using the \"ConsensusClusterPlus\" R package. Consensus clustering matrix plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), tracking plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), cumulative distribution function (CDF) curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and changes in the area under the CDF curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) indicated that k\u0026thinsp;=\u0026thinsp;5 was the optimal classification, dividing UCEC patients into 5 distinct subtypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCompared with patients of other subtypes, those of clusters 1 and 3 exhibited higher survival rates, suggesting an association between UCEC subtypes and clinical outcomes (Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). To clarify the relationship between each molecular cluster and the DEARL-based risk score, Sankey diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), PCA plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), and t-SNE plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH) showed that most samples of Subtypes 1 and 3 belonged to the low-risk group, while samples of the other 3 subtypes were mainly in the high-risk group.\u003c/p\u003e\u003cp\u003eImmune scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), stromal scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), and ESTIMATE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) of samples from clusters 1 and 3 were significantly different from those of other subtypes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A heatmap illustrated the correlation between different clusters and immune cells based on immune cell scores from the 7 algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Comparison of immune checkpoint gene expressions across molecular clusters revealed that 27 immune checkpoint genes showed differential expression among subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). These results suggested that UCEC molecular subtypes were inherently associated with the ARL prognostic model, and the classification was related to patient clinical outcomes and immune function regulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSurvival Analysis and Functional Enrichment Analysis of Key Prognostic Factors\u003c/h2\u003e\u003cp\u003eAmong the prognostic model constructed with 12 independent prognostic factors, cyclin-dependent kinase inhibitor 2B-antisense 1 (CDKN2B-AS1) and Xeroderma pigmentosum complementation group C-antisense 1 (XPC-AS1) were the most critical prognostic risk factor and protective factor, respectively. The prognostic risk factor CDKN2B-AS1 was upregulated in UCEC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Patients with high CDKN2B-AS1 expression had significantly shorter progression-free survival (PFS, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), overall survival (OS, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), disease-free survival (DFS, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), and disease specific survival (DSS, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) than those with low expression (all Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GO enrichment analysis revealed that upregulated CDKN2B-AS1 expression was significantly associated with cilia movement-related cellular processes in biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). KEGG enrichment analysis indicated that CDKN2B-AS1 was involved in mechanisms such as the neuroactive ligand-receptor signaling pathway, cAMP signaling pathway, cornified envelope formation, and motor proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). GSEA results showed that upregulated CDKN2B-AS1 expression enhanced the transforming growth factor-beta (TGF-beta) pathway and negatively regulated MYC targets V2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH \u003cb\u003eand I\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe prognostic protective factor XPC-AS1 was significantly downregulated in UCEC tissues, and its downregulation was associated with shorter OS in patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA \u003cb\u003eand B\u003c/b\u003e). GO enrichment analysis suggested that XPC-AS1 was also involved in cilia movement-related biological processes, which was similar to the biological function of CDKN2B-AS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Additionally, KEGG enrichment analysis indicated that XPC-AS1-related differentially expressed genes were enriched in neuroactive ligand-receptor interaction and motor protein pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). GSEA enrichment analysis suggested that XPC-AS1 was also involved in the regulation of MYC-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). These results showed that the most critical prognostic risk factor and protective factor in the DEARL-based prognostic model were collectively involved in cilia movement mechanisms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePreviously, UCEC was simply classified into Type I and II based on estrogen exposure, and this classification has some predictive value for pathological grading and patient prognosis. Subsequently, the TCGA classification, established based on molecular characteristics, classified UCEC into four distinct molecular subtypes. Beyond enabling more accurate prediction of patients\u0026rsquo; clinical outcomes, it also facilitated guiding treatment decisions for UCEC patients. However, the molecular classification of UCEC still has potential for further subdivision. On the one hand, 3\u0026ndash;6% of patients may belong to two molecular subtypes simultaneously\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, a scenario that impairs the prediction of prognosis and treatment response. For instance, UCEC patients who exhibited the characteristics of both POLEmut and P53abn molecular subtypes had a prognosis similar to that of POLEmut subtype patients, and thus should be managed as POLEmut subtype patients\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Similarly, patients with both dMMR and P53abn features should be classified as the dMMR subtype\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. On the other hand, 40\u0026ndash;50% of UCEC patients who simultaneously exhibited p53 wild-type, mismatch repair proficiency, and absence of POLE mutations are defined as the NSMP subtype\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Further exploration of their potential pathogenic mechanisms and key biomarkers holds significant value for risk stratification and treatment selection in these patients.\u003c/p\u003e\u003cp\u003eThis study conducted an in-depth investigation into the relationship between ARLs and the prognosis of UCEC patients. Screening via differential expression analysis and correlation analysis with ARGs yielded 675 DEARLs. Further, Cox regression and LASSO regression were employed to identify 12 DEARLs as independent risk factors for UCEC prognosis, and a prognostication model with clinical utility was developed. Immune and clustering analyses clarified differences in immune function between distinct molecular subtypes and between high- and low-risk groups. Most samples of clusters 1 and 3 were categorized into the low-risk group, exhibiting relatively favorable prognosis. Additionally, antigen presentation and immune cytotoxicity functions were upregulated in these samples. Finally, functional enrichment analysis focused on key prognostic markers (CDKN2B-AS1 and XPC-AS1) in the prognostication model, revealing that the expression of these two genes was closely associated with the regulation of ciliary movement and cellular motility.\u003c/p\u003e\u003cp\u003eAutophagy is a process that degrades and recycles intracellular components to meet cellular metabolic and self-renewal needs. It plays a crucial role in the quality control of cytoplasmic components and the maintenance of cellular homeostasis, and is closely associated with cell differentiation and development, immune function, as well as tumor initiation and progression. In normal endometrium, autophagy is involved in the cyclic changes of the endometrium during the menstrual cycle. Activation of autophagy in endometrial glandular cells induces cell death, which plays a vital role in menstrual preparation and tissue regeneration\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Autophagy exerts a dual role in the development of UCEC. UCEC cells with mutations in certain ARGs (e.g., ATG4C, RB1CC1/FIP200, ULK4) exhibit reduced autophagy, which contributed to the development of Type I UCEC and poor patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, Sivridis et al. observed a significant increase in the number of \"stone-like structures\" (a marker of autophagic activity) in Type II UCEC tissues compared to normal endometrial tissues, and this increase was associated with poor patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWith accumulating studies confirming the aberrant expression of numerous lncRNAs in various tumors, the association between lncRNAs and autophagy has attracted particular attention. LncRNAs primarily mediate the regulation of autophagy through mechanisms such as microRNAs (miRNAs), RNA-RNA interactions, and RNA-protein regulation\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Furthermore, lncRNAs were involved in multiple stages of autophagy. They first mediated autophagy initiation by regulating the expression of ULK1, mTOR, and Beclin-1, and subsequently promoted autophagy elongation by modulating the expression of ATG3, ATG5, ATG4, ATG12, and ATG7\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These mechanisms underpin the regulation of the malignant phenotype of tumor cells by ARLs. In gastric cancer, the lncRNA SNHG11, which is highly expressed, post-transcriptionally upregulates ATG12 expression via miR-1276, thereby enhancing autophagy and proliferation. It further activates the Wnt/β-catenin signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Yu et al. found that knockdown of lncRNA SNHG3 activates autophagy, which in turn inhibits the migration and invasion of breast cancer cells\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This study identified prognosis-associated ARLs in UCEC for the first time and developed a prognostic prediction model based on these genes. This model contains not only prognostic protective factors such as XPC-AS1 but also prognostic risk factors including CDKN2B-AS1. Functional enrichment analysis in this study revealed that both of these two ARLs are associated with ciliary movement and cellular motility. Furthermore, numerous studies have confirmed that ciliary movement is associated with cell cycle regulation and signaling pathway networks in multiple types of tumors\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Among these, the interaction between autophagy and cilia in tumors is of great significance. On one hand, the autophagic activity of tumor cells can regulate cilia formation, thereby contributing to tumorigenesis. On the other hand, the loss of cilia impairs autophagic activity\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. An in vitro and in vivo study confirmed that CDKN2B-AS1 is significantly overexpressed in UCEC. Knockdown of this gene inhibits the proliferation and invasion of tumor cells, as well as the growth of xenografts in nude mice\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The development of these prognostic markers lays the foundation for the further subdivision of UCEC molecular subtypes; however, further in-depth research is still needed to explore their underlying mechanisms.\u003c/p\u003e\u003cp\u003eAdditionally, this study also analyzed the involvement of ARLs in the regulation of UCEC immune function. In the ARL-based model, immune cell infiltration was reduced in high-risk group samples, and immune functions including chemokine receptors and cytolytic activity were downregulated. Among different molecular subtypes, Subtypes 1 and 3 also exhibited similar changes in immune function. These results confirm that ARLs have the potential to serve as immune markers for UCEC. Multiple studies have demonstrated that ARLs not only facilitate the development of prognostic models for various tumors but also are closely associated with immune cell infiltration, tumor microenvironment changes, and the regulation of tumor immune function\u003csup\u003e[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This is because lncRNAs are involved in key mechanisms of tumor immunity. Ye et al. found via multi-omics analysis that CDKN2B-AS1 was primarily expressed in dendritic cells (DCs) of malignant gliomas and was associated with the DNA repair pathway in malignant cells as well as antigen-presenting genes in DCs\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This suggests its potential as a key regulator of immune responses and tumor suppressor genes.\u003c/p\u003e\u003cp\u003eThis study has certain limitations. First, it is a bioinformatics analysis based on the TCGA database, and in vitro and in vivo experiments are still required to verify the biological functions and immunomodulatory effects of ARLs. Second, external multi-center cohort studies can better validate the value of the ARL-based prognostic model for the prognosis of UCEC patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study developed a UCEC prognostic model composed of 12 ARLs, which is associated with the immune function regulation and molecular subtypes of UCEC. CDKN2B-AS1 and XPC-AS1 have been identified as key prognostic markers that affect tumor progression, and hold potential for application in risk stratification and precision treatment of UCEC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data used in this article were all from online databases and therefore did not involve ethics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work got support from the Scientific Research Program of Traditional Chinese Medicine in Hebei Province (NO.2024254). The funding sponsor reviewed and approved the study design and the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWL and JY designed the study. XF, NL and YG executed data analysis and visualization. DW, XW and JS made the visualization. XF wrote the manuscript. WL and ZJ made a revision on this manuscript. WL and XF were responsible for confirming whether the data were reliable and authentic. The manuscript has been read by all named authors and obtained their approval. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMAKKER V, MACKAY H, RAY-COQUARD I, et al. Endometrial cancer [J]. Nature reviews Disease primers, 2021, 7(1): 88.\u003c/li\u003e\n\u003cli\u003eGALANT N, KRAWCZYK P, MONIST M, et al. 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Scientific reports, 2025, 15(1): 27490.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Uterine corpus endometrial carcinoma, Autophagy, Immunity, Long non-coding RNA, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7822538/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7822538/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify autophagy-related long non-coding RNAs (ARLs) that affect the prognosis of uterine corpus endometrial carcinoma (UCEC) based on The Cancer Genome Atlas (TCGA) database, and to construct a prognostic prediction model for UCEC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTranscriptome data and clinical information of UCEC were obtained from the TCGA database. Differentially expressed ARLs in UCEC were identified using differential expression analysis and correlation analysis. Cox analysis and least absolute shrinkage and selection operator (LASSO) algorithm were applied to determine prognostically significant ARLs, based on which a prognostic model for UCEC was constructed. The prognostic performance of the model was evaluated using Kaplan-Meier survival curve analysis, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Additionally, the relationships between the model and tumor microenvironment, immune infiltrating cells, and immune checkpoint genes were explored. Moreover, consensus clustering analysis was used to define molecular subtypes based on prognostic ARLs. Finally, functional enrichment analysis was performed on key prognostic ARLs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA predictive model containing 12 prognostic signature ARLs was constructed. Patients in the high-risk group had shorter overall survival, and immune function in tumor tissues was downregulated in the high-risk group. Furthermore, UCEC was classified into 5 significantly distinct molecular subtypes, among which subtypes 1 and 3 were significantly associated with autophagy and had better prognosis. Finally, functional enrichment analysis confirmed that knockdown of CDKN2B-AS1 and XPC-AS1 regulates autophagic activity through ciliary movement on the cell surface.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study constructed a UCEC prognostic model composed of 12 autophagy-related lncRNAs, which is associated with immune function regulation and molecular subtypes of UCEC. CDKN2B-AS1 and XPC-AS1, as key prognostic markers, have the potential to be applied in risk stratification and precision therapy for UCEC patients.\u003c/p\u003e","manuscriptTitle":"Analysis of Prognostic and Immunological Relevance of Autophagy-Related lncRNAs in Uterine Corpus Endometrial Carcinoma Based on TCGA Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 12:36:28","doi":"10.21203/rs.3.rs-7822538/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":"eb841901-2db9-4228-9e8d-7055b4027b29","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:10:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 12:36:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7822538","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7822538","identity":"rs-7822538","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}