
==== Front
Cancer Cell Int
Cancer Cell Int
Cancer Cell International
1475-2867
BioMed Central London

39261877
3486
10.1186/s12935-024-03486-z
Research
Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma
Chen Jun 14
Wu Tingting 24
Yang Yongwen yongwen1007@csu.edu.cn

34
1 grid.216417.7 0000 0001 0379 7164 Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China
2 grid.216417.7 0000 0001 0379 7164 Department of Cardiovasology, Xiangya Hospital, Central South University, Changsha, China
3 grid.216417.7 0000 0001 0379 7164 Department of Clinical Laboratory, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008 P. R. China
4 grid.216417.7 0000 0001 0379 7164 National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
11 9 2024
11 9 2024
2024
24 31430 11 2023
17 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Sialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.

Methods

We obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.

Results

We developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.

Conclusions

This lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-024-03486-z.

Keywords

Uterine corpus endometrial carcinoma
Sialylation
IncRNA
Prognostic
Tumor microenvironment
Hunan Provincial Natural Science Foundation of China2023JJ40927 2022JJ40839 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Uterine corpus endometrial carcinoma (UCEC) is an escalating global health concern, with increasing incidence and mortality rates, particularly in underdeveloped areas [1, 2]. As the sixth most common cancer in women, UCEC’s rise is partially attributed to lifestyle factors, such as obesity [3]. The complex nature of UCEC, especially the more aggressive type II, is underscored by its poorly understood molecular mechanisms, highlighting the critical need for extensive research into its pathogenesis [4]. This research is essential for improving prognostic accuracy, refining diagnostic methods, and developing targeted, personalized treatments.

Sialylation, a specific type of glycosylation, is vital for cancer cell survival and metastasis, similar to the unique disulfidptosis pathway in programmed cell death [5]. In various cancers, including lung [6], breast [7], and ovarian [8], increased activity of sialyltransferases leads to excessive sialylation, covering up to 60% of tumor cell surfaces [9]. This process enhances immune evasion, stimulates tumor invasion and migration, and disrupts cell-cell interactions [10]. Crucially, hypersialylation aids cancer cells in avoiding apoptosis by blocking critical receptors such as Fas and TNF, thereby impeding cell death signaling [11]. Furthermore, sialylation alters cell adhesion to the extracellular matrix, facilitating cancer spread and metastasis [12]. These insights emphasize sialylation’s role in cancer progression and immune evasion, underscoring its potential as a therapeutic target.

In UCEC, long non-coding RNAs (lncRNAs) regulate vital biological processes and show promise as non-invasive diagnostic biomarkers [13]. Sialylation, which involves adding sialic acids via sialyltransferases [14], may also play a significant role in UCEC progression. The interaction between lncRNAs and sialylation indicates a complex regulatory network affecting tumor behavior and treatment response. Understanding this interaction could offer new insights into UCEC prognosis, the tumor immune microenvironment, and therapeutic strategies, particularly regarding immunotherapy and chemotherapy.

Methods

Data gathering and organization

The RNA transcriptome, clinicopathological characteristics, and gene mutation data for UCEC samples were obtained from the TCGA database (https://gdc.cancer.gov/). We identified a total of 297 sialylation-related genes from GeneCards (https://www.genecards.org/) using a relevance score threshold of over 1 for gene selection.

Screening of LncRNAs in UCEC and development of a prognostic model

In our study, we initially divided 543 patients into training and test sets using R’s caret package. We then performed Spearman correlation analysis with the ggplot2, ggalluvial, and dplyr packages (p < 0.05, |correlation coefficient ≥ 0.6|) to identify and visualize sialylation-associated lncRNAs in a Sankey diagram. Univariate Cox regression in the training set highlighted lncRNAs associated with overall survival (OS), presented in a forest plot. We further analyzed these lncRNAs through LASSO analysis using the glmnet package in R, refining the OS-associated lncRNAs to develop a prognostic signature. Batch normalization, performed via the limma package for TCGA data and using GAPDH as a reference gene in qRT-PCR assays, ensured consistency and minimized technical bias. The LASSO logistic regression model calculated risk scores as: risk score = LnCoef (i) × EXP(i), categorizing patients into low or high-risk groups based on the median risk score.

Model validation and analysis of sialylation-associated IncRNA expression in UCEC

To validate the model, we employed Kaplan–Meier, ROC, and C-index curves, along with forest plots for univariate analysis and a nomogram. These were generated using the survival, caret, pheatmap, timeROC, survminer, regplot, pec, and dplyr packages in R, ensuring the model’s reliability in predicting clinically relevant indicators in UCEC patients. Additionally, the expression patterns of sialylation-associated lncRNAs in UCEC samples were classified by principal component analysis (PCA). The spatial distribution of the samples from both risk groups was visualized using the limma and scatterplot3 packages in R, aiding in the interpretation of the data.

Functional and pathway analysis of differentially expressed genes

We identified differentially expressed genes (DEGs) in the two risk groups using the limma package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these DEGs were conducted with the clusterProfiler, org.Hs.eg.db, and enrichplot packages. Additionally, gene set enrichment analysis (GSEA) was performed using the DOSE package to elucidate distinct functions and pathways in the risk sets. The significance threshold was set using absolute q-values and p-values less than 0.05. This approach allowed for a comprehensive understanding of the molecular underpinnings distinguishing the risk groups.

Immune landscape analysis in different risk sets of UCEC

To analyze the immune landscapes in the two risk sets, we utilized the ESTIMATE package in R based on the ESTIMATE algorithm. Variations between the risk sets were examined using reshape2 and ggpubr packages. The immunological profiles of the 543 samples were assessed through TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, XCELL, EPIC, leveraging limma, e1071, parallel, and preprocessCore packages to quantify immune cell infiltration. The distribution of immune cell was compared using the Wilcoxon test via limma, reshape2, and ggpubr. Furthermore, singlesample Gene Set Enrichment Analysis (ssGSEA) through GSVA, GSEABase, ggpubr, and reshape2 packages was performed to differentiate immune-related functions between the risk sets.

Analysis of DEG mutations, tumor mutation burden, and immune checkpoints in UCEC risk sets

We utilized R and Perl software for extracting and processing somatic mutation data. Using the maftools package in R, a waterfall plot was created to visualize mutations in differentially expressed genes (DEGs) among UCEC patients from two risk groups. Tumor mutation burden (TMB) differences and survival rates between these groups were also analyzed with R. Additionally, we compared the expression levels of 47 immune checkpoints across the two risk sets to assess their potential impact on patient prognosis and treatment response.

Prediction of chemotherapy agent efficacy using IC50 values

We assessed the half-maximal inhibitory concentration (IC50) values of various chemotherapeutic agents using the limma, oncoPredict, and parallel packages in R. This approach enabled the prediction of the efficacy of chemotherapy treatments in UCEC, providing valuable insights into personalized cancer therapy.

Experimental validation of LncRNAs in the model using Ishikawa cells and clinical samples

For the experimental validation of lncRNAs in our model, we utilized the Ishikawa, HEC-1-B cell lines, along with three pairs of tissue samples from patients diagnosed with endometrial cancer, provided by Xiangya Hospital, Central South University. These patients had not undergone any hormone treatments or preoperative tumor-related therapies. All samples were collected with informed consent, authorized by the hospital’s ethics committee, and adhered to the Declaration of Helsinki guidelines. Due to the absence of sequences for AC004884.2 and AC026202.2 in the NCBIGene database, our study was limited to assessing the expression levels of LINC01579, LINC00942, and SLC16A1-AS1 in cell lines.

To evaluate lncRNA expression, we performed quantitative real-time PCR (qRT-PCR). Total RNA was extracted from the samples using TRIzol (Thermo-Fisher, USA) following the manufacturer’s protocol. The RNA was then reverse transcribed into complementary DNA (cDNA) using lncRNA cDNA for qPCR (Tangen, China). For the qRT-PCR, a mixture of ddH2O, primers, cDNA, and qPCR mix (FulenGen, China) was prepared in accordance with the manual. The PCR reaction detection system was used for qRT-PCR with the following parameters: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 10 s, annealing at 60 °C for 20 s, and extension at 72 °C for 15 s, across a total of 40 cycles. Primary cells isolated from normal endometrial tissue were used as controls, and the expression levels were normalized to GAPDH. The specific primer sequences used are listed in Table 1.

Table 1 lncRNA PCR primer

Gene Name	Primer sequence (5’→3’)	Product size (bp)	
LINC01579-F	TCCCAGTGAAGAGAGAGCGA	98	
LINC01579-R	GCTGCTTTTTGCTGATGGCT		
LINC00942-F	CATCTCATCCCCACGATCCG	123	
LINC00942-R	AAATCAGAGACGCCAGCTCC		
SLC16A1-AS1-F	GTGACCGCATAACCAACGTG	106	
SLC16A1-AS1-R	CAGGGGCCTTGCCTCATATT		
GAPDH-F	CTGGGCTACACTGAGCACC	101	
GAPDH-R	AAGTGGTCGTTGAGGGCAATG		

Statistical analysis

Continuous variables between the two groups were compared using t-tests, while categorical variables were analyzed using the chi-square test. For survival analysis, both univariate and multivariate Cox regression analyses were employed. The log-rank test was utilized for analyzing overall survival (OS) data. All data analyses were conducted using R (version 4.3.2) and GraphPad Prism (version 9).

Results

Establishment of a sialylation-associated LncRNA risk model in UCEC patients

In our study, Spearman correlation analysis between sialylation genes and lncRNAs identified 156 upregulated and 109 downregulated lncRNAs (Fig. 1A). We randomly divided patients into training and test sets (Fig. 1B and C). Furthermore, UniCox analysis in the training group identified 18 prognostic lncRNAs (Fig. 1E). We then employed LASSO logistic regression analysis to select five key lncRNAs for the risk model (Additional Table 1). The risk score equation, with coefficients rounded to three decimal places, is: Risk score = (1.462) × Exp AC004884.2 + (-2.420) × Exp AC026202.2 + (1.354) × Exp LINC01579 + (0.327) × Exp LINC00942 + (1.903) × Exp SLC16A1-AS1. The relationship network between the lncRNA and corresponding mRNA is shown in Fig. – Supplement.

Fig. 1 Identification of a sialylation-associated lncRNA prognostic signature. (A) Volcano plot depicting differential expression of sialylation-associated Long Non-Coding RNAs. (B, C) Lasso Regression approach was employed to develop a sialylation-related lncRNA risk assessment model. (D) GO functional enrichment analysis. (E) Forest plot of sialylation-associated prognostic lncRNAs. (F, G) GSEA functional enrichment analysis

Differential gene expression enrichment analysis

The GO analysis of sialylation-associated genes in our study mainly revealed enrichment in cellular structures and movement processes. These processes include cilium movement, microtubule bundle formation, microtubule-based movement, axoneme assembly, and cilium or flagellum-dependent cell motility. This indicates that differentially expressed genes in sialylation significantly contribute to cellular structural dynamics and motility (Fig. 1D). In our pathway analysis, these differentially expressed genes (DEGs) were notably enriched in cancer biology and cellular process pathways. These pathways include protein digestion and absorption, neuroactive ligand-receptor interaction, and motor proteins. This enrichment underscores the importance of these pathways in cancer functionality, highlighting the potential for novel therapeutic targets in cancer treatment and a better understanding of the complex mechanisms of cancer progression and cellular interaction.

In the low-risk group, Gene Set Enrichment Analysis (GSEA) showed significant associations with immune response pathways, including allograft rejection, antigen processing and presentation, and autoimmune thyroid disease (Fig. 1F-G). This indicates an active immune environment in these patients. In contrast, the high-risk group demonstrated enrichment in cell cycle, dilated cardiomyopathy, and ECM receptor interaction pathways, suggesting a tendency toward cell proliferation, structural heart changes, and enhanced extracellular matrix interactions. These findings offer insights into the distinct molecular and cellular environments characterizing the different risk groups.

Assessment and validation of the predictive model

In our analysis of UCEC, both univariate and multivariate regression analyses identified age, grade, and risk score as independent prognostic factors (Fig. 2A-B). Notably, our model demonstrated an Area Under the Curve (AUC) of 0.710, outperforming traditional clinicopathological indicators such as age and grade in prognosticating UCEC outcomes (Fig. 2C). Further, the AUCs for 1-, 3-, and 5-year survival predictions were 0.730, 0.710, and 0.673, respectively, indicating the robust predictive performance of the risk model (Fig. 2D). Patients were stratified into low- and high-risk categories based on median risk scores. In subgroup analyses, patients older than 65 years and those 65 years or younger in the low-risk group exhibited prolonged survival times (Fig. 2H and L). Analysis of survival outcomes across the entire cohort, as well as within the training and validation sets, revealed significant differences in overall survival (OS) between the risk groups. Specifically, patients in the high-risk group demonstrated markedly poorer OS (Fig. 2E–G). The expression profiles of the five lncRNAs, which form the basis of our model, showed distinct patterns between the low- and high-risk groups across the entire dataset, as well as in the training and validation sets (Fig. 2I–K). The distribution of risk scores between the low- and high-risk groups further validated our findings, with higher scores observed in the high-risk group. This pattern was consistent in the entire cohort, as well as in the training and validation sets (Fig. 2M–O). Finally, the survival status data of patients in both risk groups were compared across the whole cohort, training set, and test set, further underscoring the prognostic relevance of our risk stratification model (Fig. 2P–S).

Fig. 2 Verification of the prognostic signature for sialylation-associated lncRNAs in UCEC. (A, B) Validation of the risk score for sialylation-associated lncRNAs through univariate and multivariate regression analysis. (C) ROC curves of the model and other clinicopathological indicators. (D) ROC curves of the model for the 1-, 3- and 5-year survival rates. (E, F, G) Survival curves of patients with UCEC in the entire cohort, training, and testing Groups. (I, J, K) Comprehensive heatmap displaying UCEC patient profiles in the entire cohort, training, and testing groups within the Model. (H, L) Survival curves of patients with UCEC in the >65 and ≤ 65. (M, N, O) Distribution of risk scores for UCEC patient profiles across the entire cohort, training, and testing groups within the model. (P, Q, S) Temporal distribution of survival and mortality numbers for UCEC patient profiles throughout the entire cohort, training, and testing groups within the model

In our study, a comprehensive nomogram incorporating both clinicopathological factors and molecular signatures was constructed to enhance the prognostication of UCEC patients (Fig. 3A). This model adeptly predicted the 1-, 3-, and 5-year survival probabilities, providing a nuanced approach to patient prognosis. The calibration plots, as depicted in Fig. 3B, demonstrated a high degree of agreement between the observed OS rates and the nomogram’s predictions for 1-, 3-, and 5-year survival outcomes. These findings highlight the model’s exceptional ability to discriminate between different risk profiles, effectively categorizing patients into distinct prognostic groups. Our results underscore the utility of this nomogram as a reliable tool in clinical decision-making, offering a significant advancement in the personalized management of UCEC.

Fig. 3 Nomogram for the Validation of the Predictive Model. (A) Nomogram that combines the model and clinicopathological factors for the prediction of the 1-, 3-, and 5-year overall survival rates of patients with UCEC. (B) Calibration curve to assess the concordance between the predicted OS rates and the actual OS rates

Immunocyte infiltration and somatic cell mutations

Figure 4A outlines the distribution of somatic mutations in UCEC patients, distinctly stratified into low- and high-risk groups. A notable inverse relationship is evident between Tumor Mutation Burden (TMB) and risk categories (Fig. 4B). Interestingly, we found that higher TMBs were associated with improved survival outcomes (Fig. 4C), particularly among patients classified in the high-risk group. Furthermore, TMB values were markedly higher in the low-risk group compared to the high-risk group (Fig. 4D). The mutation frequency was also significantly elevated in the low-risk group (Fig. 4E-F), highlighting the intricate relationship between TMB, risk stratification, and survival in UCEC.

Fig. 4 Analysis of patients’ tumor microenvironments and mutations. (A) Comparison of tumor mutation burden between high and low risk groups. (B) Correlation analysis of tumor mutation burden in high and low risk groups. (C, D) Survival analysis across high and low tumor mutation burden groups. (E, F) Landscape of mutation burden in high and low risk groups

In the Tumor Immune Microenvironment (TIME) analysis, shown in Fig. 5C, samples from the high-risk group demonstrated increased immune scores, indicating reduced immunosurveillance and clearance capabilities, potentially leading to more aggressive tumor behavior in these patients. The immunocyte composition, analyzed through various immune scoring algorithms and ssGSEA, is depicted in Fig. 5A-B. The results showed a lower prevalence of CD8 + T cells and a higher proportion of activated dendritic cells (aDCs) in the high-risk group. Survival analysis, based on different immune algorithms (Fig. 5D-F), revealed that a higher composition of CD8 + T cells correlated with extended survival in the low-risk group, suggesting their crucial role in hindering UCEC progression.

Fig. 5 Evaluation of the Tumor Microenvironment in UCEC Populations with High and Low Risk. (A) Predictions of the immune microenvironment in UCEC high and low risk groups using seven different algorithms. (B) Differential immune infiltration in high and low risk groups as predicted by ssGSEA. (C) Analysis of immune score differences between high and low risk UCEC groups. (D-J) Survival analysis of CD8 + T cell proportions in high and low risk groups across seven different algorithms. (K) Immune function analysis in high and low risk UCEC groups. (L) Assessment of immune checkpoints in UCEC high and low risk populations

Additionally, significant correlations were observed between various immune functions, such as T cell co-inhibition, type I and II IFN responses, and cytolytic activity (Fig. 5K), indicating more pronounced immune activity in the high-risk group. This group also showed higher expression of three immune checkpoints (Fig. 5L), suggesting potential responsiveness to immune checkpoint inhibitors in these patients. This analysis emphasizes the complex interplay of immune factors in UCEC, particularly in relation to risk assessment and potential therapeutic interventions.

Assessing the robustness of risk scoring in UCEC: a subgroup stratification approach

We categorized UCEC patients into three distinct subtypes: C1, C2, and C3 (Fig. 6E). Principal Component Analysis (PCA) demonstrated clear differentiation between the high- and low-risk groups, as well as among the three subtypes (Fig. 6A-D). A Sankey diagram revealed that the C2 subgroup primarily consisted of low-risk individuals, whereas the C1 subgroup was predominantly composed of high-risk individuals (Fig. 6F). Survival analysis indicated that the C1 group had a relatively shorter survival duration (Fig. 6G). Additionally, immune scoring revealed that the C1 group had significantly lower immune scores compared to the C2 and C3 groups (Fig. 6H). Immune infiltration analysis also identified a differential distribution of CD8 + T cells across these subgroups (Fig. 6I). Furthermore, an analysis of immune checkpoint correlation revealed statistically significant expression differences in 22 immune checkpoints across the three groups. Notably, CD200 expression was significantly elevated in the C2 group, while TNFSF9 expression was markedly higher in the C3 group (Fig. 6J). These findings underscore the heterogeneity within UCEC and highlight the potential of immune profiling in understanding the disease’s progression and guiding therapeutic strategies.

Fig. 6 Subgroup Analysis Validating the Robustness of the Sialylation-Associated lncRNAs Model in UCEC. (A) Principal component analysis (PCA) for high and low risk UCEC groups. (B) t-Distributed Stochastic Neighbor Embedding (tSNE) plot for UCEC high and low risk populations. (C) PCA for three distinct subgroups of UCEC patients. (D) tSNE plot for the three UCEC subgroups. (E) Clustering diagram of UCEC patients divided into three subgroups. (F) Sankey diagram depicting risk distribution among the three UCEC patient subgroups. (G) Survival analysis chart for the three UCEC subgroups. (H) Immune score analysis for the three UCEC subgroups. (I) Immune infiltration landscape across the three UCEC subgroups. (J) Immune checkpoint landscape in the three UCEC subgroups

Predicting treatment efficacy in UCEC: a comparative analysis of drug sensitivity

In this study, we compared drug sensitivity data from clinical trials and common clinical practices, focusing on UCEC. We observed significant variations in the half-maximal inhibitory concentration (IC50) values of various chemotherapeutic agents between the risk-based groups and the three distinct subgroups (Fig. 7A–F). Notably, the differences in IC50 values for Sepantronium bromide, Vinorelbine, and Podophyllotoxin bromide were statistically significant across both risk strata and the three subgroups. These agents, whose efficacy differed based on sialylation-related gene expression profiles, were identified as theoretically effective treatments in these specific UCEC cohorts (p < 0.05). This analysis provides crucial insights into the potential of personalized medicine in UCEC, highlighting the importance of molecular profiling in optimizing chemotherapy regimens.

Fig. 7 Prediction of chemotherapeutic drugs using the sialylation-associated lncRNAs model in UCEC. (A, D) IC50 values of sepantronium bromide in high and low risk UCEC groups and across three subgroups. (B, E) IC50 values of vinorelbine in high and low risk UCEC groups and three subgroups. (C, F) IC 50 values of podophyllotoxin bromide in high and low risk UCEC groups and across three subgroups

Enhanced expression of lncRNAs in UCEC: evidence from TCGA database clinical samples and Ishikawa cell line analysis

Our investigation, as illustrated in Fig. 8A–B, shows that in clinical samples, as well as in the Ishikawa and HEC-1-B cell lines, these lncRNAs exhibited significantly higher expression levels compared to the normal group (p < 0.05). The significant increase in lncRNA expression observed in TCGA cancerous tissues, clinical samples, and the Ishikawa and HEC-1-B cell lines not only confirms their potential oncogenic role in UCEC but also highlights their potential as biomarkers or therapeutic targets for this disease.

Fig. 8 Relative Expression Levels of lncRNAs in Different Cell Lines and clinical samples. (A) Relative expression levels of LINC01579, LINC00942 and SLC16A1-AS1 in Ishikawa, HEC-1-B cell line, and primary endometrial stromal cells. (B) Relative expression levels of LINC01579, LINC00942 and SLC16A1-AS1 in clinical samples. (*p < 0.05, **p < 0.01, ***p < 0.001)

Discussion

UCEC is one of the most commonly diagnosed gynecological malignancies, with its incidence notably rising in correlation with shifts in lifestyle and dietary patterns [15]. Although there has been considerable advancement in deciphering the biological underpinnings of UCEC [16], the prognosis for patients with advanced stages of this disease remains grim. Despite improvements in the overall 5-year survival rate for early-stage UCEC patients, challenges persist in the treatment of advanced cases, which are often characterized by high recurrence and metastatic rates [17]. Consequently, there is an urgent need to enhance UCEC screening, refine early diagnostic techniques, and develop more effective therapeutic strategies [18]. This necessitates a deeper, continuous investigation into the molecular and cellular mechanisms driving UCEC pathogenesis, aiming to facilitate the development of targeted treatments and improve patient outcomes in both early and advanced stages of the disease.

Sialylation, a pivotal post-translational modification involving the addition of sialic acid residues to glycoproteins and glycolipids, plays a crucial role in various biological processes [19], including cell signaling [20] and immune response modulation [21]. Recent research has highlighted its significant impact on cancer progression and metastasis [22]. Aberrant sialylation patterns in cancer cells have been linked to enhanced immune evasion [23], as altered sialo-glycans can mask tumor antigens and modulate immune cell interactions [24]. This modification can lead to the suppression of natural killer cell cytotoxicity and the promotion of tumor metastasis. Furthermore, sialylation influences the adhesion and migration properties of cancer cells, facilitating their spread to distant organs [25]. Given these insights, sialylation has emerged as a potential therapeutic target in oncology. Current studies are exploring the role of sialylated structures in the tumor microenvironment and their interaction with immune checkpoints [26]. By unraveling the mechanisms underlying sialylation in cancer, researchers aim to develop novel therapeutic strategies that could inhibit tumor growth and metastasis, potentially improving the prognosis and survival outcomes for cancer patients [27]. The intricate role of sialylation in cancer biology underscores the necessity for further investigation, particularly in the context of its interaction with emerging concepts like disulfidptosis and its potential as a biomarker for cancer progression and response to therapy.

In our study, we leveraged the TCGA database to identify lncRNAs co-expressed with sialylation genes, isolating prognostic lncRNAs through univariate Cox analysis and refining them with LASSO regression for model construction. The model’s validity was confirmed by its correlation with clinical indicators, showing that lower risk sets had better overall survival. The model’s effectiveness is comparable to that of models constructed based on metabolic pathways [28]. Enrichment analysis of sialylation-related DEGs highlighted key pathways involved. Recent research emphasizes that upregulation of sialyltransferases and resultant hypersialylation on tumor surfaces are common in various cancers [29], significantly impacting immune evasion and signaling pathways [30], thus offering potential targets for anti-metastatic cancer treatment strategies.

In the Tumor Immune Microenvironment of UCEC, CD8 + T cells play a crucial role [30]. Our study found that patients in the high-risk group had a more significant presence of immunocytes, including CD8 + T cells, and experienced worse tumor progression. This is closely associated with CD8 + T cell levels and may be an independent prognostic factor in UCEC. Patients characterized by lower levels of immune cell infiltration, gene mutations, and expression of immune checkpoints, which implies reduced sensitivity to immunotherapy [31]​​. This comprehensive analysis emphasizes the significant prognostic role of CD8 + T cells in UCEC, particularly in the context of the complex interplay within the TIME [31, 32]. Understanding the dynamics of CD8 + T cell infiltration and their impact on tumor progression and patient outcomes is pivotal in advancing personalized treatment strategies in UCEC.

In cancer therapy, immune checkpoints like PD-1, PD-L1, and CTLA-4 inhibitors have revolutionized treatment, particularly in non-small cell lung cancer, melanoma, and colon cancer [33–35]. These inhibitors work by reactivating the immune system against cancer cells. Additionally, CD200 and TNFSF9 play crucial roles in modulating immune cell functions and responses to immunotherapy [36, 37]. CD200, an immune checkpoint molecule, suppresses immune activity within the tumor microenvironment, promoting tumor growth [38]. Conversely, TNFSF9 in pancreatic cancer exhibits an anti-tumor role, with its expression linked to patient survival rates [39]. These findings suggest potential implications for UCEC treatment, where understanding immune-related genes can predict responses to immunotherapy and chemotherapy, offering key insights into tailored treatment strategies.

In the evolving landscape of cancer research, lncRNAs have emerged as key players, particularly due to their specific expression patterns in various cancers and their detectability in human bodily fluids [40]. These attributes position lncRNAs as promising biomarkers for cancer diagnosis and as novel therapeutic targets [41]. Among these, LINC01579 stands out in glioblastoma research, functioning as a competing endogenous RNA that regulates EIF4G2 expression, thereby modulating cell proliferation [42]. Similarly, LINC00942 plays a significant role in lung adenocarcinoma and gastric cancer, contributing to chemoresistance, and in breast cancer, it influences gene expression and stability through m6A methylation. Another lncRNA, SLC16A1-AS1, exhibits dual functions: it promotes cell proliferation in oral cancers while inhibiting growth and inducing apoptosis in lung cancer cells [2]. These findings not only provide deeper insights into the molecular mechanisms of cancer but also underscore the complex role of lncRNAs in oncogenesis and metastasis [43]. The potential of lncRNAs in improving cancer diagnosis and treatment highlights the importance of ongoing research in this area, promising advancements in personalized cancer therapy and management.

This study focuses on a limited set of sialylation-related lncRNAs, providing an initial overview rather than a comprehensive analysis. Due to resource constraints, we were unable to perform detailed mechanistic studies, such as knockdown or overexpression assays, to clarify the precise functions of these lncRNAs. Moreover, the absence of in vivo validation limitsthe generalizability of our results. Future studies should expand the scope to include additional lncRNAs and incorporate in vivo experiments to validate and deepen our understanding of the regulatory mechanisms and potential clinical implications of these lncRNAs in UCEC.

In conclusion, our findings suggest that lncRNA models associated with sialylation independently predict UCEC patient prognosis, with notable correlations to the Tumor Immune Microenvironment and treatment responses to immunotherapy and chemotherapy. This study aims to shed light on the roles of sialylation-related lncRNAs in UCEC and their influence on clinical therapy. However, it is important to acknowledge that while promising, these findings are preliminary and would benefit from further validation in larger, diverse cohorts to reinforce their clinical applicability.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We would like to express our sincere gratitude to our colleagues at the Reproductive Medicine Center and the Pathology Department of Xiangya Hospital, Central South University, as well as the Bioinformatics Center, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, for their valuable support and contributions to this study.

Author contributions

JC, YWY and TTW conducted statistical analysis and drafted the article. JC and YWY was responsible for the design and guidance of the whole experiment. All authors have read and agreed to the published version of the manuscript.

Funding

The study is funded from Hunan Provincial Natural Science Foundation of China (2022JJ40839, 2023JJ40927).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The Ethics Committee approved this study at Clinical Medical Ethics Committee, Xiangya Hospital, Central South University . All patients provided their voluntary informed consent prior to the procedure being performed.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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