Construction and verification of a prognostic model for cervical cancer based on genes associated with the p53 regulatory pathway

Introduction

Cervical squamous cell carcinoma (CESC) poses a considerable public health issue, significantly contributing to elevated mortality rates while also exerting a considerable economic strain on healthcare systems worldwide (1). As reported by the World Health Organization, cervical cancer ranks among the most prevalent malignancies in women, particularly in low- and middle-income countries where access to screening and therapeutic interventions is often restricted (2). Available treatment modalities for cervical cancer encompass surgical procedures, radiotherapy, and chemotherapy; however, these approaches frequently encounter challenges due to high rates of postoperative recurrence and pronounced adverse effects (3). Furthermore, many current prognostic models inadequately address the intricate molecular mechanisms that drive tumor development, particularly those related to the p53 regulatory pathway (4). This underscores an urgent need for groundbreaking research aimed at clarifying the significance of p53 regulatory pathway-related genes (PRRGs) linked to CESC, which may ultimately enhance patient stratification and treatment efficacy. By focusing on the expression profiles of PRRGs, this investigation aspires to bridge the existing void in prognostic assessment and contribute to the formulation of more effective treatment strategies for individuals diagnosed with CESC.

The p53 signaling pathway plays a crucial role in the onset, advancement, and treatment of cervical cancer, operating through various mechanisms (4). A significant contributor to this process is the inactivation of p53 functionality resulting from human papillomavirus (HPV) infection. Notably, the E6 oncoprotein produced by high-risk HPV facilitates the degradation of p53 via the ubiquitin-proteasome system, which disrupts the regulation of the cell cycle and leads to genomic instability (5). This viral-induced inactivation of p53 is regarded as a fundamental mechanism in the etiology of cervical cancer, as it hinders the repair of damaged DNA and fosters the uncontrolled proliferation of atypical cells (5,6). Additionally, mutations in p53 are strongly associated with cervical cancer prognosis. In patients with HPV-negative cervical cancer, mutations or abnormal expression of the p53 gene (including missense mutations) can lead to the loss of its tumor-suppressive capabilities, thereby enhancing tumor invasiveness and adversely affecting prognosis (7,8). Furthermore, p53 polymorphisms, such as those occurring at codon 72, may affect individual vulnerability to HPV infection. Research also indicates that there are interactions between the p53 pathway and other molecular entities. For example, CENPK interferes with the interaction between p53 and β-catenin, thereby promoting the nuclear translocation of β-catenin and inhibiting the p53 signaling pathway. This interaction may amplify cancer stemness, chemoresistance, and metastasis within cervical cancer (9). Moreover, p53 is involved in regulating MMP1 expression, and its dysregulation may further exacerbate tumor invasiveness (5). Regarding p53-targeted therapeutic approaches, efforts to restore p53 functionality—by inducing cell cycle arrest, apoptosis, and counteracting resistance to chemotherapy and radiation—show promise in curtailing cervical cancer progression. Potential strategies include the use of small molecule agents like PRIMA-1 and Nutlin-3, which function by inhibiting MDM2/HDM2 or reinstating the wild-type structure of mutant p53 (6). In the realm of immunotherapy, p53-targeted vaccines, such as those utilizing viral vectors or dendritic cells (DCs), have exhibited immunogenicity and some clinical efficacy in trials (10). Additionally, combination radiotherapy that incorporates selenium compounds can activate p53-dependent DNA damage responses, thereby improving the effectiveness of radiotherapy while minimizing adverse effects (11). In conclusion, the p53 pathway not only serves as a pivotal driver in the pathogenesis and progression of cervical cancer but also represents a promising therapeutic target. Approaches that seek to restore p53 functionality may pave the way for enhanced patient outcomes.

SMYD2 functions as a pivotal lysine methyltransferase that significantly influences the onset, advancement, and dissemination of various malignancies (12,13). Its operational mechanisms pertain to the modulation of apoptosis, metabolic processes, angiogenesis, and interconnected signaling cascades. Notably, in colon cancer tissues, SMYD2 expression is markedly augmented, facilitating tumor proliferation by obstructing the phosphorylation of RIPK1, which in turn hinders TNF-induced apoptosis and necroptosis (14). Furthermore, SMYD2 amplifies the proliferation and invasiveness of colorectal cancer cells through the ERBB2/FUT4 signaling pathway while simultaneously exhibiting an inhibitory role on apoptotic processes (15). Additionally, SMYD2 methylates HNRNPK at the K422 position, which stabilizes EGFL7 mRNA, thereby promoting angiogenesis in colorectal cancer. The application of its inhibitor, BAY-598, in conjunction with the anti-angiogenic agent apatinib, demonstrates a considerable reduction in tumor growth (16). In the context of breast cancer, SMYD2 methylates BCAR3 at the K334 site, which enhances FMNL protein-mediated cytoskeletal reorganization, thereby significantly improving the migratory and invasive capabilities of breast cancer cells, establishing it as a critical regulator of metastasis. The strategic inhibition of SMYD2 is regarded as an effective approach to impede metastatic spread (13). Moreover, SMYD2 is involved in the methylation of STAT3 and NF-κB p65, leading to the activation of pro-inflammatory signaling pathways that foster the proliferation and survival of triple-negative breast cancer (TNBC) cells, thus creating a positive feedback loop with IL-6 and TNFα signaling (17). In gastric cancer tissues, the overexpression of SMYD2 correlates closely with tumor size, lymphatic invasion, lymph node metastasis, and recurrence, and is recognized as an independent prognostic marker influencing overall survival (OS) (18). Nonetheless, the association between SMYD2 expression and clinical factors, as well as prognosis in cervical cancer, remains to be elucidated, necessitating further research to define its role in cancer progression.

The aim of this study is to investigate the role of SMYD2, a gene associated with the p53 signaling pathway, within the framework of CESC, and to create a prognostic model based on these genes. Given the significant impact of CESC on patient survival rates and the healthcare system, there is an urgent need for improved prognostic tools to aid in clinical decision-making. This research fills a crucial gap in the current literature and establishes a basis for future studies centered on targeted therapies and biomarkers relevant to cervical cancer. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-687/rc).

Methods

Data collection

A collection of 57 genes implicated in the p53 regulatory pathway, collectively designated as PRRGs (Table S1), was meticulously compiled from the ‘PID_P53_REGULATION_PATHWAY’ gene set available in the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb). Moreover, the CESC dataset, which includes RNA sequencing information, raw count data, clinical characteristics, and a total of 306 tumor tissue samples, was obtained from The Cancer Genome Atlas (TCGA) via UCSC XENA (https://xenabrowser.net/). Furthermore, data for 10 normal cervical tissue samples were acquired from the Genotype-Tissue Expression (GTEx) database, also through UCSC XENA (https://xenabrowser.net/).

Identifcation of prognostic signature

The R package ‘sva’ was utilized to mitigate batch effects present in the datasets from TCGA and GTEx. Genes associated with the p53 regulatory pathway were leveraged to develop a prognostic model. A total of 57 genes associated with the p53 signaling pathway were subjected to analysis through the least absolute shrinkage and selection operator (LASSO) Cox regression method, employing ten-fold cross-validation alongside a seed value of 2022. This approach facilitated the identification of 6 potential prognostic variables related to PRRGs, aimed at minimizing redundancy and mitigating the risk of model overfitting. Consequently, these 6 genes were utilized to develop a prognostic risk scoring model intended to forecast the OS of patients diagnosed with CESC. Based on the average risk score, the samples were categorized into high-risk and low-risk groups. Univariate Cox regression analyses were performed to identify genes correlating with OS in patients with CESC by analyzing expression profiles and clinical data from TCGA-CESC subjects. The classic random forest algorithm was adopted for feature selection, allowing for the assessment of prognostic-related genes with a significance threshold set at P<0.05. Survival analysis for the gene SMYD2 was executed using the ’survival’ package, and visualizations were generated with the aid of the ’survminer’ package.

Functional enrichment analysis and gene set enrichment analysis (GSEA)

Patients diagnosed with CESC were divided into two distinct categories based on the expression levels of SMYD2. The identification of target genes was conducted through differential analysis utilizing the ‘DESeq2’ package, adhering to the specified criteria of |log₂[fold change (FC)]| >1 and an adjusted P value threshold of <0.05. Enrichment analysis for Gene Ontology (GO) was performed, encompassing biological processes (BP), cellular components (CC), and molecular functions (MF). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was executed using the ‘clusterProfiler’ package, with a significance level set at P<0.05. Furthermore, GSEA was undertaken utilizing the ‘clusterProfiler’ package, and the resultant data were illustrated using the ‘ggplot2’ package.

Correlation analysis of SMYD2 expression levels and immune cell infiltration

The single-sample GSEA (ssGSEA) algorithm, which is incorporated within the ‘GSVA’ R package, was employed to evaluate the infiltration levels of 24 unique immune cell types present in the tumors. Following this, Spearman’s correlation analysis was performed to investigate the relationships between the expression levels of SMYD2 and the infiltration of immune cells.

Construction of a nomogram to improve the applicability

A predictive nomogram was developed utilizing the R package ‘rms’, integrating clinical characteristics along with the risk score. The score assigned to a patient is derived from the cumulative total of individual scores corresponding to each variable. In order to assess the efficacy of the nomogram, a calibration plot was created to juxtapose the anticipated survival rates at 1, 3, and 5 years against the real outcomes. Furthermore, receiver operating characteristic (ROC) curves were generated employing the ’survivalROC’ R package to evaluate the precision of the risk score.

Clinical data collection

Tissue samples embedded in paraffin, comprising both neighboring normal tissues and cancerous tissues, were obtained from patients diagnosed with CESC via pathological examination. These individuals had received surgical intervention at The Second Affiliated Hospital of Zhejiang University. The study encompassed a total of 33 cases. This study was conducted following the Declaration of Helsinki and its subsequent amendments. The Medical Ethics Committee of The Second Affiliated Hospital of Zhejiang University sanctioned this research (No. 2024-0142), and individual consent for this retrospective analysis was waived. The baseline data of the patients are shown in Table 1.

Immunohistochemical testing

The specimens underwent fixation in formalin, followed by dehydration, embedding, and sectioning into continuous slices of 4-µm thickness. The tissue sections were incubated at 60 ℃ for one hour, dewaxed in xylene, and subsequently rehydrated using a series of gradient alcohols. Following this, the slides were immersed in a sodium citrate solution for antigen retrieval and were subjected to microwave treatment to enhance antigen accessibility. An SMYD2 antibody (dilution 1:500, Proteintech, Wuhan, China) was applied to the sections and incubated overnight at 4 ℃. Afterward, the sections were treated with a secondary antibody (Beijing Zhongshan Golden Bridge Biotechnology Co.) at room temperature for a duration of 30 minutes. Nuclear staining was accomplished using hematoxylin. Finally, microscopic examination and image acquisition were conducted to enable comprehensive analysis of the findings.

Statistical analysis

Bioinformatics evaluations and the utilization of R packages were carried out via R software (version 4.2.0). A one-way analysis of variance (ANOVA) was employed to assess the variations among several groups, succeeded by Tukey’s post hoc test for subsequent pairwise comparisons. In instances requiring pairwise comparisons between two distinct groups, the Wilcoxon rank-sum test was implemented.

Results

Construction and evaluation of the prognostic model based on PRRGs

A comparative examination was conducted to evaluate the expression profiles of 57 PRRGs in CESC tissues relative to normal samples (Figure 1A). By applying the selection criteria of |log₂(FC)| greater than 1 and a P value of less than 0.05, 15 differentially expressed genes (DEGs) were identified, comprising 11 that were upregulated and 4 that were downregulated, as depicted in the volcano plot (Figure 1B). Significantly, 11 PRRGs—including SMYD2, CCNA2, CDK2, CDKN2A, CHEK1, CHEK2, CSE1L, PPP1R13L, PRKCD, SKP2, and TP53AIP1—demonstrated a marked increase in expression within CESC tissues. Conversely, ABL1, RPL5, SETD7, and CCNG1 reflected diminished expression levels.

Figure 1 Expression levels of PRRGs in CESC. (A) Heatmap showing the expression of PRRGs in normal and CESC groups. (B) Volcano plot highlighting genes with significant upregulation (represented in red) and downregulation (depicted in blue). CESC, cervical squamous cell carcinoma; PRRGs, p53 regulatory pathway-related genes.

In order to assess the prognostic implications of genes associated with the regulation pathway of p53 in CESC, we established a prognostic risk model utilizing LASSO regression analysis. This model, formulated by identifying 57 relevant genes, categorized CESC patients into low-risk and high-risk groups predicated on OS outcomes, as elucidated by LASSO Cox regression analysis. As illustrated in Figure 2A,2B, an initial evaluation of PRRGs revealed six genes that are appropriate for the construction of a prognostic risk model. Figure 2C provides a comprehensive analysis of risk scores, survival outcomes, and the expression profiles of the six identified PRRGs across the distinct risk categories. Patients diagnosed with CESC were categorized into high-risk and low-risk groups, determined by the median risk score threshold. Individuals classified within the low-risk group demonstrated markedly superior OS rates when compared to those classified as high-risk. To determine the prognostic relevance of these genes, a univariate Cox regression analysis was conducted. The findings revealed that patients exhibiting low expression levels of SMYD2 had significantly enhanced survival rates in contrast to those with elevated expression levels of this gene (Figure 2D). This observation was further corroborated by Kaplan-Meier analysis, as depicted in Figure 2E.

Figure 2 Identification of PRRGs in CESC associated with the p53 regulatory pathway using LASSO Cox regression. (A,B) LASSO regression analysis was conducted on genes correlated with PRRGs. (C) Proportion of mortality as risk score values escalated within low- and high-risk groups. (D) The forest plot illustrates the prognostic significance of genes involved in the p53 regulatory pathway. (E) High SMYD2 expression was associated with poor OS in CESC. CESC, cervical squamous cell carcinoma; CI, confidence interval; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; OS, overall survival; PRRGs, p53 regulatory pathway-related genes.

SMYD2 expression is significantly elevated in various cancers, including CESC

A univariate Cox regression analysis was performed to examine the relationship between SMYD2 expression levels and OS across 33 different cancer types, illustrated in Figure 3A. The data revealed that increased SMYD2 expression is significantly linked to unfavorable outcomes in patients with diagnoses of adrenocortical carcinoma (ACC) (P<0.001), CESC (P<0.001), lower grade glioma (LGG) (P<0.001), lung adenocarcinoma (LUAD) (P<0.01), and sarcoma (SARC) (P<0.01). The evaluation of SMYD2 expression within various cancer datasets underscores its role in tumor biology. Specifically, SMYD2 expression was analyzed within 33 cancer datasets sourced from the TCGA and GTEx databases, revealing a notable upregulation in the majority of cancer types, particularly in CESC (P<0.001, Figure 3B). These observations are further validated by the heatmap representation depicted in Figure 3C.

Figure 3 SMYD2 expression significantly up-regulated in multiple cancers including CESC. (A) The association between SMYD2 expression levels and OS in patients across multiple cancer types was evaluated using univariate Cox regression analysis. (B) SMYD2 expression levels in different human cancer tissues were compared with normal tissues using data from the TCGA and GTEx databases. (C) A heatmap analysis was used to assess the relationship between SMYD2 expression levels and OS in patients diagnosed with different types of cancer. *, P<0.05; **, P<0.01; ***, P<0.001. CESC, cervical squamous cell carcinoma; CI, confidence interval; GTEx, Genotype-Tissue Expression; HR, hazard ratio; OS, overall survival; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

Functional enrichment analysis of SMYD2-associated DEGs in CESC

We conducted a functional annotation of the DEGs associated with SMYD2 in patients diagnosed with CESC utilizing the ‘clusterProfiler’ R package. The outcomes of the GO enrichment analysis, which encompasses significantly enriched BP, CC, and MF, are illustrated in Table 2. The foremost BP identified were “regulation of hormone levels”, “hormone metabolic process”, “keratinocyte differentiation”, “isoprenoid metabolic process”, and “retinoid metabolic process” (Figure 4A). The MF that exhibited the highest enrichment included “monooxygenase activity”, “hormone activity”, “receptor ligand activity”, “signaling receptor activator activity”, and “glucuronosyltransferase activity” (Figure 4B). Additionally, the analysis underscored significant CC such as the “apical part of the cell”, “Golgi lumen”, “microvillus”, “cornified envelope”, and “actin-based cell projection” (Figure 4C). The pathways identified in the KEGG comprised “retinol metabolism”, “metabolism of xenobiotics by cytochrome P450”, “drug metabolism-cytochrome P450”, “steroid hormone biosynthesis”, and “pentose and glucuronate interconversions” as depicted in Figure 4D. The conclusive clustering diagram effectively aligns the outcomes of GO and KEGG, illustrating the interconnectedness and biological functional relationships that exist between these two analyses (Figure 4E).

Figure 4 Functional enrichment analysis of the DEGs based on the SMYD2 expression levels in CESC. (A-C) GO enrichment analysis of SMYD2-associated DEGs revealed enriched BP, CC, and MF. (D) KEGG pathway enrichment analysis related to SMYD2 in CESC was performed. (E) The clustering diagram displays the results of the GO and KEGG pathway analyses. BP, biological processes; CC, cellular components; CESC, cervical squamous cell carcinoma; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular functions.

GSEA analysis of SMYD2

We undertook an in-depth examination of the expression profile of the SMYD2 gene to deepen our comprehension of its biological significance in CESC. The top ten genes exhibiting both positive and negative correlations with SMYD2 are represented in heatmaps (Figure 5A,5B). To assess the influence of varying expression levels of SMYD2 on the progression of CESC, we conducted GSEA to pinpoint the biological functions and pathways that are most significantly enriched. Our findings indicated a notable enrichment of the high SMYD2 expression group in several BP, particularly those associated with translation and ribosome biogenesis (Figure 5C,5D). Additionally, this group demonstrated substantial enrichment in critical pathways, including those linked to the regulation of the p53 pathway and the activation of MYC.

Figure 5 GSEA enrichment analysis of SMYD2. (A) The top 10 genes positively co-expressed with SMYD2 are shown in heat maps. (B) The top 10 genes negatively co-expressed with SMYD2 are shown in heat maps. (C) The GSEA results are represented using mountain plots. (D) Analysis of the functional roles and pathway enrichment associated with SMYD2 is presented. GSEA, gene set enrichment analysis; FDR, false discovery rates; NES, normalized enrichment scores; TPM, transcripts per million.

Associations of SMYD2 with immune infiltration

The interactions between immune cells and tumor cells within the tumor microenvironment are essential for the progression of CESC. A rigorous evaluation of the infiltration levels of 24 immune cell types within the CESC tissue was conducted utilizing ssGSEA. Furthermore, the correlation between the expression levels of SMYD2 and the infiltration of immune cells was quantitatively analyzed using Spearman’s correlation coefficient. The findings indicated a significant negative correlation between SMYD2 expression and 13 specific immune cell types, which include plasmacytoid DCs (pDCs) (R=−0.276), DCs (R=−0.242), T cells (R=−0.238), immature DCs (iDCs) (R=−0.237), B cells (R=−0.221), cytotoxic cells (R=−0.198), neutrophils (R=−0.185), Th1 cells (R=−0.169), mast cells (R=−0.158), T follicular helper (TFH) (R=−0.155), CD8⁺ T cells (R=−0.148), activated DCs (aDCs) (R=−0.147), and natural killer (NK) CD56 bright cells (R=−0.135) (Figure 6).

Figure 6 The correlation analysis between immune cell infiltration and SMYD2 expression in CESC. (A) Spearman’s correlation analysis of the infiltration levels of 24 immune cell types and SMYD2 expression levels in CESC tissues. (B) A scatter plot depicting immune cell infiltration across different levels of SMYD2 expression. (C) A heatmap illustrating immune cell infiltration at various levels of SMYD2 expression. *, P<0.05; **, P<0.01; ***, P<0.001. aDC, activated dendritic cell; CESC, cervical squamous cell carcinoma; DC, dendritic cell; iDC, immature dendritic cell; NK, natural killer; ns, no statistical difference; pDC, plasmacytoid dendritic cell; Tcm, central memory T cell; TFH, follicular helper T cell; Tgd, gamma delta T cell; Th, T helper; TPM, transcripts per million; TReg, regulatory T cell.

Genetic alterations of SMYD2

The investigation of SMYD2 gene mutations in cancer was conducted utilizing the cBioPortal platform, which encompasses data derived from 32 distinct studies comprising a total of 10,967 samples. Within this dataset, 71 unique mutation sites in the SMYD2 gene were identified, of which 47 were classified as missense mutations. Notably, the mutations H193Y and L194W emerged as the most prevalent among these alterations (Figure 7A). Furthermore, our findings indicated that alterations in the SMYD2 gene were observed in approximately 1.6% of patients diagnosed with CESC (Figure 7B).

Figure 7 Gene mutation analysis of SMYD2. (A) A mutation diagram of SMYD2 across its protein domains, with H193Y and L194W being the most common mutation sites. (B) A mutation map of SMYD2 based on the TCGA PanCancer Atlas studies. CNA, copy number alteration; TCGA, The Cancer Genome Atlas.

Association of SMYD2 expression with clinical parameters in CESC

A significant positive association was identified between increased SMYD2 expression and various clinical parameters, including advanced tumor (T) stage (P<0.05), metastasis (M) stage (P<0.05), clinical stage (P<0.05), as well as the presence of squamous cell carcinoma (P<0.001) (Figure 8A-8D). Conversely, no substantial differences in SMYD2 expression levels were observed in relation to age and lymph node (N) stage (Figures 8E,8F). Subsequently, we integrated the pathological T stage, N stage, and SMYD2 expression levels to construct a nomogram aimed at predicting survival outcomes (Figure 8G). Importantly, the expression of SMYD2 showed promise in improving the precision of survival probability assessments at the 1-, 3-, and 5-year intervals. Moreover, a calibration chart was employed to assess the prediction accuracy of the proposed model (Figure 8H). The time-dependent ROC curves revealed area under the curve (AUC) values surpassing 0.65 for the 1-, 3-, and 5-year timeframes, underscoring the robust performance of the model (Figure 8I).

Figure 8 SMYD2 expression levels correlate with multiple clinicopathological characteristics of CESC patients. (A-F) The correlation between SMYD2 expression and various clinical parameters—such as the T stage, M stage, N stage, clinical stage, age, and histological type—has been investigated. (G) A nomogram was developed to predict 1-, 3-, and 5-year OS in patients with CESC. (H) Calibration plots for predicting 1-, 3-, and 5-year OS were generated. (I) Time-dependent survival ROC curves were constructed to predict 1-, 3-, and 5-year survival rates in CESC patients based on SMYD2 expression levels. *, P<0.05; **, P<0.01. AUC, area under the curve; CESC, cervical squamous cell carcinoma; FPKM, Fragments Per Kilobase of transcript per Million mapped fragments; FPR, false positive rate; M, metastasis; N, lymph node; OS, overall survival; ROC, receiver operating characteristic; T, tumor; TPR, true positive rate.

SMYD2 expression in tissues of CESC patients

Histological examination through HE staining indicated that the tumor cells associated with cervical adenocarcinoma exhibited histopathological features akin to those of moderately differentiated gastric-type adenocarcinoma. In contrast, the tumor cells observed in CESC presented characteristics that are typically associated with non-keratinizing squamous cell carcinoma (Figure 9). Ki-67 serves as a critical biomarker for evaluating the malignancy of tumors in both clinical and research settings. An elevated expression of Ki-67 is generally indicative of increased cellular division, heightened proliferation rates, and enhanced invasive characteristics, all of which correlate with adverse prognostic implications. D2-40 is specifically employed to delineate lymphatic vessel structures within tumor tissues, functioning as an invaluable instrument for the staging of malignant tumors. CK7, a protein prevalent within the intermediate filaments of epithelial cells, exhibits high levels of expression in adenocytic epithelium and tumors derived from it. Immunohistochemical analyses revealed a marked increase in the expression levels of SMYD2, Ki-67, CK7, and D2-40 in cervical adenocarcinoma and squamous cell carcinoma compared to normal cervical tissue (Figure 10A). In order to further corroborate this observation, we conducted a quantitative assessment of the positively stained cells (Figure 10B).

Figure 9 The application of HE staining in the examination of normal cervical tissue, cervical squamous cell carcinoma, and cervical adenocarcinoma. Scale bar, 100 µm. HE, hematoxylin and eosin.

Figure 10 Validation of SMYD2 expression in clinical samples from patients with CESC. (A) The expression levels of the proteins SYMD2, Ki-67, D2-40, and CK7 were evaluated through IHC. (B) The quantification of cells expressing SYMD2, Ki-67, D2-40, and CK7 was performed to determine their respective percentages. *, P<0.05; **, P<0.01; ***, P<0.001. CESC, cervical squamous cell carcinoma; IHC, immunohistochemistry.

Discussion

CESC poses a substantial public health concern globally, with a particularly pronounced impact on women in developing nations, where it ranks among the most prevalent malignancies (19,20). The World Health Organization has identified cervical cancer as a primary contributor to cancer-related fatalities, underscoring the pressing necessity for enhanced diagnostic and therapeutic modalities (21). Existing treatment alternatives, which encompass surgical intervention, radiotherapy, and chemotherapy, frequently suffer from high recurrence rates and considerable adverse effects (22). Consequently, there is a critical demand for the identification of innovative prognostic biomarkers and therapeutic targets aimed at improving patient outcomes and mitigating the disease burden. In the present study, we endeavor to construct and assess a model grounded in prognostic risk genes that are linked to the p53 signaling pathway. Through the application of sophisticated statistical techniques, notably LASSO-Cox regression analysis, we identified genes with significant differential expression and developed a risk stratification model capable of accurately categorizing patients into low-risk and high-risk cohorts based on OS projections. The findings underscore the promise of PRRGs, specifically SMYD2, as pivotal prognostic biomarkers for CESC, thus laying the groundwork for enhanced clinical decision-making and therapeutic approaches. Further discourse will delve into the ramifications of our results, examine the biological relevance of the identified genes, their correlations with immune responses, and the prospective clinical applications of the prognostic model.

Recent investigations have highlighted the p53 signaling pathway’s critical role in cellular response mechanisms, particularly its influence over various genes integral to the regulation of the cell cycle, apoptosis, and DNA repair processes (23-25). In the present investigation, we utilized LASSO regression analysis, a technique widely recognized for its outstanding ability to select features within high-dimensional datasets, to establish a six-gene model linked to the p53 signaling pathway. The regularization characteristics inherent to LASSO facilitate a reduction in model complexity while preserving the most predictive genetic factors. Building upon this foundation, we subsequently utilized the random forest algorithm to pinpoint genes of paramount prognostic relevance within this model. The random forest technique, recognized as a robust ensemble learning approach, mitigates the likelihood of overfitting and enhances prediction precision by generating multiple decision trees and aggregating their outcomes. Through this analytical framework, SYMD2 emerged as a significant candidate gene, warranting further exploration of its potential involvement in the p53 signaling pathway. Such investigations will not only enrich our understanding of the p53 signaling pathway but may also unveil novel therapeutic targets for related conditions, including cancer.

In investigations utilizing the Cox proportional hazards model, it has been demonstrated that the identification of independent prognostic factors can markedly improve the predictive accuracy of survival outcomes for cancer patients. These prognostic determinants elucidate the correlation between distinct gene expression profiles and survival duration, thereby facilitating the effective differentiation between high-risk and low-risk cohorts, particularly concerning genes implicated in the p53 signaling pathway. Evidence suggests that the p53 pathway is integral to the initiation and advancement of various malignancies, with alterations in the expression of its associated gene, SMYD2, being closely linked to patient prognosis. For instance, a study developed a novel prognostic model by examining autophagy-related genes in patients with gastric cancer, identifying the expression levels of several critical genes as prognostic biomarkers via Cox proportional hazards regression analysis. These genes, associated with the p53 signaling pathway, demonstrated significant survival disparities between high-risk and low-risk populations (26). Pertinent research indicates that the risk scores derived from these genes exhibit substantial correlations with clinical characteristics and overall patient survival (27). Additionally, further investigations revealed that the expression of the HJURP protein is closely linked to the prognosis of LUAD, where it has been identified as an independent prognostic factor in multivariable Cox regression analysis, hinting that its interaction with the p53 signaling pathway may significantly affect tumor progression and patient survival (28). These discoveries not only highlight the clinical potential of risk scores derived from the Cox model but also offer fresh perspectives for the development of personalized therapeutic strategies. In conclusion, the risk factor association diagram based on the Cox proportional hazards model can effectively furnish more comprehensive prognostic insights for clinical practice, thereby aiding physicians in making more informed decisions in the management of tumors.

The pathways uncovered through functional enrichment analysis offer significant insights into the BP associated with CESC. Among these, the “retinol metabolism” pathway stands out as a pivotal factor in the pathogenesis of CESC (29). Retinol, a metabolite of vitamin A, is essential for cellular differentiation and proliferation (30,31). The dysregulation of retinol metabolism has been associated with various cancers, including cervical cancer, potentially influencing the expression of genes that govern cell cycle regulation and apoptosis. The connection between this pathway and SMYD2 implies that modifications in retinol metabolism could serve as a mechanism through which SMYD2 manifests its oncogenic properties, thereby revealing a novel therapeutic target for intervention. Another critical pathway identified is the “cytochrome P450-mediated metabolism of exogenous substances”. This pathway is fundamental to the detoxification processes of numerous environmental carcinogens and pharmaceuticals, with any dysregulation posing an increased risk for cancer development (32,33). The heightened expression of genes related to this pathway in CESC tissues may reflect an adaptive mechanism to the tumor microenvironment, where augmented metabolic activity could facilitate tumor growth and survival. Investigating the interplay between SMYD2 and cytochrome P450 enzymes may provide insights into the metabolic vulnerabilities inherent to CESC, potentially leading to targeted therapies that leverage these metabolic pathways. Furthermore, our analysis revealed significant enrichment in the “steroid hormone biosynthesis” pathway. Steroid hormones, such as estrogen, have been implicated in the modulation of cervical cancer development and progression through their effects on cell proliferation and apoptosis (34). The relationship between SMYD2 and the steroid hormone signaling pathway could clarify the role of hormonal factors in CESC pathogenesis. This association highlights the potential benefits of integrating hormonal therapy with current treatment approaches to enhance outcomes for CESC patients. In conclusion, the pathways identified in this investigation not only elucidate the complex involvement of SMYD2 in CESC but also propose promising strategies for therapeutic intervention. Further investigation into these pathways could deepen our understanding of CESC biology and facilitate the development of more effective treatment modalities.

Immune responses are pivotal within the tumor microenvironment, significantly affecting the development and prognosis of CESC (35). Our analysis indicates a noteworthy negative correlation between the expression of SMYD2 and the infiltration of various immune cell populations, including pDCs, T cells, and NK cells. The role of pDCs is particularly vital for the initiation of immune responses, as these cells are recognized for their ability to produce type I interferons and activate T cells, thereby bolstering anti-tumor immunity (36). The identified negative correlation implies that increased levels of SMYD2 may hinder the recruitment or activation of pDCs, which could facilitate tumor-mediated immune evasion. T cells, especially CD8⁺ cytotoxic T lymphocytes, are crucial for the identification and destruction of cancerous cells (37). Their infiltration is generally correlated with a more favorable prognosis; however, our findings suggest that higher levels of SMYD2 expression are associated with diminished T cell infiltration. This observation aligns with prior research indicating that specific oncogenes can influence T cell responses, leading to immune suppression within the tumor microenvironment (38). The significance of this relationship is profound, as it posits that targeting SMYD2 might enhance T cell-mediated anti-tumor immunity, thus improving patient outcomes. NK cells, another essential element of the immune response, can swiftly react to tumor cells (39). The negative correlation between SMYD2 expression and NK cell infiltration prompts investigation into the mechanisms by which SMYD2 may suppress NK cell function. Research has demonstrated that tumor cells can exploit various strategies to evade recognition and elimination by NK cells (40). Elucidating the interactions between SMYD2 and NK cell dynamics may yield insights into innovative therapeutic approaches aimed at reactivating immune responses in CESC patients. In summary, the significant negative correlation between SMYD2 expression and the infiltration of critical immune cell types highlights the potential role of SMYD2 in tumor immune evasion. Further exploration into the mechanisms through which SMYD2 modulates immune responses may provide valuable information for the development of targeted therapies designed to enhance anti-tumor immunity in CESC.

The survival analysis conducted in our study highlights the significant prognostic value of SMYD2 expression in CESC. Our findings indicate that high SMYD2 expression is associated with poorer OS across various cancer types, including CESC, as confirmed by univariate Cox regression analysis (P<0.05). This observation is consistent with previous studies that have identified SMYD2 as a key factor linked to tumor progression and poor prognosis in several malignancies, including LUAD (41). The negative correlation between SMYD2 expression and survival suggests that SMYD2 may serve as a valuable biomarker for stratifying patients based on risk profiles, facilitating more personalized treatment approaches. The clinical relevance of our prognostic model, which incorporates SMYD2 expression levels, is further demonstrated by its ability to classify patients into low-risk and high-risk groups, with the former showing significantly better survival outcomes. This stratification is crucial for clinical decision-making, as it enables the identification of patients who may benefit from more aggressive therapeutic strategies. Furthermore, the integration of SMYD2 expression into survival prediction nomograms enhanced the accuracy of prognosis assessment, with AUC values exceeding 0.65 for 1-, 3-, and 5-year survival predictions. In conclusion, our survival analysis not only reinforces the prognostic value of SMYD2 in CESC but also underscores the necessity for further exploration of its underlying mechanisms. Future research should aim to elucidate the pathways through which SMYD2 influences tumor biology and patient outcomes and explore its potential as a therapeutic target. The implications of these findings extend beyond CESC, suggesting that SMYD2 may play a broader role in cancer biology, warranting further investigation across various tumor types.

The observed histological changes in CESC are crucial for understanding tumor biology and its clinical implications. Histomorphology, which involves the study of tissue microstructure, can reveal key alterations associated with malignant tumors. In our study, immunohistochemical analysis demonstrated a significant increase in SMYD2 expression in CESC tissues compared to adjacent non-tumor tissues. This upregulation was associated with enhanced cell proliferation, as evidenced by increased expression of the proliferation marker Ki-67. These findings suggest that SMYD2 may play a critical role in promoting tumor growth and aggressiveness. Furthermore, histopathological examination revealed cellular and structural changes in the tumor microenvironment. The presence of atypical cells, characterized by nuclear pleomorphism and an increased nuclear-to-cytoplasmic ratio, indicated the presence of malignant tumors and reflected the typical structural disorganization of cancerous tissue. Additionally, observed lymphatic vessel proliferation, as evidenced by D2-40 staining, indicated enhanced metastatic potential, a hallmark of advanced malignancies. These histological changes not only provide insights into the invasive characteristics of CESC but also highlight the potential of SMYD2 as a biomarker for tumor progression. The clinical relevance of these findings lies in their impact on clinical practice. Understanding the histological changes associated with SMYD2 expression can aid in the development of targeted therapies and improve prognostic assessments. Moreover, the correlation between SMYD2 levels and histological features such as cell proliferation and lymphatic invasion underscores the need for further research into the mechanisms through which SMYD2 influences tumor behavior. Ultimately, this knowledge may contribute to more effective treatment strategies and improve the prognosis of CESC patients.

The constraints of this research necessitate thorough reflection. Initially, the modest sample size may hinder the extrapolation of our results, as larger participant groups could enhance statistical robustness and improve the dependability of the prognostic model. Furthermore, the absence of multicenter clinical validation raises issues regarding the reproducibility of our findings across varied populations and clinical environments. It is essential to address these limitations in forthcoming investigations to substantiate the clinical relevance of the identified prognostic biomarkers and to confirm their efficacy in personalized treatment approaches for CESC. In summary, this study underscores the crucial involvement of SMYD2 expression in CESC and its correlation with patient prognosis. Our results highlight the significance of SMYD2 within the tumor immune microenvironment, establishing a foundation for future inquiries focused on the development of targeted therapies and innovative biomarkers. Ultimately, the knowledge acquired from this research could enhance clinical outcomes for individuals diagnosed with CESC.

Conclusions

The results demonstrate that heightened expression levels of SMYD2 in CESC correlate with increased disease severity, unfavorable prognostic outcomes, and irregularities in the infiltration of immune cells. It is anticipated that the identification of SMYD2 may enhance personalized therapeutic approaches for individuals diagnosed with CESC and support more informed decision-making in clinical practices.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-687/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-687/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-687/prf

Funding: This work was supported by grants from the National Natural Science Foundation of China Youth Fund Project (No. 82302903).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-687/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted following the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of The Second Affiliated Hospital of Zhejiang University (No. 2024-0142), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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