Unveiling the expression and mechanistic role of SYCP2 in cervical lesions

Abstract

Background

Persistent HPV infection is the primary cause of cervical carcinogenesis. While HPV testing as a screening modality has reduced cervical cancer incidence, standalone HPV screening cannot distinguish persistent infections, necessitating novel molecular markers to identify effective HPV infection in cervical epithelial cells.

Methods

Genes associated with HPV-positive cervical lesions were screened using GEO databases, with SYCP2’s diagnostic potential evaluated by ROC curve analysis. TCGA data were analyzed for SYCP2-clinicopathological correlations and prognostic significance. RNA-seq following SYCP2 silencing identified differentially expressed genes, with functional characterization via GO/KEGG enrichment. Downstream effector IL13RA2 was validated at RNA/protein levels. SYCP2-immunological parameter correlations were investigated using the TISIDB repository.

Results

Integrated analysis of four GEO datasets identified SYCP2 as a key upregulated gene in HPV16-positive cervical cancer compared to HPV-negative tissues. Clinical analyses revealed SYCP2 overexpression correlated with lymph node metastasis and worse disease-free survival (HR (high) = 2.1, P = 0.0096). ROC curves demonstrated diagnostic efficacy for cervical lesions (CIN2+: AUC = 0.846(95%C1 0.777, 0.915); CIN3+: AUC = 0.806(95%C1 0.731, 0.881)). RNA-seq of SYCP2-knockdown cells identified 68 differentially expressed genes, with GO/KEGG analyses linking SYCP2 to viral response pathways (e.g., influenza A, measles) and extracellular matrix remodeling. Alternative splicing analysis revealed enrichment in viral carcinogenesis pathways. SYCP2 silencing significantly downregulated IL13RA2 expression, validated via RT-PCR, Western blot, and IL13 rescue experiments. Correlation analyses revealed that SYCP2 expression levels showed statistically significant negative correlations with the abundance of Activated Dendritic Cells (rho = − 0.347, P < 0.001), Regulatory T cells (rho = − 0.351, P < 0.001), Monocytes (rho = -0.363, P < 0.001), Macrophages (rho = − 0.327, P < 0.001), Myeloid-Derived Suppressor Cells (rho = − 0.313, P < 0.001), and Gamma Delta T cells (Tgd, rho = − 0.433, P < 0.001). Pan-cancer analysis showed SYCP2 upregulation in cervical squamous carcinoma but downregulation in testicular/thyroid cancers.

Conclusions

SYCP2 emerges as a critical biomarker for HPV-driven cervical carcinogenesis, with diagnostic potential for high-grade lesions and prognostic value for metastasis risk. Its regulatory role in IL13RA2-mediated signaling and association with viral response pathways suggest mechanistic involvement in tumor microenvironment remodeling. The dual context-dependent expression (upregulated in cervical cancer vs. downregulated in other malignancies) highlights tissue-specific oncogenic functions. These findings position SYCP2 as a promising target for HPV-associated cervical cancer screening and therapeutic strategies.

Graphical abstract

Background

Cervical cancer is one of the malignant tumors that seriously endanger women’s health. Fortunately, cervical cancer is a causally linked tumor. As early as 1980 s, it was discovered that persistent infection by high-risk Human papillomavirus (HPV) is a necessary precursor for cervical cancer [1]. Accordingly, cervical cancer screening methods using HPV testing as the main method are widely carried out all over the world, which significantly reduces the incidence and mortality of cervical cancer [2]. But it is undeniable that cervical cancer screening has not been widely carried out in developing countries, and the incidence and mortality of cervical cancer are still relatively high [3, 4]. Advancing cancer therapeutics requires elucidating the complex biology of cancer [5]. Consequently, understanding the oncogenic mechanisms of HPV in cervical carcinogenesis and identifying novel biomarkers are essential for the early detection and treatment of cervical cancer. Identifying HPV-associated host genes constitutes an essential first step in this process.

HPV, a DNA virus from the Papillomaviridae family, comprises over 200 genotypes, of which approximately 14 high-risk types are strongly associated with carcinogenesis [6], with HPV 16 and 18 being the predominant oncogenic drivers, collectively accounting for 70% of global cervical cancer cases [7, 8]. Notably, HPV 16 alone is implicated in 55–60% of cervical squamous cell carcinomas (SCC), while HPV 18 contributes to 15–20% of cases and is more frequently linked to adenocarcinoma [9]. Although most HPV infections (~ 90%) are transient and resolve spontaneously within 1–2 years due to immune clearance, approximately 10% of persistent infections progresses to precancerous lesions or invasive cancer [10–12]. Therefore, we aim to identify sentinel genes associated with persistent HPV infection. Published studies have identified multiple genes associated with HPV infection, including clinically established biomarkers such as p16 and Ki-67 [13]. Emerging molecular markers for cervical cancer screening and risk assessment, involving epigenetic mechanisms such as DNA methylation, histone modification, and noncoding RNA regulation, are under active investigation [14].

Multiple studies have confirmed that SYCP2 expression is upregulated in HPV-positive head and neck cancers [15, 16], establishing its association with HPV infection. While preliminary analyses of SYCP2 in HPV-positive cervical carcinomas exist [17, 18], critical knowledge gaps persist in this specific malignancy. Crucially, there is a notable absence of systematic investigations characterizing SYCP2’s expression patterns across large cervical cancer cohorts or defining its clinical relevance (e.g., diagnostic/prognostic utility). Furthermore, despite its established link in other HPV-driven cancers, the functional role of SYCP2 in cervical carcinogenesis remains entirely unexplored. There is a complete lack of mechanistic insights regarding how SYCP2 contributes to oncogenic processes such as proliferation, invasion, metastasis, or treatment resistance within the cervical tumor microenvironment. Advances in sequencing and high-throughput DNA microarray analyses have enabled extensive profiling of gene expression in cervical cancer versus normal tissues. However, a significant limitation persists: most studies focus narrowly on individual datasets, neglecting comprehensive integrated analyses that would enhance statistical power and generalizability. Addressing this gap, our study integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Strikingly, only SYCP2 and CDKN2A (encoding p16) were robustly identified as significantly associated with HPV 16-positive cervical cancer. CDKN2A’s association and oncogenic mechanisms are well-documented [19, 20], highlighting SYCP2 as a novel candidate requiring in-depth investigation.

Therefore, this study employs detailed data mining and rigorous integration of multi-cohort datasets to deeply explore the diagnostic and prognostic value of SYCP2, aiming to develop effective strategies for cervical cancer patient screening and survival prediction. Furthermore, we systematically analyze potential downstream regulatory networks and signaling pathways modulated by SYCP2. This multifaceted approach directly addresses the current deficiencies in SYCP2 research by providing the first comprehensive assessment of its clinical utility in cervical cancer, and novel mechanistic insights into its downstream effectors, thereby elucidating its contribution to cervical carcinogenesis.

Materials and methods

Datasets from the GEO

We systematically searched the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and applied the following inclusion criteria: (1) Datasets containing both normal cervical tissues and cervical carcinoma tissues; (2) Datasets with detailed HPV infection status annotation. Ultimately, four microarray datasets (GSE6791, GSE9750, GSE67522, GSE39001) meeting these criteria were retrieved (Table 1). GEO2 R is a free download system for online data analysis in the GEO; thus, the differentially expressed genes (DEGs) between normal cervical tissue and HPV 16-positive cervical cancer tissue in these datasets could be obtained. We established the following inclusion criteria for the DEGs: upregulated genes must have a log2 fold change (logFC) ≥ 1 and an adjusted P value < 0.05, while downregulated genes must have a logFC ≤ −1 and an adjusted P value < 0.05. One RNA-seq datasets (GSE151666) were downloaded from the GEO, which contains HPV-negative and HPV16-positive cervical cancer tissues, in which DEGs were identified with the edgeR package in R software with a threshold log2 fold change (FC) > 1.0 and P < 0.05.

Table 1.

The case number of GEO datasets in this study

Datasets	Platforms	Case numbers
		HPV negative normal	HPV 16 positive cancer	HPV negative cancer
GSE67522	GPL10558	11	20	–
GSE6791	GPL570	8	8	–
GSE9750	GPL96	24	19	–
GSE39001	GPL6244	5	19	–
GSE151666	GPL21290	–	37	10

Data from TCGA

The RNA-sequencing data and clinicopathological information of cervical cancer were downloaded from TCGA database (https://genome-cancer.ucsc.edu/). The relationship between SYCP2 expression and clinicopathological were analyzed based on TCGA data. Additionally, the association between SYCP2 expression and patient prognosis was analyzed with the TCGA biolinks package in R software. In the present study, we employed the Gene Expression Profiling Interactive Analysis (GEPIA, Copyright© 2017 Zefang Tang, Chenwei Li, Boxi Kang. Zhang’s Lab) platform, a publicly accessible bioinformatics tool, to interrogate transcriptomic datasets from TCGA and evaluate SYCP2 expression patterns across malignancies.

Immune infiltration analysis

The TISIDB repository [21] (http://cis.hku.hk/TISIDB/index.php), a comprehensive tumor immunology database, was leveraged to investigate immunological parameters encompassing 28 tumor-infiltrating lymphocytes (TILs) subtypes, 41 cytokine, and 18 cytokine receptors across multiple cancer types. Quantitative estimation of TIL infiltration density coupled with cytokine-cytokine receptor axis activation status was performed through GSVA (Gene Set Variation Analysis) based on SYCP2 expression profiles. Spearman’s rho correlation coefficients were computed to assess associations between SYCP2 expression and three immunological parameter categories: (a) lymphocyte infiltration indices, (b) cytokine expression level, and (c) cytokine receptor expression level. All statistical analyses adopted a bidirectional hypothesis framework with α = 0.05 defining significance thresholds.

SYCP2 expression in cell lines

We analyzed SYCP2 RNA and protein expression levels across various cell lines using the Human Protein Atlas (HPA) database (Version: 24.0). This database integrates transcriptomic data from 1,206 human cell lines, representing 28 cancer types, one non-cancerous group, and one uncategorized cell line group [22]. For protein subcellular localization analysis, indirect immunofluorescence assays were performed using confocal microscopy. Representative multi-channel fluorescence images are presented, with the target protein (SYCP2) shown in green and microtubule markers displayed in red.

SYCP2 expression in pan cancer

The GEPIA database was employed to visualize the mRNA expression of SYCP2 in carcinoma and non-cancerous samples. The expression of SYCP2 in cells and normal tissue were determined using HPA database. In this database, we use consensus dataset, which consists of normalized expression levels for 55 tissue types, created by combining the HPA and GTEx transcriptomics datasets using the internal normalization pipeline. Color-coding is based on tissue groups, each consisting of tissues with functional features in common.

RNA sequencing (RNA-seq) analysis

RNA-seq was performed to compare transcriptomic profiles between wild-type cells and SYCP2-silenced cells. The transcriptomic sequencing service, including library preparation and sequencing, was conducted by Wuhan SeqHealth Tech Co., Ltd. (Wuhan, China). Briefly, Illumina paired-end libraries with an average insert size of ~ 300 bp were constructed and sequenced on an Illumina platform. Raw sequencing reads underwent rigorous quality assessment, including base quality distribution, nucleotide composition balance, and duplicate sequence analysis. Reads containing adapter contamination or low-quality bases (Q ≤ 10) were filtered using the following criteria: Adapter trimming: Removal of 3’ adapter sequences (AGATCGGAAG) with ≥ 10 bp overlap allowing ≤ 20% mismatch; Ambiguous base filtering: Reads with ≥ 10% undetermined bases (N) discarded; Quality filtering: Reads discarded if > 50% of bases had Phred quality scores ≤ 10; Processed reads then underwent UID deduplication. This pipeline yielded high-quality, deduplicated, and error-corrected sequencing data for downstream analysis. Subsequent bioinformatics analyses were performed to process the transcriptomic data, including alignment to the reference genome, quantification of gene expression, and identification of differentially expressed genes. Differential gene expression analysis was conducted with DESeq2 (|log2 fold change| >1, adjusted P < 0.05). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. For mRNA alternative splicing (AS) analysis, differential splicing events were identified using rMATS (replicate Multivariate Analysis of Transcript Splicing) software with stringent thresholds (|ΔPSI| >0.1, P < 0.05) to define statistically significant events. rMATs software can classify AS events into the following five categories, including skipped exon (SE), mutually exclusive exon (MXE), alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS) and retained intron (RI).

Plasmid construct and transfection targeting SYCP2

Plasmid against SYCP2 were designed and synthesized by Genechem Company (Shanghai, China) to silence SYCP2 expression. Siha cells (3 × 10⁵ cells) were seeded into 6-well plates and incubated overnight. Then, cells were transfected with empty plasmid (mock) and SYCP2-shRNA plasmid using Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.) following the manufacturer’s protocol. Target sequence information for SYCP2-shRNA: shRNA1, cgAGATAATTGCCTTGACTTA; shRNA2, atGACAGCAAGACTGATATTA; shRNA3, caATTGAAGAATCGCTGATAT; Cells of SYCP2-shRNA2 + IL13 refers to SYCP2-silenced cells treated with Human IL-13 Protein (Yeasen Biotechnology, Shanghai, China).

RNA extraction and qRT-PCR

Total RNA was isolated from Siha cells with RNAiso Plus (Cat. No. 9108; Takara Bio Inc., Kusatsu, Shiga, Japan) following the manufacturer’s instructions. First-strand cDNA synthesis was performed using the Hifair II 1 st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology, Shanghai, China) in accordance with the provided protocol. Quantitative real-time (qRT) PCR analysis was conducted on an ABI QuantStudio 5 system with Hieff qPCR SYBR Green Master Mix (Yeasen Biotechnology, Shanghai, China) as directed by the manufacturer. Gene expression levels were quantified using the comparative threshold cycle (2^−ΔΔCT​) method. The primer sequence for IL13RA2, forward AACAATGCTGGGAAGGTGAAGA and reverse GGGTAGGTGTTTGGCTTACG; SYCP2, forward TCAGAATCAGAGTGTGAAC and reverse CATCTGAATAGCTGTTACTG; GAPDH, forward TGACTTCAACAGCGACACCCA and reverse, CACCCTGTTGCTGTAGCCAAA.

Western blot analysis

Siha cells were lysed in RIPA buffer (Saiwen Innovation Biotechnology, China) supplemented with protease inhibitors. Protein concentration was quantified using a BCA assay kit (Solarbio Science & Technology, China). Protein samples (20 µg per lane) were resolved by 10% SDS-PAGE and transferred onto PVDF membranes. After blocking with 5% non-fat milk, membranes were incubated overnight at 4 °C with the following primary antibodies: anti-IL13RA2 (1:1000, Proteintech, China), anti-SYCP2 (1:1000, ImmunoWay, USA), and anti-GAPDH (1:5000, Zhengneng, China). Membranes were subsequently probed with HRP-conjugated secondary antibodies (1:5000, Zhengneng, China) for 1 h at room temperature. Protein signals were visualized using an enhanced chemiluminescence (ECL) substrate (Biomed, China) and analyzed with ImageJ software.

Immunofluorescence staining

Siha cells on coverslips were fixed with 4% paraformaldehyde (biosharp, China), permeabilized with 0.1% Triton X-100 (Beyotime, China), and blocked with 5% BSA (Solarbio, China) for 30 min. Cells were stained with anti-L13RA2 (1:500; Proteintech, China) overnight at 4 ℃, then incubated with Alexa Fluor 488-conjugated secondary antibody (1:500; Zhengneng, China) for 1 h. Nuclei were labeled with DAPI (Beyotime, China), and images were captured by 20× objective lens.

Statistical analysis

The measurement data were presented as means ± standard deviation (SD) and analyzed using Student’s t-test. Receiver operating characteristic (ROC) curve was used to assess the optimal diagnosis of SYCP2. Each experiment was repeated at least three times. A two-sided P value < 0.05 was defined as statistically significant, as only pairwise comparisons were performed in this study. All statistical analysis was performed with SPSS 24 statistical software (IBM Corporation, Armonk, NY, USA) and GraphPad Prism 8.02 (San Diego, California, USA).

Results

Integrated bioinformatics analysis identifies SYCP2 as a HPV16-associated upregulated gene in cervical cancer

Integrated bioinformatics analysis of four gene expression datasets (GSE6791, GSE9750, GSE67522, GSE39001) comparing HPV-negative normal cervical tissues with HPV16-positive cervical cancer tissues identified 144 consistently dysregulated genes (fold change >2), including 97 upregulated and 47 downregulated genes (Fig. 1A, B). Cross-validation using dataset GSE151666 (HPV-negative vs. HPV16-positive cervical cancers) revealed that only SYCP2 and CDKN2A maintained consistent differential expression across all datasets. (Fig. 1C). Both genes demonstrated significantly upregulated in HPV16-positive cervical cancer compared to HPV-negative tissues (both normal and cancerous). Given CDKN2A’s established role in HPV16-positive carcinogenesis [23], subsequent focus was placed on SYCP2. Further analysis of GSE151666 confirmed significantly elevated SYCP2 expression in both HPV16- and HPV18-positive cervical cancers relative to HPV-negative cases (All P < 0.05, Fig. 1D).

Fig. 1.

The screening of HPV infection-dependent differentially expressed genes using the GEO database. A Venn diagram of differentially expressed genes (DEGs) between HPV16-positive cervical cancer tissues and HPV-negative normal cervical tissues across four GSE datasets. B Heatmap of common DEGs identified in these four datasets. C Integrates the results from Panel B with a Venn diagram of DEGs derived from the GSE151666 dataset, comparing HPV16-positive cervical cancer and HPV-negative cervical cancer tissues. D The expression levels of SYCP2 under different HPV infection statuses based on the GSE151666 dataset (^nsP >0.05, ^**P < 0.01, ^***P < 0.001)

Clinical significance of SYCP2 in cervical lesion progression and diagnosis

Analysis of TCGA data revealed significant associations between SYCP2 expression and clinicopathological characteristics (Table 2). Notably, SYCP2 demonstrated differential expression patterns between patients with and without lymph node metastasis (P = 0.004). Survival analysis using GEPIA indicated that elevated SYCP2 expression was significantly associated with worse disease-free survival (DFS, HR (high) = 2.1, P = 0.0096), though no significant correlation was observed with overall survival (OS, HR (high) = 0.81, P = 0.45) (Fig. 2A, B). Further analysis of cervical lesion progression (GSE63514) revealed progressively increasing SYCP2 expression with advancing lesion severity (Fig. 2C). Given the clinical urgency in managing cervical intraepithelial neoplasia (CIN), particularly CIN2 and above lesions (CIN2+) and CIN3 and above lesions (CIN3+), we evaluated the diagnostic potential of SYCP2 using ROC analysis. SYCP2 demonstrated strong diagnostic performance for CIN2+ (AUC = 0.846, 95%CI 0.777–0.915; sensitivity 83.32%, specificity 81.58%) and CIN3+ (AUC = 0.806, 95%CI 0.731–0.881; sensitivity 80.88%, specificity 68.33%) (Fig. 2D, E). Most strikingly, analysis of GSE151666 revealed exceptional diagnostic accuracy for HPV16/18-positive cervical cancer (AUC = 0.975, 95%CI 0.930–1.000.930.000; sensitivity 95.50%, specificity 100%) (Fig. 2F).

Table 2.

The relationship between SYCP2 and clinicopathological characteristics in cervical cancer

	Number of cases	SYCP2 (mean ± sd)	T-value	P-value
Age(years)			0.794	0.428
< 45	132	11.01 ± 2.24
≥ 45	172	10.79 ± 2.51
Figo stage			− 0.183	0.855
Ⅰ–Ⅱ	231	10.86 ± 2.43
Ⅲ–Ⅳ	66	10.92 ± 2.37
Histological type			− 0.261	0.795
Squamous cell carcinoma	252	10.90 ± 2.23
Adenocarcinoma	48	11.02 ± 2.94
Lymph nodes metastasis			− 2.898	0.004*
Yes	60	11.57 ± 1.60
No	133	10.66 ± 2.71
Distant metastasis			1.666	0.098
Yes	10	9.55 ± 2.97
No	116	10.97 ± 2.55

Fig. 2.

Analysis of SYCP2 in cervical lesions and survival outcomes. A Association between SYCP2 expression levels and disease-free survival based on GEPIA analysis; B Association between SYCP2 expression levels and overall survival based on GEPIA analysis; C SYCP2 expression levels across different cervical lesions analyzed using the GSE63514 dataset; D Diagnostic value of SYCP2 for CIN2 + lesions using the GSE63514 dataset; E Diagnostic value of SYCP2 for CIN3 + lesions using the GSE63514 dataset; F Diagnostic value of SYCP2 for HPV16/18-positive cervical cancer using the GSE151666 dataset

Analysis of SYCP2 expression patterns in cervical cancer cells

Analysis of the HPA database revealed SYCP2 mRNA expression levels in multiple tumor cell lines were highest in cervical cancer SiHa cells, followed by colon adenocarcinoma Caco-2 and gallbladder carcinoma SNU-308 cells (Fig. 3A). Immunofluorescence showed SYCP2 protein was predominantly nucleoplasmic, with notably strong expression in SiHa cells (Fig. 3B, C, green fluorescence). Furthermore, SYCP2 mRNA levels varied significantly among cervical cancer cell types: expression was highest in HPV16-positive cells (SiHa, CaSki), intermediate in HPV18-positive (HeLa) and HPV68-positive (ME-180) cells, and lowest in HPV-negative cells (C33a) (Fig. 3D).

Fig. 3.

Analysis of SYCP2 expression patterns across different cell types based on the Human Protein Atlas database. A Expression levels of SYCP2 mRNA in various tumor and non-tumor cell lines (SYCP2 RNA expression data presented as normalized transcripts per million (nTPM) values for each cell line). B Immunofluorescence analysis of SYCP2 protein expression in SiHa cells. C Immunofluorescence analysis of SYCP2 protein expression in Caco-2 cells. D SYCP2 mRNA expression levels in different cervical cancer cell types (SYCP2 RNA expression data presented as nTPM values for each cell line)

Analysis of downstream regulatory pathways of SYCP2

The above studies show the correlation between HPV infection and SYCP2 expression. To investigate SYCP2’s downstream regulatory mechanisms in cervical cancer, SYCP2 expression was knocked down using shRNA. Relative SYCP2 mRNA levels were significantly lower in the knockdown group (SYCP2-shRNA2: 0.186) compared to the control group (SYCP2-NC: 0.998; P < 0.05) (Fig. 4A). Subsequently, RNA-seq was conducted to identify differentially expressed genes between the SYCP2-shRNA2 (sh_sycp2) and SYCP2-NC (sh_NC) groups. The results showed that there were 29 up-regulated genes and 39 down-regulated genes (Fig. 4B, C). GO functional enrichment analysis demonstrated that downregulated genes were predominantly enriched in response to virus, response to external stimulus, response to cytokine, and defense response (Fig. 4D), while upregulated genes were primarily associated with receptor-mediated endocytosis, proteinaceous extracellular matrix, extracellular structure organization, and extracellular matrix organization (Fig. 4F). KEGG pathway analysis revealed that downregulated genes showed significant enrichment in transcriptional misregulation in cancer, measles, and influenza A (Fig. 4E), whereas upregulated genes were enriched in gastric acid secretion, cAMP signaling pathway, and axon guidance (Fig. 4G). Otherwise, we also performed alternative splicing (AS) analysis about downstream regulatory of SYCP2. The results encompassed analyses of AS events detected by junction reads alone and total AS events identified through combined junction and target-specific reads. Using junction reads, we identified 126 A3SS events, 74 A5SS events, 228 MXE events, 15 RI events, and 967 SE events (Fig. 5A). GO enrichment analysis of differentially spliced genes revealed significant enrichment in organelle lumen, nucleus, membrane-enclosed lumen, and intracellular organelle lumen (Fig. 5B), while KEGG pathway analysis demonstrated associations with viral carcinogenesis, TNF signaling pathway, lysosome, and herpes simplex infection (Fig. 5C). When integrating both junction and target reads, the analysis detected 131 A3SS, 73 A5SS, 226 MXE, 16 RI, and 1008 SE events (Fig. 5D). Subsequent GO enrichment analysis of these differentially spliced genes again highlighted terms such as nucleus, organelle lumen, membrane-enclosed lumen, and intracellular organelle lumen (Fig. 5E), and KEGG pathway analysis further implicated pathways including viral carcinogenesis, epstein-barr virus infection, tuberculosis, and herpes simplex infection (Fig. 5F).

Fig. 4.

Analysis of downstream regulatory pathways of SYCP2 based on RNA-seq. A Validation of SYCP2 knockdown efficiency by qRT-PCR (^*P < 0.05, ^***P < 0.001, ^****P < 0.0001); B Volcano plot of differentially expressed genes; C Heatmap of differentially expressed genes; D GO enrichment analysis of downregulated genes; E KEGG pathway analysis of downregulated genes; F, GO enrichment analysis of upregulated genes; G KEGG pathway analysis of upregulated genes

Fig. 5.

mRNA alternative splicing analysis of SYCP2 downstream regulatory genes. A Volcano plot of differential alternative splicing events detected by junction reads; B GO functional enrichment analysis of differentially alternative spliced genes identified via junction reads; C KEGG pathway analysis of differentially alternative spliced genes identified via junction reads; D Volcano plot of differential alternative splicing events detected by combined junction and target reads; E GO functional enrichment analysis of differentially alternative spliced genes identified via combined junction and target reads; F KEGG pathway analysis of differentially alternative spliced genes identified via combined junction and target reads

SYCP2 regulates the expression of IL13RA2 protein in cervical cancer cells

In RNA-seq results, we validated IL13RA2 as a prominently expressed SYCP2-associated downstream gene. qRT-PCR, Western blot, and immunofluorescence analyses confirmed significantly reduced IL13RA2 mRNA and protein expression following SYCP2 knockdown (Fig. 6A–C). Given the IL13/IL13RA2 ligand-receptor relationship, we subsequently supplemented IL13. Both qRT-PCR and Western blot demonstrated upregulated IL13RA2 expression following IL13 supplementation (Fig. 6D–E). These results collectively confirm SYCP2 regulates IL13RA2 through the IL13 signaling axis.

Fig. 6.

SYCP2 regulates the expression of IL13RA2 protein in cervical cancer cells. A qRT-PCR analysis of IL13RA2 mRNA expression levels after SYCP2 knockdown; B WB detection of IL13RA2 and SYCP2 protein expression after SYCP2 knockdown; C Immunofluorescence assessment of IL13RA2 protein expression after SYCP2 knockdown; D qRT-PCR evaluation of IL13RA2 mRNA expression after SYCP2 knockdown with subsequent IL13 supplementation; E WB analysis of IL13RA2 and SYCP2 expression after SYCP2 knockdown with subsequent IL13 supplementation. (^nsP >0.05, ^*P < 0.05, ^**P < 0.01)

Correlation of SYCP2 and immune infiltration level

Building on SYCP2’s association with immune interactions, we analyzed correlations between SYCP2 expression and immune infiltration/chemokine signaling in cervical cancers.SYCP2 levels showed statistically significant negative correlations with the abundance of Activated Dendritic Cells (Act_DC, rho = −0.347, P < 0.001), Regulatory T cells (Treg, rho = −0.351, P < 0.001), Monocytes (rho = −0.363, P < 0.001), Macrophages (rho = −0.327, P < 0.001), Myeloid-Derived Suppressor Cells (MDSC, rho = −0.313, P < 0.001), and Gamma Delta T cells (Tgd, rho = −0.433, P < 0.001), as presented in Fig. 7A. Additionally, SYCP2 expression exhibited significant correlations with chemokine and its receptor expression levels: negative correlations with CCL3 (rho = −0.33, P < 0.001) and positive correlations with CXCL17 (rho = 0.326, P < 0.001) in Fig. 7B, as well as negative correlations with CCR1 (rho = −0.269, P < 0.001) and positive correlations with CCR10 (rho = 0.278, P < 0.001) in Fig. 7C.

Fig. 7.

Correlations between SYCP2 expression and tumor-immune components in cervical cancers. A Relationships between SYCP2 expression and tumor-infiltrating lymphocytes (TILs). B Associations of SYCP2 expression with chemokine levels. C Connections between SYCP2 expression and chemokine receptor expression

Expression of SYCP2 in pan-cancer and literature review

Analysis of SYCP2 expression in normal tissues via HPA database revealed highest levels in testis and breast (Fig. 8A). Using GEPIA with TCGA data, SYCP2 was significantly upregulated in cervical squamous cell carcinoma but downregulated in testicular germ cell tumors (TGCTs) and thyroid carcinoma (THCA) versus matched normal tissues (Fig. 8B). Literature review further established SYCP2’s oncogenic role, showing aberrant expression in head and neck squamous cell carcinoma (HNSCC) and digital papillary adenocarcinoma (DPA), with emerging associations identified between SYCP2 dysregulation and clinical prognosis (Table 3).

Fig. 8.

SYCP2 expression profile across all tumor and normal tissues. A The expression profile of SYCP2 across 50 human tissue types was systematically characterized using the consensus dataset from the Human Protein Atlas (HPA) database. B SYCP2 expression profile across all tumor samples and paired normal tissues. Each dots represent expression of samples

Table 3.

Expression status of SYCP2 in published literature

Article	Tumor	Significance
Zhengrong Zhou, 2025 [24]	PC	Worse prognosis
Ruxing Xi, 2023 [25]	VSCC	High expression
Lukas Leiendecker, 2023 [18]	DPA	High expression
Snežana Hinić, 2022 [15]	SCCHN	Hypomethylated
Hongyan Zheng, 2022 [26]	BC	Worse prognosis
Galo Méndez-Matías, 2021 [27]	SCCHN	Better prognosis
Theresa Guo, 2020 [28]	OPSCC	High ASE rates
Chihua Wu, 2019 [29]	BC	Worse prognosis
Liam Masterson, 2015 [30]	OPSCC	High expression
Ivan Martinez, 2007 [31]	SCCHN	High expression

DPA, digital papillary adenocarcinoma; SCCHN, squamous cell carcinoma of the head and neck; OPSCC, oropharyngeal squamous cell carcinomas; BC, breast carcinoma; PC, Prostate cancer; VSCC, vulval squamous cell carcinomas; ASE, alternative splicing events;

Discussion

Although persistent HPV infection is a well-established cause of cervical carcinogenesis, the majority of HPV infections are transient and do not progress to malignancy [11]. Reliance on HPV testing in clinical practice often leads to overtreatment and increased psychological burden in women [32]. Identifying molecular biomarkers for discriminating progressive HPV infections is therefore critical. This study identifies SYCP2 as a critical mediator in HPV-driven cervical carcinogenesis, demonstrating its specific overexpression in HPV-positive cervical carcinomas, particularly those associated with HPV16 and HPV18 infections. However, as this study did not encompass all high- and low-risk HPV types, we cannot definitively determine whether SYCP2 specifically associates with high-risk HPV infections or all HPV infections, which requires further investigation. This study identified a strong association between SYCP2 and the progression of high-grade cervical intraepithelial neoplasia along with unfavorable clinical outcomes. Mechanistically, the observed negative correlation between SYCP2 expression and TILs abundance provides a plausible immunological explanation for the poor prognosis linked to SYCP2 overexpression, suggesting its role in fostering an immunosuppressive tumor microenvironment. These findings collectively position SYCP2 as a promising ancillary biomarker for refining HPV-based diagnostic stratification and prognostic prediction in cervical cancer management.

SYCP2 has been rarely studied in tumors beyond breast cancer [29], with existing research primarily focused on HPV-positive malignancies such as DPA [18] and HNSCC [16, 27]. Our pan-cancer analysis revealed increased SYCP2 expression in HPV-associated tumors and breast cancer, while decreased expression was observed in TGCTs and THCA. This dual pattern suggests context-dependent oncogenic mechanisms that warrant further investigation. Notably, SYCP2’s high expression in normal testicular tissue aligns with its established role in meiotic processes [33]. It is plausible that germ cell abnormalities during TGCT pathogenesis [34] may contribute to its downregulation in these tumors, though this hypothesis requires further experimental validation.

While SYCP2, a core component of meiotic synaptonemal complexes [35, 36], is physiologically restricted to testicular tissue where it regulates spermatogenesis but epigenetically silenced in normal cervical epithelium [37], this study establishes that HPV infection aberrantly reactivates SYCP2 in cervical cells. This reactivation suggests HPV-mediated reprogramming of host cell cycle machinery toward a somatic meiosis-like state, a phenomenon supported by prior reports of ectopic meiotic gene expression in cervical carcinogenesis [38–40]. Such pathological recapitulation of meiotic programs may drive genomic instability, though the precise mechanistic link between HPV oncoproteins and meiotic dysregulation requires further validation. Notably, SYCP3 (a synaptonemal complex homolog) is documented to trigger oncogenic Akt signaling and mediated immune-resistant phenotypes [41], implying conserved pathological functions among meiotic proteins in neoplasia.

Beyond meiotic reprogramming, transcriptome sequencing revealed that SYCP2 silencing disrupts immune-related pathways, particularly antiviral responses, identifying the IL13/IL13RA2 axis as a core downstream regulatory hub. Critically, SYCP2 expression demonstrates statistically significant inverse correlations with tumor infiltration levels of activated dendritic cells, regulatory T cells, monocytes, and macrophages, mirroring breast cancer findings [26]. This pattern suggests SYCP2’s potential role in impairing immune cell infiltration and interfering with immunotherapy efficacy. While SYCP2’s cancer-related immune functions are increasingly recognized [25, 26, 42], whether it directly modulates immune cell recruitment remains unverified. Collectively, these findings position SYCP2 as a master regulator of tumor-immune evasion and provide a mechanistic rationale for targeting the SYCP2/IL13RA2 axis to restore antitumor immunity in cervical cancer. Given the established role of cancer cell plasticity in driving therapeutic resistance [43] and emerging evidence of immune cell plasticity potentiating treatment efficacy [44], future investigation into the relationship between SYCP2 and cell plasticity represents a promising strategy to overcome resistance to SYCP2-targeted therapies. Specifically, elucidating whether SYCP2 regulates plasticity-associated pathways could identify synergistic co-targets to prevent adaptive escape mechanisms.

Conclusion

SYCP2 is established as a pivotal biomarker in HPV-driven cervical carcinogenesis, demonstrating diagnostic potential for high-grade lesions and prognostic value in predicting metastatic risk. Its modulatory role in IL13RA2-mediated signaling, coupled with enrichment in viral response pathways, suggests mechanistic involvement in tumor microenvironment remodeling through immune regulation and extracellular matrix reorganization. Notably, SYCP2 exhibits context-dependent duality, upregulated in cervical squamous carcinoma yet downregulated in testicular/thyroid malignancies, highlighting tissue-specific oncogenic functions. These findings position SYCP2 as a promising therapeutic target and diagnostic marker for optimizing HPV-associated cervical cancer screening algorithms and precision therapies targeting the SYCP2-IL13RA2 axis. Further validation of its clinical utility in longitudinal cohorts is warranted.

Abbreviations

HPV

Human papillomavirus

SCC

Squamous cell carcinomas

GEO

Gene Expression Omnibus

TCGA

The Cancer Genome Atlas

FC

Fold change

HPA

Human Protein Atlas

nTPM

normalized Transcripts Per Million

RNA-seq

RNA Sequencing

GO

Gene ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

AS

Alternative splicing

rMATS

Replicate Multivariate Analysis of Transcript Splicing

SD

Standard deviation

ROC

Receiver operating characteristic

DFS

Disease free survival

OS

Overall survival

CIN

Cervical intraepithelial lesion

SE

Skipped exon

MXE

Mutually exclusive exon

A5SS

Alternative 5 splice site

A3SS

Alternative 3 splice site

RI

Retained intron

HNSCC

Head and neck squamous cell carcinoma

DPA

Digital papillary adenocarcinoma

SCCHN

Squamous cell carcinoma of the head and neck

OPSCC

Oropharyngeal squamous cell carcinomas

BC

Breast carcinoma

PC

Prostate cancer

VSCC

Vulval squamous cell carcinomas

ASE

Alternative splicing events

TILs

Tumorinfiltrating lymphocytes

Author contributions

CCR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. LL: Conceptualization, Data curation, Formal analysis, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Acknowledgements. YHZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. ZCH: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. CLW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. HNH: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ZAZ: Conceptualization, Data curation, Formal analysis, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Acknowledgements. XY: Conceptualization, Data curation, Formal analysis, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. All authors reviewed the manuscript.

Funding

This work was supported by Henan Natural Science Foundation Project (No. 252300420559), Henan Provincial Central Plains Young Talents Support Project (No. 2023HYTP048) and the “Three 100s” Program for Overseas Training of Medical Science and Technology Talents in Henan Academy of Medical Science (No. HNMOT2024025).

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. In this study, we utilized the following public data resources: five microarray datasets (GSE6791, GSE9750, GSE67522, GSE39001, and GSE151666) from the GEO database; data from The Cancer Genome Atlas (TCGA) database (available at https://genome-cancer.ucsc.edu/); the Gene Expression Profiling Interactive Analysis (GEPIA) platform (accessible via http://gepia.cancer-pku.cn/detail.php?gene=SYCP2); the TISIDB repository (available at http://cis.hku.hk/TISIDB/browse.php?gene=SYCP2); and the Human Protein Atlas (HPA) database (available at https://www.proteinatlas.org/ENSG00000196074-SYCP2).

Declarations

Ethics approval and consent to participate

Not applicable. This study does not include any identifiable patient data or images requiring consent for participating. Therefore, this section is not applicable.

Consent for publication

This study does not include any identifiable patient data or images requiring consent for publication. Therefore, this section is not applicable.

Competing interests

The authors declare no competing interests.