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Discov Oncol
Discov Oncol
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10.1007/s12672-024-01283-8
Research
RNA sequencing and multiplexed immunohistochemistry reveal the factors for postoperative recurrence of stage IB-IIA cervical squamous cell carcinoma
Wu Meiyao 12345
Li Baixue 1234
Shi Lina 6
Yang Lingling 6
Liang Chuqiao 6
Wang Tianhong 305587870@qq.com

1234
Sheng Xiujie 2008691150@gzhmu.edu.cn

1234
1 https://ror.org/00zat6v61 grid.410737.6 0000 0000 8653 1072 Department of Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150 China
2 https://ror.org/00zat6v61 grid.410737.6 0000 0000 8653 1072 Department of Gynecologic Oncology Research Office, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150 China
3 https://ror.org/00zat6v61 grid.410737.6 0000 0000 8653 1072 Guangzhou Key Laboratory of Targeted Therapy for Gynecologic Oncology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150 China
4 https://ror.org/00zat6v61 grid.410737.6 0000 0000 8653 1072 Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150 China
5 grid.415550.0 0000 0004 1764 4144 Department of Obstetrics and Gynaecology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
6 grid.518662.e Medical Department, Nanjing Geneseeq Technology Inc., Nanjing, 211899 China
10 9 2024
10 9 2024
12 2024
15 42217 7 2024
27 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

Stage IB-IIA Cervical Squamous Cell Carcinoma (CSCC) presents diverse clinical outcomes, the mechanisms that cause recurrence in CC patients remain unclear. The goal of this study was to identify predictive biomarkers leading to tumor recurrence in IB-IIA CSCC after surgical treatment by comparing the transcriptional and immune landscape between the recurrence and non-recurrence group.

Methods

We performed mRNA sequencing and multiplexed immunohistochemistry (mIHC) analysis among stage IB-IIA patients with or without recurrence after surgical resection and were followed-up for a median of three years.

Results

Integrated analysis indicates that the upregulated gene expression in zinc finger proteins, the activation of the PI3K/Akt pathway, and the low infiltration level of T follicular helper cells and B-cells may serve as potential recurrent biomarkers for CSCC. We also observed significant differences in the immune and genomic landscape between two groups.

Conclusions

These findings provide new insights into the relapse mechanisms of CSCC, which could potentially guide clinical exploration of drug targets.

Keywords

Cervical cancer
RNA sequencing
Multiplexed immunohistochemistry
Immune cell
Prognosis biomarkers
Universities (high-level universities) and Enterprises in Guangzhou , China (CN)no. SL2022A03J01028 issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Cervical cancer (CC) is the fourth most common malignancy and the fourth leading cause of cancer-related death in women globally. In 2020, it was estimated to cause 604,000 new cases and 342,000 deaths worldwide [1]. China accounted for 18.6% of these new cases and 15.4% of deaths [2]. Persistent high-risk HPV infection has been recognized as a carcinogen of cervical cancer. In recent years, with the widespread increase in HPV vaccination and precancerous lesion screening, the incidence of advanced disease has gradually reduced [3], However, the 5-year overall survival rate for all stages of cervical cancer is still only 68% [4]. Recurrent or metastatic cervical cancer is currently incurable and patients have limited therapeutic options, highlighting the need for sophisticated prognostic markers [5]. Furthermore, the exact mechanisms that cause recurrence in CC patients remain unclear. From a macro perspective, Corrado et al. found that in FIGO stage IB1-IB2 cervical cancer, minimally invasive surgery did not show a different recurrence pattern compared to abdominal radical hysterectomy, nor did it have a higher risk of distant metastasis, and there were no significant differences in DFS and OS [6]. Investigating the molecular characteristics of early-stage cervical cancer from a microscopic perspective, including analyses at the molecular level, may yield potential biomarkers for recurrence risk and patterns of recurrence.

The tumor microenvironment (TME), particularly the tumor immune microenvironment (TIME), is crucial in fighting against tumors. TIME is a complex milieu maintaining a delicate balance between inflammation and tolerance [7]. If this balance is disrupted, tumor cells can’t be effectively eliminated, leading to tumorigenesis, tumor progression and metastasis. Several studies have highlighted the significance of the genomic factors and patterns of immune cell infiltration in cervical cancer cells. Thought bioinformatics analysis, these studies revealed potential and novel immune microenvironment-related diagnostic and prognostic markers. A comprehensive analysis has discovered eighteen novel genes associated with the immune microenvironment, which show a strong correlation with patient prognosis. This result was validated by an independent dataset. [8]. Higher levels of activated memory CD4 + T cells are independently related to favorable overall survival, while a higher fraction of activated mast cells is independently associated with adverse outcomes [9]. A TMEscore model has been established to potentially predict the prognosis of cervical cancer (CC) patients by comparing global gene expression and survival analysis of TCGA-CESC CC patients [10]. Nevertheless, it should be noted that these findings rely solely on bioinformatics analysis and thus possess certain limitations. Therefore, it is still necessary to prove these models’ relevance to the tumor immune microenvironment, despite the predictive efficiency.

Predictive biomarkers are necessary to stratify patients based on their potential of recurrence, enabling a more accurate determination of individual risk/benefit. This study represents the first analysis of clinical data and outcomes for 10 patients with stage IB-IIA CSCC, focusing on potential factors that may influence postoperative recurrence. This study aims to provide a new approach to assessing the risk of recurrence. This study used RNA-seq data from samples to identify transcriptional and immune landscape. It also carried out spatial characterization of the CSCC immune microenvironment, specifically focusing on the expression of T follicular helper cells, resting mast cells, and B-cells using mIHC. Our findings provide theoretical support and new insights for the research and development of novel drugs to treat relapse CC.

Materials and methods

Study design and participants

We retrospectively enrolled 10 patients with stage IB-IIA CSCC, including 4 patients in non-recurrence group and 6 patients in recurrence group. A surgical resection was performed on these patients, and some also received adjuvant therapy. All samples were obtained from The Third Affiliated Hospital of Guangzhou Medical University, and underwent pathological diagnosis. Postoperative tumor tissue sections were obtained for RNA-seq and mIHC analysis. Informed consents was signed by each patient. This study was approved by Medical Ethics Committee of the Third Affiliated Hospital of Guangzhou Medical University granted approval for this study (No.2021-054, Ethics Number: 2021-054). And all methods were followed in compliance with ethical regulations.

RNA isolation and sequencing

RNeasy FFPE Kit (Qiagen) was used to extract total RNA from FFPE samples. Qubit 3.0 was used to quantify all RNA samples using the RNA BR Assay Kit (Life Technologies). Using the Bioanalyzer 2100 (Agilent Technologies, USA), RNA integrity was evaluated. RIN value (RNA integrity number) greater than 7 was necessary. RNase H was used to depleted ribosomal RNA, and the KAPA Stranded RNA-seq Kit with RiboErase (KAPA Biosystems, USA) was used to prepare the library. The KAPA Library Quantification Kit (KAPA Biosystems, USA) was utilized to determine the library concentration, and the quality of the library (2 × 150 bp paired-end reads) was assessed by the Agilent High Sensitivity RNA kit on Bioanalyzer 2100. The library was then sequenced on Illumina HiSeq4000 NGS platforms (Illumina, USA) to generate 0.1 M 2 × 150 bp pair-end reads in an average of 30 M reads.

RNA expression and functional analysis

The bcl2fastq v2.16.0.10 (Illumina, USA) was used for base calling in order to generate sequence reads in the FASTQ format. Trimmomatic (version 0.33) [11] was used for quality control, where N bases and low-quality (score < 15) bases were eliminated. Using STAR (version 2.5.3a) [12], cleaned reads were aligned to the human reference genome (hg19/GRCh37). Transcripts were measured using RSEM (version 1.3.0) [13], which assigns reads that map to multiple transcripts optimally using expectation maximization algorithm. The R package pheatmap was used to visualized the differentially expressed genes (DEGs) across tumor recurrence and non-recurrence samples. The DEGs were detected using edgeR with fold change R2 and P value < 0.05 [14]. Using metascape online tools (https://metascape.org/gp/index.html#/main/step1) [15] based on the KEGG databases, the pathway of enriched DEGs was conducted. GOATOOLS [16] was used for gene ontology enrichment analysis (GOEA).

Immune cell infiltration estimate

Using gene expression data from RNA-seq, the relative infiltration level of immune cells was estimated for each sample with CIBERSORT [17] and TIMER [18]. Briefly, each sample has its own gene expression profile. The known gene expression signature matrix of immune cells and their relative fractions are combined linearly to create this profile.

Immunomodulator gene expression analysis

Earlier study by Thorssen et al. measured the level of immunomodulation [19]. Seventy-four immunomodulatory genes were assessed by Gene expression levels in transcript per million (TPM). Seven super categories including receptor, ligand, co-stimulator, co-inhibitor, cell adhesion, antigen presentation, and other were used to group these genes. They can also function as immune checkpoint inhibitors, stimulators, or neither. The median TPM of each gene was determined for recurrence and non-recurrence groups, and the z-score normalized between the two groups for each gene were displayed with a heatmap with the results.

Cytolytic activity

As previously described, the geometric mean of gene expression levels of GZMA and PRF1 gene expression levels was used to calculate the immune cytolytic activity [20]. For each gene, the TPM (transcript per million) values were z-scored across samples to determine the amount of gene expression.

Multiplexed immunohistochemistry

According to the manufacturer’s instructions, mIHC was carried out by staining 4-um-thick formalin-fixed, paraffin-embedded whole tissue sections in a sequential manner using standard, primary antibodies and pairing them with TSA 6-color kit (H-D110061,yuanxibio, China). Using the C-kit marker, resting mast cells were identified. B cells were identified using the CD20 marker. T follicular helper cells were identified by CD4 and CXCR5. Different antibodies/fluorescent dyes were sequentially applied, including anti-CXCR5(Cat# 72172s, Cell Signaling Technology, USA)/Novo-Light 520, anti-CD4 (Cat# ab133616, abcam, UK)/Novo-Light 570, anti-CD20 (Cat# CM-0221, jiehaobio, China)/Novo-Light 670, anti-PanCK(Cat# GM351507, Gene Tech, China)/Novo-Light 440. Finally, all slides were stained with DAPI (A11010-100T; yuanxibio, China) and pictures was taken by Pannoramic MIDI tissue imaging system (3DHISTECH, HUN). Indica Halo software were used for analyzing images.

Statistical analysis

The differences between groups was analyzed by non-parametric Wilcoxon’s test, and P < 0.05 was considered to determine as statistically significant difference. To adjust for multiple testing, FDR was employed. All statistical analyses were carried out using R (v.3.5.3).

Results

Characteristics of patient cohort

We obtained 10 primary tumor samples from stage IB-IIA CSCC patients during surgical resection. The clinical characteristics of samples were summarized in Table 1. The median age of the patients was 51.5 years (range: 37–61 years). The follow-up time ranged from 10 to 48 months, with a median of 32 months. The patient distribution was similar between the non-recurrence group and the recurrence group.

Table 1 Demographics and clinical characteristics of patients

Characteristic	Recurrence (n = 6)	Non-recurrence (n = 4)	P value	
Age (mean, years)	47.5 (37–60)	56 (49–61)	0.240	
Age (%)			0.524	
 ≤ 50	4 (66.7)	1 (25)		
 > 50	2 (33.3)	3 (75)		
Tumor size (%)			0.400	
 < 4 cm2	6 (100)	3 (75)		
 ≥ 4 cm2	0 (0)	1 (25)		
HPV infection status (%)			0.190	
 Positive	5 (83.3)	1 (25)		
 Negative	1 (16.7)	3 (75)		
Stromal invasion (%)			0.571	
 Superficial 1/3 and middle 1/3	3 (50)	3 (75)		
 Deep 1/3	3 (50)	1 (25)		
Lymph node metastasis (%)			1.000	
 No	4 (66.7)	3 (75)		
 Yes	2 (33.3)	1 (25)		
Recurrence pattern (%)				
 Local recurrence	3 (60)			
 Remote metastasis	2 (40)			
Differentiation (%)			0.054	
 Well differentiated	2 (33.3)	2 (50)		
 Moderately differentiated	4 (66.7)	0 (0)		
 Poorly differentiated	0	2 (50)		
FIGO stage (2009) Stage (%)			0.644	
 IB1	1 (16.7)	1 (25)		
 IB2	2 (33.3)	2 (50)		
 IIA1	1 (16.7)	1 (25)		
 IIA2	2 (33.3)	0 (0)		
Surgery type (%)			1.000	
 LRH	5 (83.3)	4(100)		
 ARH	1 (16.7)	0(0)		
Adjuvant therapy (%)			0.644	
 No	2 (33.3)	1(25)		
 Radiation	1 (16.7)	0(0)		
 CCRT	2 (33.3)	1(25)		
 Chemo	1 (16.7)	2(50)		
LVSI (%)			1.000	
 No	3 (50)	2(50)		
 Yes	3 (50)	2(50)		
FIGO: International Federation of Gynecology and Obstetrics; LVSI: lymphovascular space invasion; LRH: laparoscopic radical hysterectomy; ARH: abdominal radical hysterectomy

Specific gene expression and biological function

First, we performed GO pathway enrichment analysis and differential gene expression analysis. In comparison to the non-recurrence group, a total of 1198 genes were expressed differently in the recurrence group. The top 10 upregulated genes of statistical significance were ZNF90, ZNF551, RGPD2, C22orf34, ZNF813, C5AR2, ADAMTS4, MYADM, ZNF776 and SMOC2 (Fig. 2A) respectively. The top 10 downregulated genes in order of effect size were HIST1H2AB, SRP72P1, AC005523.2, MTND1P23, RTP3, RP13-996F3.5, TRIM54, HIST1H2BF, HIST1H3B, and ANKRD33B (Fig. 1A), since several of downregulated genes had similarly high statistical significance. Upregulation pathway analysis showed enrichments in the following cancer-related pathways in the order of significance: PI3K-AKT, ECM-receptor interaction, Cell adhesion molecules (CAMs), Complement and coagulation cascades, Phagosome and cGMP-PKG signaling pathways (Fig. 1B). Out of all the enriched GO terms, the top five upregulated and downregulated terms are related to cell, cell part, organelle, binding, biological regulation, cellular process, and single-organism process (Fig. 1C). And we discovered that the recurrence and non-recurrence groups can be distinctly differentiated based on the top 50 differentially expressed genes. This distinction is evident from the clear separation of the recurrence samples (highlighted in salmon color) and the non-recurrence samples (highlighted in azure color). (Fig. 1D).

Fig. 1 Transcriptomics landscape revealed a distinct pattern between recurrence and non-recurrence group A Differential gene expression between recurrence and non-recurrence group. P-adjusted < 0.05 was considered significant. Genes upregulated in recurrence group with log2 fold change > 1 were colored in red. Genes downregulated in recurrence group with log2 fold change < − 1 were colored in blue. B Upregulation pathway enrichment in recurrence group compared to non-recurrence group. C GO (gene ontology) term enrichment in recurrence group compared to non-recurrence group. D Heatmap of top 50 differentially expressed genes between recurrence group and non-recurrence group. Values represent TPM z-scored across samples for each gene. Genes and samples were hierarchically clustered with dendrograms drawn on left and top of heatmap

Fig. 2 Tumor microenvironment (TME) status and immune infiltration characteristics A–B Immune cell infiltration estimate comparison. Infiltration % of immune cells was estimated for each sample with CIBERSORT and TIMER using gene expression data from RNA-seq. C Cytolytic activity score of recurrence vs. non-recurrence group. Cytolytic activity score is the median of all samples from respective groups, 2-sided Wilcoxon test was used. P-value < 0.05 was seen as statistically significant. D Immunomodulators median gene expression levels for recurrence and non-recurrence group. Heatmap TPM value is the median of all samples from respective groups, z-score normalized across the two groups. Two-sided Wilcoxon test was used. P-value < 0.05 was seen as statistically significant. *P < 0.05

Tumor microenvironment status and immune infiltration characteristics

Next, we study the differences in the tumor microenvironment of immune landscape between the recurrence and the non-recurrence group. Using CIBERSORT immune cell infiltration estimation package, we found that recurrence group had a significantly higher level of resting mast cells infiltration compared to non-recurrence group (P = 0.04; Fig. 2A), but significantly lower T follicular helper cells infiltration (P = 0.02; Fig. 2A). We also assessed immune cell infiltration by using TIMER, and found generally no significant difference between the two groups of patients, except for B cells significantly decreased in recurrence group (P = 0.04; Fig. 2B). We found no significant difference in cytolytic T-cell activity between two group by assessing the level of cytolytic T cell activity by geometric mean expression levels of GZMA and RPF1 (Fig. 2C). In addition, we also assessed immunomodulation via 74 genes expression involved in immunomodulation. Both groups exhibited low expression of stimulatory and inhibitory types of immunomodulators, except for the categories of antigen presentation. We found that the recurrence group had a greater level of immunomodulation for HLA-A and HLA-C, while lower for HLA − DRA compared to the non-recurrence group (Fig. 2D).

We validated the infiltration of selected cells in CSCC cohort patients utilizing mIHC staining (Fig. 3A, B). The tumor region was defined by PanCK, and the density of positive markers within the tumor region was also evaluated, excluding necrotic cells and tissues. The non-recurrence group had a strong indicator of antitumor immunity dynamism, even though the significance of these differences was limited due to the small sample size. The recurrence group was associated with lower densities of resting mast cells, T follicular helper cells, and B-cell inside the tumors compared to the non-recurrence group. (Fig. 3C–E).

Fig. 3 The Immune cell populations in cervical cancer were identified by mIHC A, B Typical micrographs of sections stained for recurrence (left, Pa2) and non-recurrence (right, Pa9) using multiplexed IHC and multispectral imaging, at 400× magnification; Antibody panel: CD4 (yellow), CXCR5 (green), CD20 (purple), C-kit (red), cytokeratin (CK, cyan), and 2-(4-amidinophenyl)-6-indolecarbamidine dihydrochloride (DAPI, blue). C–E Cell density inside tumor by TME status. Comparison of change in Resting mast cells, B cells and T follicular helper cells between recurrence and non-recurrence group based on multiplex immunofluorescence staining. Two-sided Wilcoxon test was used. P-value < 0.05 was seen as statistically significant

Discussion

CSCC is one of the most common gynecological malignancies worldwide. Currently, the primary methods for preventing gynecological malignancies encompass effective screening measures, vaccination, adopting a healthy lifestyle, and actively managing risk factors. Furthermore, it is imperative to develop novel biomarkers for enhanced diagnosis and treatment [21]. However, the etiology and molecular mechanisms that contribute to the progression and recurrence of CSCC are not yet fully understood. Surgery is currently the most effective curative treatment for stage IB-IIA CSCC, with the hope of entirely eradicating the tumor cells and improving survival. However, even with the assistance of postoperative radiotherapy or chemo-radiotherapy as adjuvant therapy, a portion of patients still experience a relapse. In our study, we used RNA-seq and mIHC analysis to uncover the key differences between recurrence patients and non-recurrence patients. Furthermore, we also selected prognostic biomarkers that could effectively stratify patients into tiers based on their varying risks of recurrence.

To investigate the underlying mechanism related to recurrence risk in stage IB-IIA CSCC patients, we performed comprehensive gene expression analysis. We found that the recurrence group exhibited distinct gene expression patterns compared to the non-recurrence group. For example, among the top 10 upregulated genes in recurrence compared to non-recurrence group, ZNF90, ZNF551, ZNF813, and ZNF776 were transcription factors that belong to the zinc finger proteins (ZFPs) family. ZFPs play a vital role in various biological processes such as development, differentiation, metabolism, and apoptosis. Furthermore, recent research has also demonstrated a closed connection between ZFPs and different stages of cancer development [22, 23]. Reports indicated that the upregulated expression of ZNF185 and ZEB1 was significantly related to the incidence of liver metastasis in patients with colon cancer. Furthermore, these two factors independently indicate the liver metastasis and prognosis [24]. Similarly, high expression of ZBTB20 and ZNF689 in hepatocellular carcinoma patients was strongly associated with poor clinical prognosis as well as a high recurrence rate [25]. On the contrary, significantly better survival rates were observed in the head and neck squamous cell carcinoma patients with higher levels of ZNF418 and ZNF540, compared to those with lower of expression [26].Our study revealed a strong association between high expression of ZFPs and the recurrence of cervical cancer. The facts above demonstrated that, the mechanisms through which ZFPs contribute to cancer progression can vary across different cancer types. In addition, we also found that low expression of HIST1H2AB, HIST1H2BF and HIST1H3B is associated with poor prognosis in recurrence group. The recent study found that decreased histone variant expression is a poor indicator in cervical cancer patients, and two gene sets (HIST1H2BD and HIST1H2BJ; and HIST1H2BD, HIST1H2BJ, HIST1H2BH, HIST1H2AM and HIST1H4K) can be employed as significant prognostic markers for survival prediction [27]. These histones may be modified by a large number of epigenetic modifications enzymes and are associated with multiple cancer developments [28].

With advances in molecular-biological research on the pathogenesis of cervical cancer, PI3K/AKT signaling pathway is known to play a central role in the growth and proliferation of CC cells [29, 30]. Our study showed that genes with high expression level are significantly involved in the PI3K/AKT pathway in recurrence group. It is notable that PI3K/AKT/mTOR have been found to be the indicators of poorer prognosis in a study on the predictive and prognostic role of molecular overexpression in cervical cancer [31]. According to the most recent CBioPortal of Cancer Genetics statistics (https://www.cbioportal.org/), PIK3CA is expressed in 35% of cervical cancer. In addition, direct inhibition of either the PI3K/Akt pathway or its target genes and molecules has shown a promising perspective for cervical cancer therapy [32–34].

We also studied the tumor immune microenvironment in the patients with relapse. In the analysis of immune cell infiltration, recurrence group showed significantly lower infiltration levels of two crucial anti-tumor immune cells—T follicular helper cells (Tfh) and B cells compared to non-recurrence group. Tfh cells, as a non-canonical role, can contribute to anti-tumor immunity. TFH cells produce CXCL13 to recruit lymphocytes and promote the formation of tertiary lymphoid structures (TLSs). Besides actively facilitating TLS formation, Tfh cells can indirectly enhance CD8 + T cell-mediated anti-tumor immunity through the secretion of IL-21 [35]. Our study identified that the relapse of CSCC patients was associated with lower Tfh cells. B cells primarily perform anti-tumor function in the tumor microenvironment by generating tumor-reactive antibodies and stimulating CD4 + and CD8 + T cells [36]. High expression of B cells was associated with improved overall survival in cervical cancer patients [37]. Resting mast cells is crucial in the treatment of cancer due to their distinctive developmental, phenotypic, and functional adaptability. These cells play a vital role in maintaining tissue homeostasis by constantly sampling the microenvironment [38]. Research found that high mast cell infiltration was associated with poor prognosis in patients with hepatocellular carcinoma [39].This is similar to our study results, where recurrent cervical cancer patients had higher expression of resting mast cells. Conversely, Zhang et al. categorized the meningioma samples based to the gene expression levels of resting mast cells in high- and low-risk groups. They found that the risk score positively correlated with the concentration of mast cells [40]. The composition of immune cells in cervical cancer tissue shows a specific pattern, indicating varying immune cell infiltration. The recurrence group was found to be situated within an immunosuppressive microenvironment.

For all this, identifying patients at high risk of relapse is challenging due to the complexity of their monitoring. In the current study, the main limitation is the small sample size of the cohort due to substandard tissue sample quality and incomplete follow-up information for patients with early-stage cervical cancer. While these samples allowed us to identify RNA expression differences between recurrence and non-recurrence patients, they limited our ability to conduct an in-depth study on the tumor immune microenvironment by mIHC. Additionally, the post-surgery follow-up time was relatively short. Therefore, further DFS follow-up data are needed to determine whether the long-term clinical benefit is related to pathological response.

In conclusion, we characterized distinct molecular characteristics of stage IB-IIA CSCC with a high risk of recurrence, as evidenced by changes in gene expression and immune conditions, implying that tumors may reshape their surrounding environment to facilitate relapse. However, these findings remain speculative, and require validation from a larger study group.

Acknowledgements

We thank all the participants and their families for supporting this study. We thank The Third Affiliated Hospital, Guangzhou Medical University in China for providing samples.

Author contributions

T.W. and X.S. directed and oversaw the project. M.W. and B.L. were involved in the diagnostic flow and patient follow-up, L.S. wrote this article. L.Y. and C.L. performed data formal analysis. All the authors participated in the discussion, data interpretation, and manuscript editing. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the Basic Research Project Jointly Funded by Universities (high-level universities) and Enterprises in Guangzhou, China (CN) (No. SL2022A03J01028), and Guangzhou Science and technology project (No. 2023A03J0375).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by The Third Affiliated Hospital of Guangzhou Medical University (No: 2021-054). Written informed consents were obtained from each patient at the time of sample submission.

Competing interests

L.S., L.Y. and C.L. are employees of Nanjing Geneseeq Technology, Inc. All remaining authors declare no conflicts of interest.

Publisher’s note

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

Meiyao Wu, Baixue Li and Lina Shi have contributed equally to this work.
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References

1. Sung H Ferlay J Siegel RL Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 2021 71 3 209 49 10.3322/caac.21660 33538338
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660.33538338 10.3322/caac.21660
2. Arbyn M Weiderpass E Bruni L Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis Lancet Glob Health 2020 8 2 e191 203 10.1016/s2214-109x(19)30482-6 31812369
Arbyn M, Weiderpass E, Bruni L, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health. 2020;8(2):e191-203. 10.1016/s2214-109x(19)30482-6.31812369 10.1016/s2214-109x(19)30482-6
3. Ronco G Dillner J Elfström KM Efficacy of HPV-based screening for prevention of invasive cervical cancer: follow-up of four European randomised controlled trials. Lancet (London, England) Feb 2014 8 9916 524 32 10.1016/s0140-6736(13)62218-7
Ronco G, Dillner J, Elfström KM, et al. Efficacy of HPV-based screening for prevention of invasive cervical cancer: follow-up of four European randomised controlled trials. Lancet (London, England). Feb. 2014;8(9916):524–32. 10.1016/s0140-6736(13)62218-7.10.1016/s0140-6736(13)62218-7
4. Orbegoso C Murali K Banerjee S The current status of immunotherapy for cervical cancer Rep Pract Oncol Radiother 2018 23 6 580 8 10.1016/j.rpor.2018.05.001 30534022
Orbegoso C, Murali K, Banerjee S. The current status of immunotherapy for cervical cancer. Rep Pract Oncol Radiother. 2018;23(6):580–8. 10.1016/j.rpor.2018.05.001.30534022 10.1016/j.rpor.2018.05.001
5. Uyar D Rader J Genomics of cervical cancer and the role of human papillomavirus pathobiology Clin Chem Jan 2014 60 1 144 6 10.1373/clinchem.2013.212985
Uyar D, Rader J. Genomics of cervical cancer and the role of human papillomavirus pathobiology. Clin Chem Jan. 2014;60(1):144–6. 10.1373/clinchem.2013.212985.10.1373/clinchem.2013.212985
6. Corrado G Anchora LP Bruni S Patterns of recurrence in FIGO stage IB1-IB2 cervical cancer: comparison between minimally invasive and abdominal radical hysterectomy Eur J Surg Oncol 2023 49 11 107047 10.1016/j.ejso.2023.107047 37862783
Corrado G, Anchora LP, Bruni S, et al. Patterns of recurrence in FIGO stage IB1-IB2 cervical cancer: comparison between minimally invasive and abdominal radical hysterectomy. Eur J Surg Oncol. 2023;49(11):107047. 10.1016/j.ejso.2023.107047.37862783 10.1016/j.ejso.2023.107047
7. Hiam-Galvez KJ Allen BM Spitzer MH Systemic immunity in cancer Nat Rev Cancer 2021 21 6 345 59 10.1038/s41568-021-00347-z 33837297
Hiam-Galvez KJ, Allen BM, Spitzer MH. Systemic immunity in cancer. Nat Rev Cancer. 2021;21(6):345–59. 10.1038/s41568-021-00347-z.33837297 10.1038/s41568-021-00347-z
8. Ma J Cheng P Chen X Zhou C Zheng W Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment PeerJ 2020 8 e9627 10.7717/peerj.9627 32904067
Ma J, Cheng P, Chen X, Zhou C, Zheng W. Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment. PeerJ. 2020;8:e9627. 10.7717/peerj.9627.32904067 10.7717/peerj.9627
9. Wang J Li Z Gao A Wen Q Sun Y The prognostic landscape of tumor-infiltrating immune cells in cervical cancer Biomed Pharmacother 2019 120 109444 10.1016/j.biopha.2019.109444 31562978
Wang J, Li Z, Gao A, Wen Q, Sun Y. The prognostic landscape of tumor-infiltrating immune cells in cervical cancer. Biomed Pharmacother. 2019;120:109444. 10.1016/j.biopha.2019.109444.31562978 10.1016/j.biopha.2019.109444
10. Peng L Hayatullah G Zhou H Tumor microenvironment characterization in cervical cancer identifies prognostic relevant gene signatures PLoS ONE 2021 16 4 e0249374 10.1371/journal.pone.0249374 33901225
Peng L, Hayatullah G, Zhou H, et al. Tumor microenvironment characterization in cervical cancer identifies prognostic relevant gene signatures. PLoS ONE. 2021;16(4):e0249374. 10.1371/journal.pone.0249374.33901225 10.1371/journal.pone.0249374
11. Bolger AM Lohse M Usadel B Trimmomatic: a flexible trimmer for Illumina sequence data Bioinformatics 2014 30 15 2114 20 10.1093/bioinformatics/btu170 24695404
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. 10.1093/bioinformatics/btu170.24695404 10.1093/bioinformatics/btu170
12. Dobin A Davis CA Schlesinger F STAR: ultrafast universal RNA-seq aligner Bioinformatics 2013 29 1 15 21 10.1093/bioinformatics/bts635 23104886
Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. 10.1093/bioinformatics/bts635.23104886 10.1093/bioinformatics/bts635
13. Li B Dewey CN RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome BMC Bioinf 2011 4 323 10.1186/1471-2105-12-323
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinf. 2011;4:323. 10.1186/1471-2105-12-323.10.1186/1471-2105-12-323
14. Robinson MD McCarthy DJ Smyth GK edgeR: a bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 2010 1 1 139 40 10.1093/bioinformatics/btp616
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;1(1):139–40. 10.1093/bioinformatics/btp616.10.1093/bioinformatics/btp616
15. Zhou Y Zhou B Pache L Metascape provides a biologist-oriented resource for the analysis of systems-level datasets Nat Commun 2019 3 1 1523 10.1038/s41467-019-09234-6
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;3(1):1523. 10.1038/s41467-019-09234-6.10.1038/s41467-019-09234-6
16. Klopfenstein DV Zhang L Pedersen BS GOATOOLS: a Python library for gene ontology analyses Sci Rep 2018 18 1 10872 10.1038/s41598-018-28948-z
Klopfenstein DV, Zhang L, Pedersen BS, et al. GOATOOLS: a Python library for gene ontology analyses. Sci Rep. 2018;18(1):10872. 10.1038/s41598-018-28948-z.10.1038/s41598-018-28948-z
17. Chen B Khodadoust MS Liu CL Newman AM Alizadeh AA Profiling tumor infiltrating Immune cells with CIBERSORT Methods Mol Biol 2018 1711 243 59 10.1007/978-1-4939-7493-1_12 29344893
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating Immune cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–59. 10.1007/978-1-4939-7493-1_12.29344893 10.1007/978-1-4939-7493-1_12
18. Li T Fu J Zeng Z TIMER2.0 for analysis of tumor-infiltrating immune cells Nucleic Acids Res 2020 48 W1 W509 14 10.1093/nar/gkaa407 32442275
Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-14. 10.1093/nar/gkaa407.32442275 10.1093/nar/gkaa407
19. Thorsson V Gibbs DL Brown SD The immune landscape of cancer Immunity 2018 48 4 812 830.e14 10.1016/j.immuni.2018.03.023 29628290
Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer. Immunity. 2018;48(4):812-830.e14. 10.1016/j.immuni.2018.03.023.29628290 10.1016/j.immuni.2018.03.023
20. Rooney MS Shukla SA Wu CJ Getz G Hacohen N Molecular and genetic properties of tumors associated with local immune cytolytic activity Cell 2015 15 1–2 48 61 10.1016/j.cell.2014.12.033
Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;15(1–2):48–61. 10.1016/j.cell.2014.12.033.10.1016/j.cell.2014.12.033
21. Ferrari F Giannini A Approaches to prevention of gynecological malignancies BMC Women’s Health 2024 24 1 254 10.1186/s12905-024-03100-4 38654319
Ferrari F, Giannini A. Approaches to prevention of gynecological malignancies. BMC Women’s Health. 2024;24(1):254. 10.1186/s12905-024-03100-4.38654319 10.1186/s12905-024-03100-4
22. Jen J Wang YC Zinc finger proteins in cancer progression J Biomedical Sci 2016 23 1 53 10.1186/s12929-016-0269-9
Jen J, Wang YC. Zinc finger proteins in cancer progression. J Biomedical Sci. 2016;23(1):53. 10.1186/s12929-016-0269-9.10.1186/s12929-016-0269-9
23. Ye Q Liu J Xie K Zinc finger proteins and regulation of the hallmarks of cancer Histol Histopathol 2019 34 10 1097 109 10.14670/hh-18-121 31045237
Ye Q, Liu J, Xie K. Zinc finger proteins and regulation of the hallmarks of cancer. Histol Histopathol. 2019;34(10):1097–109. 10.14670/hh-18-121.31045237 10.14670/hh-18-121
24. Liu S Sima X Liu X Chen H Zinc finger proteins: functions and mechanisms in colon cancer Cancers 2022 10.3390/cancers14215242 36612279
Liu S, Sima X, Liu X, Chen H. Zinc finger proteins: functions and mechanisms in colon cancer. Cancers. 2022. 10.3390/cancers14215242.36612279 10.3390/cancers14215242
25. Li X Han M Zhang H Structures and biological functions of zinc finger proteins and their roles in hepatocellular carcinoma Biomark Res 2022 9 1 2 10.1186/s40364-021-00345-1
Li X, Han M, Zhang H, et al. Structures and biological functions of zinc finger proteins and their roles in hepatocellular carcinoma. Biomark Res. 2022;9(1):2. 10.1186/s40364-021-00345-1.10.1186/s40364-021-00345-1
26. Sobocińska J Nowakowska J Molenda S Zinc finger proteins in Head and Neck squamous cell carcinomas: ZNF540 may serve as a biomarker Curr Oncol 2022 16 12 9896 915 10.3390/curroncol29120779
Sobocińska J, Nowakowska J, Molenda S, et al. Zinc finger proteins in Head and Neck squamous cell carcinomas: ZNF540 may serve as a biomarker. Curr Oncol. 2022;16(12):9896–915. 10.3390/curroncol29120779.10.3390/curroncol29120779
27. Li X Tian R Gao H Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients Sci Rep Nov 2017 28 1 16495 10.1038/s41598-017-16472-5
Li X, Tian R, Gao H, et al. Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients. Sci Rep Nov. 2017;28(1):16495. 10.1038/s41598-017-16472-5.10.1038/s41598-017-16472-5
28. Zhao Z Shilatifard A Epigenetic modifications of histones in cancer Genome Biol 2019 20 1 245 10.1186/s13059-019-1870-5 31747960
Zhao Z, Shilatifard A. Epigenetic modifications of histones in cancer. Genome Biol. 2019;20(1):245. 10.1186/s13059-019-1870-5.31747960 10.1186/s13059-019-1870-5
29. Liang J Slingerland JM Multiple roles of the PI3K/PKB (akt) pathway in cell cycle progression Cell Cycle 2003 2 4 339 45 10.4161/cc.2.4.433 12851486
Liang J, Slingerland JM. Multiple roles of the PI3K/PKB (akt) pathway in cell cycle progression. Cell Cycle. 2003;2(4):339–45.12851486 10.4161/cc.2.4.433
30. Zhang L Wu J Ling MT Zhao L Zhao KN The role of the PI3K/Akt/mTOR signalling pathway in human cancers induced by infection with human papillomaviruses Mol Cancer 2015 17 87 10.1186/s12943-015-0361-x
Zhang L, Wu J, Ling MT, Zhao L, Zhao KN. The role of the PI3K/Akt/mTOR signalling pathway in human cancers induced by infection with human papillomaviruses. Mol Cancer. 2015;17:87. 10.1186/s12943-015-0361-x.10.1186/s12943-015-0361-x
31. Faried LS Faried A Kanuma T Predictive and prognostic role of activated mammalian target of rapamycin in cervical cancer treated with cisplatin-based neoadjuvant chemotherapy Oncol Rep 2006 16 1 57 63 16786123
Faried LS, Faried A, Kanuma T, et al. Predictive and prognostic role of activated mammalian target of rapamycin in cervical cancer treated with cisplatin-based neoadjuvant chemotherapy. Oncol Rep. 2006;16(1):57–63.16786123
32. Wu J Chen C Zhao KN Phosphatidylinositol 3-kinase signaling as a therapeutic target for cervical cancer Curr Cancer Drug Targets  2013 13 2 143 56 10.2174/1568009611313020004 23297827
Wu J, Chen C, Zhao KN. Phosphatidylinositol 3-kinase signaling as a therapeutic target for cervical cancer. Curr Cancer Drug Targets  2013;13(2):143–56. 10.2174/1568009611313020004.23297827 10.2174/1568009611313020004
33. Sun G Zhang Q Liu Y Xie P Role of phosphatidylinositol 3-kinase and its catalytic unit PIK3CA in cervical cancer: a mini-review Appl Bionics Biomech 2022 2022 6904769 10.1155/2022/6904769 36046780
Sun G, Zhang Q, Liu Y, Xie P. Role of phosphatidylinositol 3-kinase and its catalytic unit PIK3CA in cervical cancer: a mini-review. Appl Bionics Biomech. 2022;2022:6904769. 10.1155/2022/6904769.36046780 10.1155/2022/6904769
34. Bogani G Chiappa V Bini M BYL719 (alpelisib) for the treatment of PIK3CA-mutated, recurrent/advanced cervical cancer Tumori 2023 109 2 244 8 10.1177/03008916211073621 35311394
Bogani G, Chiappa V, Bini M, et al. BYL719 (alpelisib) for the treatment of PIK3CA-mutated, recurrent/advanced cervical cancer. Tumori. 2023;109(2):244–8. 10.1177/03008916211073621.35311394 10.1177/03008916211073621
35. Yu D Walker LSK Liu Z Linterman MA Li Z Targeting T(FH) cells in human diseases and vaccination: rationale and practice Nat Immunol 2022 23 8 1157 68 10.1038/s41590-022-01253-8 35817844
Yu D, Walker LSK, Liu Z, Linterman MA, Li Z. Targeting T(FH) cells in human diseases and vaccination: rationale and practice. Nat Immunol. 2022;23(8):1157–68. 10.1038/s41590-022-01253-8.35817844 10.1038/s41590-022-01253-8
36. Downs-Canner SM Meier J Vincent BG Serody JS B cell function in the tumor microenvironment Annual Rev Immunol 2022 40 169 93 10.1146/annurev-immunol-101220-015603 35044794
Downs-Canner SM, Meier J, Vincent BG, Serody JS. B cell function in the tumor microenvironment. Annual Rev Immunol. 2022;40:169–93. 10.1146/annurev-immunol-101220-015603.35044794 10.1146/annurev-immunol-101220-015603
37. Kim SS Shen S Miyauchi S B cells improve overall survival in HPV-associated squamous cell carcinomas and are activated by radiation and PD-1 blockade Clin Cancer Res 2020 1 13 3345 59 10.1158/1078-0432.Ccr-19-3211
Kim SS, Shen S, Miyauchi S, et al. B cells improve overall survival in HPV-associated squamous cell carcinomas and are activated by radiation and PD-1 blockade. Clin Cancer Res. 2020;1(13):3345–59. 10.1158/1078-0432.Ccr-19-3211.10.1158/1078-0432.Ccr-19-3211
38. Frossi B Mion F Tripodo C Colombo MP Pucillo CE Rheostatic functions of mast cells in the control of Innate and adaptive immune responses Trends Immunol 2017 38 9 648 56 10.1016/j.it.2017.04.001 28462845
Frossi B, Mion F, Tripodo C, Colombo MP, Pucillo CE. Rheostatic functions of mast cells in the control of Innate and adaptive immune responses. Trends Immunol. 2017;38(9):648–56. 10.1016/j.it.2017.04.001.28462845 10.1016/j.it.2017.04.001
39. Zhang H Sun L Hu X Mast cells resting-related prognostic signature in hepatocellular carcinoma J Oncol 2021 2021 4614257 10.1155/2021/4614257 34840569
Zhang H, Sun L, Hu X. Mast cells resting-related prognostic signature in hepatocellular carcinoma. J Oncol. 2021;2021:4614257. 10.1155/2021/4614257.34840569 10.1155/2021/4614257
40. Xie H Yuan C Ding XH Li JJ Li ZY Lu WC Identification of key genes and pathways associated with resting mast cells in meningioma BMC Cancer 2021 12 1 1209 10.1186/s12885-021-08931-0
Xie H, Yuan C, Ding XH, Li JJ, Li ZY, Lu WC. Identification of key genes and pathways associated with resting mast cells in meningioma. BMC Cancer. 2021;12(1):1209. 10.1186/s12885-021-08931-0.10.1186/s12885-021-08931-0
