Cervical cancer (CC) is the fourth leading cause of cancer-related death among all types of women malignancies (Sung et al., 2021). The two major histological types of CC are squamous cell carcinoma of cervix (SCC) and adenocarcinoma of cervix (ADC), accounting for 70% and 25% of all cases, respectively (Cohen et al., 2019). Compared to SCC, ADC patients have a higher rate of rapid progression, relapse, and insensitivity to chemotherapy, radiotherapy, and even immunotherapy. Therefore, ADC is considered a more aggressive phenotype than SCC (Sasieni et al., 2009). Unfortunately, the current standard treatment for CC is not stratified based on pathologic types. As a result, ADC patients often have suffered from therapeutic failures and experienced short-term recurrence and metastasis, even finishing standard treatment procedures (Sasieni et al., 2009). However, due to the low prevalence of ADC, in-depth investigations are insufficient. Furthermore, the molecular mechanisms elucidating its aggressiveness and the associated biomarkers are not yet well understood. Therefore, this study aims to comprehensively compare the intratumor heterogeneity of ADC, hoping to find out the potential molecule markers specifically associated with ADC.

For early-stage CC patients, it is a crucial and unavoidable issue to have post-surgical upstaging. Over 10% of patients, who are initially diagnosed with early stage and deemed suitable for radical surgery, end up being upstaged due to lymph node (LN) metastasis discovered after the surgery (Dabi et al., 2018; Thelissen et al., 2022). This indicates misdiagnosis, leading to different therapeutic strategies and outcome. According to the National Comprehensive Cancer Network (NCCN) guidelines, these misdiagnosed patients require additional radiotherapy after surgery, even though they have underwent surgery (Bhatla et al., 2018). However, combo treatment of surgery and radiotherapy may cause additional complications and heavier financial burdens, without offering more benefits than radiotherapy alone (Cushman et al., 2018; Kashima et al., 2023). Actually, the optimal therapeutic approach should be radiotherapy alone if the presence of LN metastasis could have been accurately diagnosed beforehand (Abu-Rustum et al., 2020). To avoid this problem, the International Federation of Gynecology and Obstetrics (FIGO) in 2018 updated a new stage as IIIC by including LN status and stratified this stage into two subgroups: IIICp (p stands for pathological findings of LN, which means misdiagnosis) and IIICr (r stands for radiological findings of LN, which means pre-surgical evidence) (Bhatla et al., 2018). Unfortunately, radiological imaging tools do have limitations since CT or MRI can only detect suspiciously metastatic LN when the minimum diameter is ≥10 mm (Saleh et al., 2020). Therefore, together with radiological diagnosis, it is urgent to search for potential molecular predictors of LN metastasis in order to decrease the rate of post-surgical upstaging.

Persistent infection with human papillomavirus (HPV) is the primary cause of CC (Mann et al., 2023). However, there are about 5–15% of patients testing negative for HPV (Yang et al., 2023). It is reported that HPV-negative patients tend to experience worse clinical outcomes (Fernandes et al., 2022). However, current therapeutic strategies for CC do not take into account the HPV infection status as a factor for stratified therapy. Therefore, it is imperative to investigate the precise mechanisms underlying the development of HPV non-infected tumors.

The tumor immune microenvironment (TIME), consisting of tumor cells, immune cells, cytokines, and their interactions, predominantly determines the efficacy of immunotherapy (Lv et al., 2022). In CC, while it has been reported that 30–50% of patients exhibit positive PD-L1 expression, however, only an estimated 20–30% are expected to respond positively to PD-1/PD-L1 blockade therapy (Omenai et al., 2022; Sun et al., 2020). Therefore, CC, especially the pathological type of ADC, is characterized as an immune-insensitive cancer type due to atypical modulation of the TIME (Cao et al., 2023). The mechanisms of tumor immune evasion are basically ascribed to dysregulation of TIME and are similar across different types of cancer, involving the deactivation of immune surveillance pathways or recruitment of immunosuppressive cells (Vinay et al., 2015). However, the specific characteristics of the TIME in ADC remain poorly understood. Therefore, in order to optimize precise therapeutic strategies and enhance the efficacy of immunotherapy, it is indeed imperious to investigate the intricacies of TIME in ADC.

To extensively investigate the cellular and molecular heterogeneity of CC, particularly ADC, the utilization of single-cell RNA sequencing (scRNA-seq) technique is crucially needed. In the current study, we focused on the remodeling of TIME in ADC and performed scRNA-seq analysis on 11 ADC samples, with another 4 SCC samples included as controls. By high-throughput analysis, we are able to characterize the landscape of TIME in ADC, with different types of tumor-promoting cells being recruited in the vicinity of the tumor, thus creating an immunosuppressive environment in ADC. The cellular crosstalk analysis provides valuable insights into the interactions between different cell types within the TIME. Furthermore, we have conducted a screening of novel signature genes as prognostic biomarkers for ADC. These biomarkers have the potential to possibly predict LN metastasis and may guide personalized treatment decisions more effectively.

CC is often regarded as less aggressive compared to ovarian cancer and endometrial cancer, primarily due to the effectiveness of HPV vaccines in prevention. When diagnosed at early stages, the 5-year survival rate for CC patients exceeds 90%, surpassing that of other gynecologic malignancies (Gennigens et al., 2022; Schiffman et al., 2007). However, if CC metastasizes to the intraperitoneal or retroperitoneal LNs, the survival rate will decrease to 30–60% (Cohen et al., 2019). Worse still, the 5-year survival rate for recurrent CC patients is less than 20% (Li et al., 2016). Specifically, when considering the ADC type alone, the prognosis is even worse. Unfortunately, our understanding for ADC is limited. Most previous studies have concentrated on SCC type of CC, and obtained heterogenetic information derived from bulk RNA-seq method. Therefore, it is worthwhile to shift our attention toward an in-depth investigation on the disparities between ADC and SCC at the single-cell resolution.

More recently, researchers have started to utilize scRNA-seq approach to investigate the tumor microenvironment of CC. For instance, Cao et al., 2023 compared five cases of SCC tumor tissues with paired adjacent normal tissues and described the immune landscape in which specific clusters of T and B cells exhibited immune-exhausting or activating processes. Another study conducted by Qiu et al., 2023 aimed to elucidate the distinct molecular patterns of immune reactions between ADC and SCC in the context of different HPV infection status at the single-cell level. However, due to limited samples tested in these studies, our knowledge on the TIME in CC is yet inadequate. In the current study, we have generated a genomic atlas of ADC, which may depict the landscape of TIME built by both ADC tumor cells and neighboring cells. Apart from the predominant presence of epithelial cells, the cell clusters in ADC are majorly composed of T cells, tumor-associated neutrophils (TANs), and plasma/B cells. The gene subsets identified within ADC-abundant cell sub-clusters, including Epi_10_CYSTM1, Tregs, pro-tumor TANs, and P/B_01_IGHA2, exert potential roles in promoting both the carcinogenesis and immunosuppression of ADC. In the TIME of ADC, the unique module of cellular interaction has preliminarily indicated that the immunosuppressive Tregs are recruited to tumor cells via chemo-attraction. Finally, our hypothesis from scRNA-seq data is further confirmed using clinical samples, providing evidence that SLC26A3 may be a potential biomarker for ADC (Figure 7—figure supplement 2).

It has been reported that immunotherapies are less effective in ADC patients, especially those who are not infected with HPV (Attademo et al., 2020; Ferrall et al., 2021). As the two major components of ADC cell types in our study, T cells and epithelial cells have been implied to be the dominant regulators in the TIME due to their reciprocal interaction dynamics. We identified several ADC-specific epithelial cell clusters that may exhibit stronger stemness and aggressiveness than other clusters. Among these clusters, Epi_10_CYSTM1 seemed to have the highest level of malignancy, but the underlying mechanism behind this behavior remains unknown. The CellChat analysis revealed that Tregs might quite actively crosstalk to Epi_10_CYSTM1. To confirm this, immunostaining for co-localization demonstrated an increased aggregation of Tregs around tumor cells exhibiting overexpression of SLC26A3. It has also been widely reported that tumor cells acquire higher stemness present more aggressive features of resistance to immunotherapy (Dianat-Moghadam et al., 2022). This study indicated that TGFβ produced by Tregs might induce the stemness of Epi_10_CYSTM1 cells by activating downstream oncogenic pathways, inducing EMT on tumor epithelial cells. Because TGFβ is a soluble protein and is hard to detect on solid tissue samples, here we only validated markers of EMT and stemness to indirectly indicate the functional existence of TGFβ, which is predicted from informatic hypothesis (Nixon et al., 2023). Interestingly, Epi_10_CYSTM1 cells express ALCAM, which is a membranal ligand, and bind to its receptor CD6 on Tregs, activating chemotaxis and facilitating Treg recruitment (Figure 7I). The infiltration of a large number of Tregs in ADC may significantly accelerate immune evasion (Figure 7—figure supplement 2). Therefore, the presence of a higher number of Tregs infiltrating the tumor area of ADC may partially explain the increased resistance to immunotherapy.

Another interesting phenomenon observed in our sing-cell sequencing data is the involvement of TANs in the regulatory interactions within the TIME of ADC. It has been clearly investigated that pro-tumor TANs, characterized by CD66, CD11, CD170, and PD-L1, exert the oncogenic roles by promoting tumor cell proliferation, angiogenesis, inducing genetic instability, and most importantly causing immunosuppression (Jaillon et al., 2020). In this study, pro-tumor TANs are relatively enriched in ADC tissues compared to SCC, especially in HPV-negative ADC cases. This suggests that ADC-specific epithelial cell sub-clusters may actively engage in the recruitment of pro-tumor TANs.

The anti-tumor function of B cells has been identified previously (Bod et al., 2023; Helmink et al., 2020). However, our study has presented a specific cluster of plasma/B cells with tumor-promoting functions. The overexpression of gene signatures from Plasma/B_01_IGH2 cells is associated with poor regression in CC patients (Figure 6F). Interestingly, Plasma/B_01-IGHA2 was found to be closely related to the Plasma cell Cluster (PC Cluster) in Cao’s study due to the shared gene set (Cao et al., 2023), such as IGHA1, IGHA2, IGHG2, and IGHG4 However, their roles were contradictory. According to that study, PC cluster was derived from the local expansion of TIME and showed potential effectiveness in anti-tumor immunity. In our case, Plasma/B_01-IGHA2 was indicated to have a reverse effect and played a tumor-promoting role in ADC. This difference in function is likely attributed to the distinct cell types and their interactions in ADC, instead of SCC. However, the underneath dynamics of B cells in ADC still needs further investigation.

Owing to post-surgical upstaging, some CC patients with stage IIIC are misdiagnosed as early-stage and have undergone radical surgeries. For these patients, the most appropriate strategy of treatment should be radiotherapy alone. However, it is challenging to avoid this issue because it is difficult to identify metastatic LN only through CT or MRI. To solve this problem, a reliable biomarker is required to diagnose LN metastasis in conjunction with radiographic tool. In this study, SLC26A3 has shown its potential as a diagnostic biomarker to predict LN metastasis in ADC patients. Combining two large cohorts of clinical data, we found that CC patients with ADC types were more vulnerable to be misdiagnosed for clinical staging. Derived from scRNA-seq data, SLC26A3 could be employed as a representative gene for the Epi_10_CYSTM1 cluster, which was found to be enriched in stage IIIC cases of ADC. The IHC staining further confirmed that ADC patients at stage IIIC exhibited a higher expression of SLC26A3 compared to early stages (FIGO stages I–II). Furthermore, we tested on biopsy samples and it implied that SLC26A3 might serve as a potential predictor of LN metastasis. Our study for the first time provides evidence that ADC patients are at a higher risk of developing LN metastasis, which can be challenging to detect solely via radiological imaging techniques. However, by incorporating LN imaging with biomarkers such as SLC26A3, it may probably reduce the misdiagnosis rate among FIGO IIIC patients.

In this study, our objective is to unravel the genomic characteristics of ADC in the hope of identifying gene signatures that can elucidate its aggressive nature. SLC26A3 is selected from the DEGs enriched in cluster Epi_10_CYSTM1, and is identified to specifically represent this ADC-specific cluster. However, based on our data, we have inevitably encountered two questions: (1) the existence of cluster Epi_10_CYSTM1 and (2) the specificity of SLC26A3. As for the first question, Epi_10_CYSTM1 is distinctive from the other 11 epithelial cell clusters, with specific DEGs (Figure 2A–C). Although Epi_10_CYSTM1 is dominantly derived from sample S7, however, its proportion is relatively high when compared with other clusters such as Epi_08_SCGB3A1, Epi_09_SST, and Epi_11_REG1A (Figure 2D, Figure 2—figure supplement 1A and B). Moreover, we have detected a positive expression of this cluster on tissue samples, whether by using ORM1/2 or SLC26A3 antibodies (two candidate markers of cluster Epi_10_CYSTM1) via IHC. As a result, we have identified the existence of this cell cluster. As for the second question, SLC26A3 has demonstrated a higher specificity compared to other candidates, such as CYSTM1, CA9, ORM1, and ORM2, following the process of identifying markers (Figure 3A and B, Figure 3—figure supplement 1E). Although the IHC results indicate that SLC26A3 is positively expressed in stage IIIC cases, however, the expression is confined to specific regions (Figure 3—figure supplement 2), which aligns with the scRNA-seq findings for SLC26A3. Notably, according to Tables 3 and 4, most of the clinical cases have a lower expression of SLC26A3. Taken together, SLC26A3 is a specific representative of cluster Epi_10_CYSTM1.

In summary, we characterized the genomic landscape of the TIME in ADC at a single-cell resolution. Our study revealed the presence of specific epithelial cell clusters in ADC that exhibited more aggressive features. The enriched gene signatures from these clusters may serve as potential therapeutic targets to improve the efficacy of immunotherapy. The interactions between tumor epithelial cells and other types of cells, such as T cells and neutrophils, appropriately depict a highly immunosuppressive microenvironment of ADC. Moreover, our investigation extended to the issue of post-surgical upstaging at the single-cell level, with the identification of biomarkers from specific tumor cell clusters showing potential as diagnostic indicators for the precise diagnosis of CC patients.

In this study, the single-cell RNA sequencing data generated is available in the NCBI Sequence Read Archive with accession number: PRJNA1231266.

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Author details

1. Yang Peng

   Fourth Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration

   Contributed equally with

   Jing Yang

   Competing interests

   No competing interests declared

2. Jing Yang

   Department of Pathology, Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Yang Peng

   Competing interests

   No competing interests declared

3. Jixing Ao

   Department of Gynecologic Oncology, Changsha Kexin Cancer Hospital, Changsha, China

   Contribution

   Resources, Data curation, Software, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Yilin Li

   Department of Pathology, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China

   Contribution

   Resources, Data curation, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

5. Jia Shen

   1. Xiangya Cancer Center, Xiangya Hospital, Central South University, Changsha, China
   2. Hunan International Science and Technology Collaboration Base of Precision Medicine for Cancer, Changsha, China
   3. Key Laboratory of Molecular Radiation Oncology of Hunan Province, Changsha, China

   Contribution

   Data curation, Software, Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

6. Xiang He

   1. Xiangya Cancer Center, Xiangya Hospital, Central South University, Changsha, China
   2. Hunan International Science and Technology Collaboration Base of Precision Medicine for Cancer, Changsha, China
   3. Key Laboratory of Molecular Radiation Oncology of Hunan Province, Changsha, China

   Contribution

   Data curation, Software, Investigation, Visualization

   Competing interests

   No competing interests declared

7. Dihong Tang

   Fourth Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Chaonan Chu

   Fourth Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China

   Contribution

   Formal analysis, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

9. Congrong Liu

   Department of Pathology, Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   For correspondence

   congrong_liu@hsc.pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9876-1603

10. Liang Weng

    1. Department of Pathology, Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    2. Xiangya Cancer Center, Xiangya Hospital, Central South University, Changsha, China
    3. Hunan International Science and Technology Collaboration Base of Precision Medicine for Cancer, Changsha, China
    4. Key Laboratory of Molecular Radiation Oncology of Hunan Province, Changsha, China

    Contribution

    Conceptualization, Software, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    wengliang@pku.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7058-8274

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