Introduction

Cervical cancer is the fourth most frequently diagnosed malignancy in women worldwide after breast, colorectal, and lung cancers.1,2 Because early-stage cervical cancer often presents with minimal or nonspecific symptoms, some patients are diagnosed only after the disease has progressed or recurred.3,4 On the other hand, despite an early stage diagnosis, patients (15%-61%) experience recurrence or metastasis within the first two years after initial treatment.5 Due to tissue fibrosis caused by prior surgery or chemoradiotherapy, tumor resistance resulting from tumor heterogeneity, and increased damage to normal tissues associated with re-irradiation, treatment options for advanced cervical cancer remain limited, and no optimal clinical treatment strategy has yet been established.6 The five-year survival rate for early stage cervical cancer is ~90%, but it drops to ~20% in metastatic cases. Despite modern medical advances, approximately 30% of patients with locally advanced disease experience recurrence. Patients with metastatic or recurrent disease have poor prognosis. Treatment for this group is largely limited to chemotherapy and targeted therapies.7,8

In recent years, with a growing understanding of tumor molecular targets and the tumor microenvironment, antitumor immunotherapy has become a new frontier in comprehensive cancer treatment. Immune checkpoint blockades (ICBs) are a class of therapeutic agents that reactivate the antitumor activity of immune cells by abolishing T-cell inhibitory signaling pathways.9 Their primary targets include PD-1, PD-L1, and CTLA-4.10 PD-1 is a key checkpoint regulator of the immune response; as a T-cell co-inhibitory receptor composed of 288 amino acid residues, it normally functions to maintain immune homeostasis.11 However, PD-1 functions by facilitating tumor immune evasion. It is expressed on mature T and B cells, thymocytes, and activated myeloid cells,12 PD-L1 shows minimal expression under normal physiological conditions. Upon malignant transformation, PD-L1 is overexpressed in both tumor cells and tumor-infiltrating lymphocytes, serving as a major driver for tumors to escape immune surveillance and promote malignant growth.

The positive expression of PD-L1 in cervical cancer has been widely documented. Feng et al examined 219 cases of cervical squamous cell carcinoma (CSCC) and found PD-L1 positivity rates of 32.4% in tumor cells and 10.0% in immune cells.13 Furthermore, Rischin et al reported that the PD-L1 positivity rate in tumor cells was 60% for CSCC and 30.2% for adenocarcinoma.14 For immune cells, these rates reached 95.5% in CSCC and 69.8% in adenocarcinoma. In a study by Heeren et al,15 primary and matched lymph node metastatic tumor samples were collected from 96 patients with CSCC and 31 patients with adenocarcinoma. The data indicated no difference in PD-L1 positive expression in tumor cells between the primary tumors and their matched metastatic lymph nodes for either histological type. However, within the immune cells, a significantly denser PD-L1 positive expression was observed in the metastatic sites than in their matched primary tumors. Pembrolizumab is a selective humanized IgG4 kappa monoclonal antibody that targets PD-1. By blocking the interaction between PD-1 and its ligands, it restores the host antitumor immune response.16

Persistent infection with high-risk human papillomavirus (HPV) is the major etiological factor in cervical cancer and contributes to cervical carcinogenesis through viral oncoproteins such as E6 and E7, which disrupt tumor suppressor pathways and promote malignant transformation.17 HPV-related cervical cancer is also characterized by a distinct immune microenvironment, and upregulation of immune checkpoint pathways, including the PD-1/PD-L1 axis, represents an important mechanism of tumor immune evasion.18 In clinical practice, PD-L1 expression is commonly assessed using the combined positive score (CPS), which considers PD-L1 staining in both tumor cells and immune cells.19 PD-L1 CPS has become an important biomarker for identifying patients who may benefit from pembrolizumab-based therapy.19

Several trials have investigated the therapeutic potential of pembrolizumab in cervical cancer. Landmark trials have demonstrated favorable late-line efficacy and a manageable safety profile in PD-L1-positive patients with cervical cancer [23–24]. Building on these findings, Monk et al presented findings from the KEYNOTE-826 study, which positioned immune checkpoint blockade as a key component of first-line therapy by investigating pembrolizumab plus chemotherapy, with or without bevacizumab, in individuals with persistent, relapsed, or metastatic cervical carcinoma [25]. Although individual studies have reported encouraging results, the available evidence remains heterogeneous in terms of patient populations, treatment settings, PD-L1 selection criteria, therapeutic regimens, and study design. Therefore, a systematic synthesis of the current evidence is needed to clarify the overall efficacy and safety of pembrolizumab-based therapy in recurrent or advanced cervical cancer. Accordingly, we conducted this systematic review and meta-analysis to evaluate survival outcomes, tumor response, and adverse events associated with pembrolizumab monotherapy and pembrolizumab-containing combination regimens, and to explore differences according to study design, treatment strategy, and PD-L1-positive status.

Methods Literature Search

A comprehensive literature search was conducted to identify eligible studies that assessed the efficacy and safety of pembrolizumab in patients with recurrent or advanced cervical cancer. The electronic databases Web of Science, PubMed, Embase, Scopus, Cochrane Library, and Google Scholar were searched from database inception to March 2026. The search strategy included combinations of keywords related to cervical cancer and pembrolizumab, such as “cervical cancer,” “cervical carcinoma,” “pembrolizumab,” “PD-1 inhibitor,” “immune checkpoint inhibitor,” and “immunotherapy.”

Eligibility Criteria

Studies were considered eligible if they included patients diagnosed with advanced, recurrent, or metastatic cervical cancer and evaluated pembrolizumab either as a monotherapy or in combination with other therapies. Eligible studies were required to report at least one of the following outcomes: overall survival (OS), progression-free survival (PFS), tumor response outcomes, including complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), overall response rate (ORR), disease control rate (DCR), and safety outcomes, including adverse events (AEs). Studies were excluded if they were conference abstracts without available outcome data, did not meet the predefined inclusion criteria, or had duplicate reports of the same clinical study.

Data Extraction

Data extraction was performed independently by two investigators. The extracted information included the first author, publication year, study region, study design, clinical trial phase, treatment regimen, pembrolizumab dosage, sample size, and patient demographic characteristics. Data on survival outcomes, including the median OS (mOS) and median PFS (mPFS), were collected when available. The tumor response outcomes were also extracted. In addition, data on any-grade and grade 3–5 AEs were collected to evaluate the treatment safety. Discrepancies between the two investigators were resolved through discussion until a consensus was reached.

Quality Assessment

The quality of the included studies was assessed according to study design. The risk of bias of randomized controlled trials (RCTs) was evaluated using the Cochrane risk-of-bias assessment tool, and the results were summarized as a risk of bias graph and summary plot.

Prospective non-randomized studies were assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool, whereas retrospective cohort studies were evaluated using the Newcastle–Ottawa Scale (NOS).

Statistical Analysis

A meta-analysis was conducted to estimate pooled outcomes for survival, tumor response, and adverse events. Pooled estimates were calculated with the corresponding 95% confidence intervals (CIs). Statistical heterogeneity among the studies was assessed using Cochran’s Q test and the I2 statistic. When significant heterogeneity was observed (P < 0.05, or I2 > 50%), a random-effects model was applied; otherwise, a fixed-effects model was used.

Pooled analyses were performed for survival outcomes, tumor response outcomes, and safety outcomes, including both any-grade and grade 3–5 adverse events. Subgroup analyses were conducted according to study design (clinical trials vs retrospective studies) and treatment strategies (monotherapy vs combination therapy). Additional subgroup analyses were performed among PD-L1–positive patients to explore the predictive value of PD-L1 expression. All statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria), and the results are presented as forest plots.

Results Study Selection

The database search yielded a total of 466 citations. Following deduplication, 299 unique records were retained for screening titles and abstracts, of which 278 were removed. The remaining 21 articles underwent a full-text review for eligibility. Of these, 10 were excluded, comprising three conference abstracts lacking usable data and seven studies that failed to satisfy the predefined selection criteria. Ultimately, 11 studies were considered eligible and were included in the meta-analysis. The study selection process is illustrated in Figure 1.

Figure 1 Flow diagram of study selection.

Study Characteristics

The study characteristics are summarized in Table 1. Eleven studies published between 2017 and 2025 were included in this meta-analysis, comprising retrospective studies and clinical trials ranging from Phase Ib to Phase III conducted in the United States, South Korea, Belgium, and multicenter international settings. Most studies administered 200 mg pembrolizumab every 3 weeks, whereas one early study used 10 mg/kg every 2 weeks. Treatment regimens included pembrolizumab monotherapy and combination therapies with radiotherapy, chemoradiotherapy, chemotherapy with or without bevacizumab, tisotumab vedotin, and GX-188E therapeutic DNA vaccine. The sample size of pembrolizumab-treated patients ranged from 14 to 528 patients, with median ages generally between 42 and 59 years. Reported outcomes included tumor response, survival outcomes, and AEs.

Table 1 Study Characteristics of the Included Studies

Quality Assessment

The risk of bias of the included RCTs was assessed using the risk of bias assessment tool. The overall results are presented in Figure 2A (risk-of-bias graph) and Figure 2B (risk-of-bias summary). Most studies demonstrated a low risk of bias across all domains. Prospective non-randomized studies were evaluated using the MINORS. The scores ranged from 14 to 15 out of a maximum of 16, indicating a good methodological quality (Table 2). Retrospective cohort studies were assessed using NOS. The NOS scores ranged from 7 to 8 out of 9, suggesting a moderate to high methodological quality (Table 3).

Table 2 Methodological Index for Non-Randomized Studies (MINORS)

Table 3 Assessment of the Cohort Studies Using the Newcastle‑Ottawa Scale

Figure 2 Risk-of-bias assessment of RCTs using Cochrane risk-of-bias tool. (A) Risk-of-bias graph. (B) Risk-of-bias summary.

Pooled Analysis of Survival Outcomes

As substantial heterogeneity was observed (p = 0.0157), a random-effects model was employed for the pooled analysis. As shown in Figure 3, the pooled mOS after treatment was 10.82 months (95% CI:8.89–12.76) (Figure 3A). Similarly, owing to significant heterogeneity (p < 0.0001), a random-effects model was employed to estimate the mPFS. The pooled mPFS was 3.47 months (95% CI:2.61–4.34) (Figure 3B).

Figure 3 Forest plots of pooled survival outcomes in patients with recurrent or advanced cervical cancer treated with pembrolizumab. (A) Pooled analysis of mOS. (B) Pooled analysis of mPFS.

Subgroup Analysis

Subgroup analyses were subsequently conducted by categorizing the studies into clinical trials versus retrospective studies as well as monotherapy versus combination therapy (Figure 4).

Figure 4 Subgroup analyses of pooled survival outcomes according to study design and treatment strategy. (A) Subgroup analysis of mOS according to study design (clinical trials vs retrospective studies). (B) Subgroup analysis of mPFS according to study design. (C) Subgroup analysis of mOS according to treatment strategy (combination therapy vs monotherapy). (D) Subgroup analysis of mPFS according to treatment strategy.

For mOS, no difference was detected between the clinical trial subgroup and retrospective study subgroup (P = 0.3454). The pooled mOS was 11.70 months (95% CI:8.25–15.16) in clinical trials and 9.88 months (95% CI:8.36–11.41) in retrospective studies (Figure 4A). Similarly, no difference in mPFS was detected between the two subgroups (P = 0.0776). The pooled mPFS was 3.91 months (95% CI:2.64–5.18) in clinical trials and 2.71 months (95% CI:2.31–3.11) in retrospective studies (Figure 4B).

When stratified by treatment strategy, no difference in mOS was detected between the combination therapy and monotherapy groups (P = 0.2739). The pooled mOS was 12.83 months (95% CI:7.63–18.03) for combination therapy and 9.84 months (95% CI:8.54–11.13) for monotherapy (Figure 4C). However, combination therapy resulted in a significantly longer mPFS than monotherapy (P = 0.0092). The pooled mPFS was 5.59 months (95% CI:3.12–8.05) for combination therapy and 2.27 months (95% CI:1.89–2.65) for monotherapy (Figure 4D).

Tumor Response Outcomes

We further analyzed the pooled tumor response outcomes. Due to substantial heterogeneity across studies, the random-effects model was applied for all pooled analyses. The pooled results showed that the CR rate was 0.10 (95% CI:0.05–0.18; Figure 5A), PR was 0.20 (95% CI:0.12–0.31; Figure 5B), SD was 0.23 (95% CI:0.18–0.29; Figure 5C), and PD was 0.33 (95% CI:0.18–0.53; Figure 5D). The pooled ORR was 0.30 (95% CI:0.16–0.49) (Figure 5E), while the pooled DCR was 0.57 (95% CI:0.36–0.76) (Figure 5F).

Figure 5 Forest plots of pooled tumour response outcomes in patients with recurrent or advanced cervical cancer treated with pembrolizumab. Pooled analysis of CR (A), PR (B), SD (C), PD (D), ORR (E) and DCR (F).

Subgroup Analysis

Given the substantial heterogeneity observed in the overall analysis, subgroup analyses were performed using a study design (Figure 6). As shown in Figure 6A, the pooled CR rate in the clinical trial subgroup was 0.13, which was higher than that of the retrospective subgroup (0.03, P = 0.0107). Similarly, the PR rate was higher in the clinical trial subgroup (0.24) than that in the retrospective subgroup (0.08, P = 0.0036; Figure 6B). The pooled SD rate was identical between the two groups (P = 0.9922; Figure 6C). In contrast, the retrospective subgroup showed a higher proportion of patients with PD (0.58) than the clinical trial subgroup (0.27) (Figure 6D). Consistent with these findings, the pooled ORR was significantly higher in the clinical trial subgroup (0.35, 95% CI:0.19–0.55) than that in the retrospective subgroup (0.12, 95% CI:0.06–0.26; P = 0.0310; Figure 6E). Finally, the DCR was also higher in the clinical trial subgroup (0.62, 95% CI:0.38–0.82) than in the retrospective subgroup (0.34, 95% CI:0.26–0.42; Figure 6F).

Figure 6 Subgroup analyses of pooled tumour response outcomes according to study design. Subgroup analysis of CR (A), PR (B), SD (C), PD (D), ORR (E) and DCR (F) according to study design (clinical trials vs retrospective studies).

To further explore the impact of the treatment strategy on clinical efficacy, subgroup analyses were performed according to monotherapy versus combination therapy (Figure 7). As shown in Figure 7A, the CR rate was higher in the combination therapy (0.18) than that in the monotherapy group (0.04, P < 0.0001). Similarly, the PR rate was markedly higher with combination therapy (0.30) than with monotherapy (0.09, P < 0.0001; Figure 7B). The pooled SD rates were similar (combination therapy: 0.25 vs monotherapy: 0.20, P = 0.9922; Figure 7C). In contrast, the PD rate was substantially lower in the combination therapy group (0.18) than that in the monotherapy (0.58, P = 0.0053; Figure 7D). Consistent with these findings, the pooled ORR was higher in the combination therapy group (0.45, 95% CI:0.28–0.63) than in the monotherapy group (0.12, 95% CI:0.09–0.17, P < 0.0001; Figure 7E). Finally, the pooled DCR was higher in the combination therapy group (0.73, 95% CI:0.52–0.87) than in the monotherapy group (0.32, 95% CI:0.27–0.38, P = 0.0003) (Figure 7F).

Figure 7 Subgroup analysesof pooled tumour response outcomes according to treatment strategy. Subgroup analysis of CR (A), PR (B), SD (C), PD (D), ORR (E) and DCR (F) according to treatment strategy.

Survival and Tumor Response Outcomes in PD-L1–Positive Patients

To evaluate the predictive value of PD-L1 expression status, pooled analyses were conducted among PD-L1–positive patients (Supplemental Figures S1 and S2). Using a random-effects model, the pooled mOS in this subgroup was 13.22 months (95% CI:8.73–17.71; Supplemental Figure S1A), whereas the pooled mPFS was 5.74 months (95% CI:3.46–8.01; Supplemental Figure S1B). Compared to the results observed in the overall population, PD-L1–positive patients appeared to have longer survival outcomes. However, direct statistical comparisons could not be performed; therefore, the statistical significance of these differences could not be determined.

Regarding tumor response outcomes (Supplemental Figure S2), PD-L1–positive patients also demonstrated relatively favorable response rates. The pooled CR rate was 0.14 (95% CI:0.07–0.28; Supplemental Figure S2A), and PR was 0.27 (95% CI:0.15–0.43; Supplemental Figure S2B). The pooled SD rate was 0.20 (Supplemental Figure S2C); while the PD in this subgroup was 0.22 (Supplemental Figure S2D). The pooled ORR was 0.41 (95% CI:0.18–0.68; Supplemental Figure S2E), while the pooled DCR reached 0.66 (95% CI:0.32–0.89; Supplemental Figure S2F).

Any-Grade AE

The pooled incidence of any-grade AEs is summarized in Table 4. Most frequently reported AEs included anaemia with a pooled incidence of 0.30 (95% CI:0.19–0.45, I2 = 93.4%), fatigue (0.20, 95% CI:0.13–0.29, I2 = 71.8%), diarrhoea (0.19, 95% CI:0.10–0.34, I2 = 94.8%), and nausea (0.18, 95% CI:0.09–0.33, I2 = 95.5%). Other commonly reported events included vomiting (0.14; 95% CI:0.09–0.21) and hypothyroidism (0.14; 95% CI:0.10–0.20).

Table 4 Meta-Analysis of Any-Grade AE

Moderate incidences of anorexia (0.11, 95% CI:0.06–0.18) and pyrexia (0.10, 95% CI:0.04–0.22). Lower incidences were reported for rash (maculo-papular) (0.08, 95% CI:0.04–0.17), hyperthyroidism (0.07, 95% CI:0.05–0.12), asthenia (0.06, 95% CI:0.04–0.10), and pruritus (0.06, 95% CI:0.03–0.11).

Less frequent AEs included AST elevation (0.05, 95% CI:0.03–0.09), abdominal pain (0.04, 95% CI:0.02–0.08), dry mouth (0.04, 95% CI:0.02–0.09), constipation (0.04, 95% CI:0.01–0.12), colitis (0.04, 95% CI:0.02–0.07), and arthralgia (0.03, 95% CI:0.01–0.16). Substantial heterogeneity was observed for several adverse events, including diarrhea, anemia, and nausea. Forest plots of the meta-analysis for any-grade AEs are presented in Supplemental Figures S3 and S4.

Grade 3–5 AEs

The pooled incidence of grade 3–5 AEs is summarized in Table 5. Among the reported severe toxicities, anemia was the most frequent, with a pooled incidence of 0.17 (95% CI:0.12–0.25, I2 = 73.9%). Severe diarrhea and rash (maculopapular) occurred at a pooled incidence of 0.03 (95% CI:0.02–0.05) and 0.03 (95% CI:0.01–0.07), respectively. Other grade 3–5 toxicities were relatively uncommon, including nausea, fatigue, and vomiting, with a pooled incidence of approximately 0.02. Similarly, severe AST elevation and colitis were observed, with a pooled incidences of 0.02. Lower incidences of asthenia, arthralgia, constipation, anorexia, hypothyroidism, pyrexia, pruritus, and dry mouth were observed, with pooled incidences of approximately 0.01, while abdominal pain and hyperthyroidism were rarely observed. Overall, most severe AEs occurred at low frequencies, although moderate heterogeneity was observed for some events, particularly anemia, vomiting, and colitis. Forest plots of the meta-analysis for grade 3–5 AEs are presented in Supplemental Figures S5 and S6.

Table 5 Meta-Analysis of Grade 3 AE

Discussion

This study systematically confirmed the significant clinical benefits of PD-1/PD-L1 inhibitors for treating recurrent or advanced cervical cancer through a pooled analysis of multi-dimensional evidence, including RCTs, single-arm Phase II trials, and retrospective studies. Our findings indicated that the pooled mOS for the entire population reached 10.82 months, with an mPFS of 3.47 months. Regarding safety, the most common any-grade AEs included anemia, fatigue, diarrhea, nausea, and vomiting, whereas grade 3–5 AEs occurred at relatively low frequencies. Although the overall analysis exhibited high heterogeneity due to differences in study designs and enrolled populations, an in-depth analysis based on literature types revealed distinct performance variations and evolutionary trends of immunotherapy across different clinical scenarios.

Among all included studies, RCTs represented by KEYNOTE-82627 provided the highest level of evidence-based medical data. The results of the study demonstrated that the addition of pembrolizumab to standard chemotherapy significantly extended patient survival. In our meta-analysis, the superior efficacy observed in the “combination therapy group” (mPFS of 5.59 months and ORR of 0.45) was highly consistent with the findings of these RCTs. This suggests that immune checkpoint inhibitors, through their synergistic effects with chemotherapy or anti-angiogenic agents, can effectively overcome the suppressive state of the tumor immune microenvironment. This synergy has successfully shifted the treatment paradigm for advanced cervical cancer from conventional chemotherapy to the era of “immunotherapy-plus” combination regimens.

Single-arm clinical trials, such as KEYNOTE-15822 and a study by Youn et al30 on DNA vaccine-immunotherapy combinations provide vital references for the application of immune monotherapy or innovative combination regimens in second-line and subsequent lines of treatment. KEYNOTE-158 demonstrated a durable response to pembrolizumab in PD-L1-positive patients (with an ORR of approximately 14.6%), which is consistent with the higher ORR observed in the “Clinical Study” group in our subgroup analysis (0.35). Furthermore, Youn et al explored combination regimens targeting HPV-specific antigens, further proving that under rigorous monitoring of clinical trial environments, screening for specific biomarkers (such as PD-L1 positivity or HPV genotypes) can significantly enhance the disease control rate and the depth of response.

In contrast to clinical trials conducted under ideal conditions, the included retrospective studies reflect the performance of immunotherapy in real-world clinical practice. Our subgroup analysis revealed that the pooled ORR of 0.12 and DCR of 0.34 in the retrospective study group were lower than those in clinical trial group.21,28 This discrepancy may stem from more complex prior treatment histories, poorer Performance Status, or a higher proportion of distant metastases among real-world patients. Studies such as that by Alholm et al also noted high treatment drop-off rates in second-line therapy and subsequent lines of therapy, which explains why the retrospective subgroup recorded a higher proportion of PD at 0.58.20 This finding suggests that the heterogeneity of real-world patients must be fully considered when extrapolating conclusions from clinical trials.

The heterogeneity observed in our meta-analysis essentially reflects the broad clinical spectrum, spanning from the rigorous standardization of RCTs to the multifaceted diversity of retrospective real-world studies. A critical factor contributing to this variance was the inconsistent application of biomarker-driven patient selection across the included literature. Our analysis of the PD-L1 positive subgroup provides compelling evidence of the predictive value of this biomarker. Specifically, patients with confirmed PD-L1 expression demonstrated prolonged survival compared with the total population. This cohort also exhibited a superior tumor response. These findings are strongly supported by the landmark KEYNOTE-158 trial, which established that pembrolizumab monotherapy is primarily effective in patients with a Combined Positive Score (CPS) ≥1. Additionally, recent evidence from the EMPOWER-Cervical 1 trial (Cemiplimab)31,32 and KEYNOTE-826 study (pembrolizumab combination) has reinforced that while immunotherapy can provide broad benefits, the magnitude of survival gain is often correlated with the degree of PD-L1 expression. The discrepancy between the “ideal” outcomes in biomarker-selected clinical trials and the more modest results in unselected retrospective cohorts underscores that PD-L1 status remains the most reliable tool for optimizing treatment decisions. By focusing on PD-L1 positive populations, clinicians can better identify patients who are likely to achieve deep and durable responses, such as the 0.14 CR rate observed in our PD-L1 subgroup that defines the success of modern immunotherapy.

Regarding safety, PD-1/PD-L1 inhibitors were well tolerated and characterized by manageable low-grade toxicities such as anemia and fatigue. Grade 3–5 AEs were generally uncommon (around 2%), except for anemia (17%), a trend also noted in the CLAP trial (camrelizumab plus apatinib) regarding the synergistic toxicity of combination regimens.33 Immune-related events, particularly hypothyroidism and hyperthyroidism, mirrored safety signals from KEYNOTE-826 but remained clinically manageable. Despite the higher heterogeneity in real-world cohorts, the favorable benefit-risk profile supports PD-1/PD-L1 inhibition as a core strategy for managing advanced cervical cancer.

This study has several limitations. Firstly, although the pooled results suggest that pembrolizumab-based therapy is associated with clinically meaningful antitumor activity in recurrent or advanced cervical cancer, these findings should be interpreted cautiously because substantial heterogeneity was observed across included studies. Differences in study design, treatment line, PD-L1 selection criteria, prior systemic therapy, and use of pembrolizumab as monotherapy versus combination therapy may have contributed to variability in survival and response outcomes. Therefore, the pooled estimates should not be interpreted as direct evidence that all pembrolizumab-based regimens provide equivalent benefit across all patient populations. Secondly, publication bias could not be robustly assessed for all outcomes because pooled analyses included a limited number of studies. Therefore, funnel plot interpretation and Egger’s test had limited statistical power. Thirdly, although outcomes in PD-L1-positive patients were summarized, direct statistical comparison between the PD-L1-positive subgroup and the overall population was not performed because individual patient-level data were unavailable and the subgroup was not independent from the total study population. Fourthly, the lack of individual patient data prevented formal statistical survival comparison for the PD-L1 subgroup. Finally, the long-term safety of newer combination regimens remains unclear.

Conclusion

Pembrolizumab demonstrated promising efficacy and a manageable safety profile in patients with recurrent or advanced cervical cancer. Combination therapy appears to provide superior clinical benefits compared with monotherapy. However, the interpretation of pooled outcomes should be cautious due to heterogeneity in study design, treatment line, patient selection, and therapeutic protocols. PD-L1 testing, particularly assessment using the combined positive score, remains important for identifying patients most likely to benefit from pembrolizumab-based therapy and should be integrated into clinical decision-making where applicable. Future prospective studies should further clarify optimal patient selection, treatment-line positioning, combination strategies, and the management of immune-related adverse events.

Data Sharing Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author (Yan Deng) upon reasonable request.

Ethics Approval and Consent to Participate

Not applicable. As this study was a systematic review and meta-analysis of previously published studies, no ethical approval or informed consent was required.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by Guangxi Natural Science Foundation (Grant No. 2026GXNSFAA00640502), Guangxi First Batch of Medical Young Reserve Talents Training Project (Gui Wei Ren Fa [2025] No. 5), Guangxi Young Elite Scientist Sponsorship Program(GXYESS2025220)and Open Competition Project of Guangxi Plateau Discipline (2026), Youjiang Medical University for Nationalities (YY2026GYZ04).

Disclosure

The authors declare that they have no competing interests.

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