Efficacy of oncolytic virus in the treatment of intermediate-to-advanced solid tumors: a systematic review and meta-analysis.

INTRODUCTION

Malignant tumors have become the second leading cause of morbidity and mortality worldwide, with increasing incidence and impact each year ( 1 ). The primary treatments for malignant tumors, including surgical resection, chemotherapy, and radiotherapy, have shown significant benefits for patients in the early stages of the disease. However, the presence of self-protective mechanisms, such as apoptosis resistance and immune evasion, poses challenges in achieving ideal outcomes for patients with advanced malignant tumors ( 1 ). Therefore, there is an imperative to explore new and effective treatment methods for patients with intermediate-to-advanced tumors.

Oncolytic viruses (OVs) are a group of viruses that occur naturally or have been genetically engineered to selectively infect and destroy malignant tumor cells while minimizing side effects on normal cells ( 2 ). Common oncolytic virus vectors can be categorized based on their nucleic acid types. These include double-stranded DNA viruses such as adenovirus and herpes simplex virus, double-stranded RNA viruses like reovirus, positive-stranded RNA viruses such as Sacchi virus, and negative-stranded RNA viruses including the measles virus ( 3 ). Oncolytic viruses can be classified into two types based on their origin: natural virus strains and genetically modified strains. Natural virus strains have the ability to specifically target tumor cells and selectively replicate within them. Examples of natural virus strains used in oncolytic virotherapy include Newcastle disease virus, myxoma virus, and reovirus ( 4 ). The other type is genetically engineered virus strains, which are modified to enhance their ability to target tumor cells. This is achieved by modifying the genes responsible for viral replication in tumor cells and deleting or inactivating key genes involved in viral replication in normal cells. Examples of genetically engineered virus strains used in oncolytic virotherapy include herpes simplex virus, measles virus, adenovirus, and others ( 4 ). Currently, there are three OV products approved for market worldwide. RIGVIR, which is a human enterocytopathic orphan virus, is indicated for the treatment of melanoma, colorectal cancer, and other malignancies. Oncorine, an adenovirus-based OV, is used for the treatment of head and neck cancer. T-VEC is another approved OV indicated for the treatment of advanced melanoma. Based on statistics from clinical trials, the viral vectors used in the development of OV drugs worldwide primarily fall into four categories. Adenovirus-based vectors constitute the largest portion with 30 trials (30.9%), followed by herpes simplex virus with 23 trials (23.7%), reovirus with 19 trials (19.6%), and vaccinia virus with 12 trials (12.4%) ( 5 ).

With the in-depth study of the anti-tumor mechanism of OVs, people found four main mechanisms. First, OVs can directly cleave tumor cells. Because of the defects in the antiviral response of malignant tumor cells, OVs can specifically infect and replicate in tumor cells, leading to the direct lysis of tumor cells; therefore, through in-depth research on the anti-tumor mechanisms of OVs, scientists have identified four main mechanisms. First, OVs can directly lyse tumor cells. Due to the impaired antiviral response of malignant tumor cells, OVs have the ability to selectively infect and replicate within tumor cells, ultimately resulting in the direct lysis of the tumor cells ( 6 ). Second, OVs can induce various forms of immunogenic cell death by triggering the endoplasmic reticulum stress response. This includes necrosis, necrotic apoptosis, immune apoptosis, pyroptosis, and autophagy, which can further stimulate an immune response against the tumor ( 7 ). Third, certain OVs, such as herpes simplex virus, vaccinia virus, and vesicular stomatitis virus, have shown the ability to target tumor stromal cells, including tumor-associated fibroblasts, vascular endothelial cells, and pericytes. This targeted infection disrupts the complex tumor structure, leading to tumor necrosis and facilitating the infiltration of immune cells into the tumor microenvironment (TME) ( 8 ). Finally, OVs can enhance tumor antigen presentation and immune responses within the TME by inducing antiviral and inflammatory reactions, as well as the production of cytokines (e.g., GM-CSF) and the expression of costimulatory molecules (e.g., TNF-associated activation protein). These immune-stimulating effects result in increased immunogenicity within the TME, breaking immune tolerance and transforming "cold" tumors into “hot” tumors that are more susceptible to immune-mediated destruction ( 9 , 10 ).

The therapeutic effect of OVs in the treatment of advanced solid tumors can vary. For example, in the treatment of unresectable stage IIIB through IV melanoma, Franke et al. ( 11 ) reported a best overall response rate (ORR) of 88.5% with T-VEC, whereas Perez et al. ( 12 ) reported the best ORR of 58.6% among their patients. Therefore, in order to comprehensively evaluate the efficacy of OV therapy for solid tumors, we have summarized the findings from published prospective clinical trials and real-world (non-randomized) cohort studies. The purpose of this study was to provide a comprehensive assessment of the current state of OV therapy in the treatment of solid tumors. By employing Response Evaluation Criteria in Solid Tumors (RECIST) and evaluating parameters such as CR, PR, SD, PD, ORR, DRR, OS, and PFS, we aim to shed light on the effectiveness of OVs in this therapeutic approach. This analysis guides us to delve deeper into the topic and explore the outcomes of OV therapy for solid tumors.

MATERIALS AND METHODS

Search strategy

Two authors conducted a comprehensive search of relevant articles in PubMed (MEDLINE), Cochrane, and Embase databases up until March 6, 2025. The detailed search strategies can be found in Table S1 . The search utilized keywords such as “oncolytic virus,” “oncolytic virotherapy,” and “solid tumor.” To ensure accuracy, a combination of machine and manual deduplication checks were performed, and articles that were not accessible in their original full-text form were excluded. The search was restricted to English-language articles. Additionally, after conducting a thorough analysis and drafting the manuscript, a subsequent literature search was performed to include any newly published studies. Furthermore, we compared the literature included in previous meta-analyses and incorporated any relevant studies that were missing. It is important to note that this meta-analysis has been registered in PROSPERO (CRD42022380652).

Inclusion criteria

All studies were included based on the PICOS (patients, interventions, comparators, outcomes, and study design) criteria.

Patients: Individuals diagnosed with intermediate and advanced solid tumors, irrespective of nationality, gender, or race.

Interventions: Patients who received single-agent therapy or combination therapy with oncolytic viruses (OVs).

Comparators: Patients who received treatments other than OV therapy.

Outcomes: The studies should report at least one of the following outcomes: number of complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD); overall response rate (ORR), durable response rate (DRR), overall survival (OS), and progression-free survival (PFS).

Study design: Retrospective studies or clinical trials.

Exclusion criteria

Unpublished studies.

Duplicated data.

Patients who did not receive OV treatment for the first time.

We have included the inclusion/exclusion criteria in Table S2 .

Data extraction and quality assessment

The two authors (Sun and Liu) independently extracted relevant information from the included studies, such as author, year, study type, cancer type, type of OVs, gender, age, outcomes, and more. The quality assessment of the included studies was conducted independently by two authors (Liu and He) using established assessment scales, including the Newcastle–Ottawa Scale (NOS), Jadad scale, and Minors scale. The Newcastle–Ottawa Scale was utilized to assess the quality of retrospective studies. It consists of three major sections (selection, comparability, and assessment of outcome) and nine subsections. A total score of more than 7 indicates good quality. The Jadad scale was employed to evaluate the quality of clinical trials (excluding single-arm clinical trials). It comprises four parts: randomization, allocation concealment, double-blinding, and withdrawals and dropouts. A score higher than 4 is considered indicative of good quality. The Minors scale was used for single-arm clinical trials and includes eight sections. A score higher than 7 is considered indicative of good quality. Detailed scores for each study can be found in Table S3 (Jadad), Table S4 (Minors), and Table S5 (NOS) in the supplementary material.

Statistical analysis

The meta-analysis was performed using the “meta” package in R v4.0.0 ( 13 ). We pooled effect size as odds ratio (OR) and hazard ratio (HR) with 95%CI. The effect sizes were pooled as odds ratios (OR) and hazard ratios (HR) with corresponding 95% confidence intervals (CI). Heterogeneity among the included studies was assessed using the standard Cochrane’s Q test and I 2 statistics. A value of I 2 >50%, and P < 0.1 indicated significant heterogeneity. In cases where substantial heterogeneity was observed, a random-effects model was employed. Conversely, a fixed-effects model was used when heterogeneity was not significant. Sensitivity analysis and cumulative meta-analysis were conducted to explore the potential sources of heterogeneity. Additionally, subgroup analysis was performed based on virus species, country, type of OVs, study type, tumor type, and other relevant factors to identify potential sources of heterogeneity. We also utilized L'Abbé plots and Galbraith plots to illustrate the heterogeneity among the studies. Begg test ( 14 ), Egger test ( 15 ), trim-and-fill method, and funnel plot were used to estimate the publication bias.

RESULTS

Basic information

A total of 21,293 publications were identified through the literature search in PubMed (MEDLINE), Cochrane, and Embase databases up until March 6, 2025. The study selection process was carried out in accordance with the PRISMA flowchart, as shown in Fig. 1 . Duplicate articles were removed using automated tools and manual screening. After a thorough review of titles, abstracts, and full-text articles, a total of 87 studies involving 5,385 patients were included in the final analysis ( Fig. 1 ). In particular, the study conducted by Franke et al. ( 16 ) was excluded from the analysis because the patient had previously received OV treatment and achieved a CR.

Among the included studies, 73 were clinical trials and 12 were retrospective studies. These studies focused on various types of solid tumors, including 28 studies on melanoma, 9 on hepatocellular carcinoma (HCC), 7 on pancreatic cancer, 4 on lung cancer, 3 on head and neck cancer, and 36 on other tumor types. The basic characteristics of these studies are presented in Table 1 (clinical trials) and Table 2 (retrospective studies). The NOS scores ranged from 7 to 9 for the retrospective studies, indicating good quality. The Jadad scores remained at 4 for the clinical trials, representing good quality as well. The Minor’s scores ranged from 9 to 10, indicating good quality for the included single-arm clinical trials. Furthermore, a total of 32 different OVs were investigated across the included studies.

Efficacy of OVs

A total of 66 studies reported the number of patients with CR, PR, SD, or PD. The analysis revealed significant heterogeneity among these studies ( I 2 = 90%, Fig. 2A ; I 2 = 91%, Fig. 2B ; I 2 = 88%, Fig. 2C ; I 2 = 91%, Fig. 2D ; P < 0.01), thus the random effects model was employed. Overall, the results showed that 11% of patients achieved CR (95% CI = 7%–14%), 18% achieved PR (95% CI = 14%–22%), 32% achieved SD (95% CI = 27%–37%), and 32% experienced PD (95% CI = 26.3%–37.4%).

We also conducted calculations for the ORR and DRR of patients receiving OV treatment. In the random effects model, the ORR was determined to be 29% (95% CI = 23%–35%), whereas the DRR was found to be 39% (95% CI = 24%–56%), as shown in Fig. 3 . In the subgroup analysis of ORR, it was observed that patients treated with herpes simplex virus (HSV) and Coxsackievirus had a higher ORR (36%, 95%CI = 26%–47%; 39%, 95%CI = 7%–78%) compared with other virus species. Additionally, T-VEC and H101 demonstrated a higher ORR (41%, 95%CI = 30%–53%; 46%, 95%CI = 12%–82%) compared with other types of oncolytic viruses ( Fig. 4 ). Moreover, the odds ratio (OR) of ORR comparing OV treatment with other non-OV treatments was calculated and found to be 1.62 (95% CI = 1.14-2.31), indicating a favorable effect of oncolytic virus treatment ( Fig. 5A ). Significant heterogeneity ( I 2 = 55%) was observed among the included studies. Subgroup analysis was conducted based on virus species, country, type of OVs, study type, and cancer type to explore potential sources of heterogeneity. In Fig. 5B , the subgroup analysis revealed a surprisingly favorable effect of T-VEC treatment (OR = 3.58, 95% CI = 1.94–6.59). However, it is important to note that only three studies were included in this subgroup, and thus, further research is needed to validate this result. Similarly, the subgroup analysis for melanoma also showed a favorable result (OR = 2.50, 95%CI = 1.04–6.00). Furthermore, we constructed univariate meta-regression models using publication year, age, and proportion of males. However, the results did not reveal any significant associations between publication year ( P = 0.2385), age ( P = 0.0811), proportion of males ( P = 0.3429), and ORR. Similarly, no significant associations were found between publication year ( P = 0.7920), age ( P = 0.3587), proportion of males ( P = 0.4570), and the odds ratio of ORR ( Fig. S1a-f and Table S6 ).

A total of 14 studies provided HR for OS, whereas 11 studies provided HR for PFS, comparing patients treated with OVs with those treated with other therapies. The meta-analysis indicated that the treatment of OVs did not have a significant effect on prolonging the overall survival of patients (HR = 0.86, 95%CI = 0.74–1.01, Fig. 6A ). Subgroup analysis results are presented in Fig. 6B . However, as shown in Fig. 6C , the effect of OVs on PFS had statistical significance (HR = 0.88, 95%CI = 0.78–0.99). Furthermore, in Fig. S2c and f , a positive relationship was observed between patients' age and OS ( P = 0.0008) as well as PFS ( P = 0.0747). This suggests that older individuals have a higher risk of death, and oncolytic viruses may potentially have better therapeutic effects on elderly patients.

Sensitivity analysis, cumulative meta-analysis, and publication bias

Given the significant heterogeneity observed among the included studies, sensitivity analysis and cumulative meta-analysis were conducted to assess the impact of each study on the overall results. Sensitivity analysis involved omitting or adding each study one by one to determine their influence on the overall outcome. Additionally, funnel plots were constructed to assess publication bias, and the trim-and-fill method, L'Abbé plot, and Galbraith plot were utilized for further evaluation. The results of these analyses, including sensitivity analysis, cumulative meta-analysis, and assessments of publication bias, are presented in the supplemental files for ORR ( Fig. S4 ), DRR ( Fig. S4g ), odds ratio of ORR ( Fig. S5 ), overall survival ( Fig. S6 ), and progression-free survival ( Fig. S7 ).

After conducting sensitivity analysis and cumulative meta-analysis, all the included studies demonstrated good stability, as shown in Fig. S4a,b, S5a,b, S6a,b and S7a,b . However, the funnel plots for CR, PR, SD, and PD did not exhibit ideal symmetry ( Fig. S3 ), and similar findings were observed for ORR, DRR, odds ratio of ORR, and PFS ( Fig. S4d , Fig. 4G , Fig. S5d and S7c ). To address potential publication bias, we used the trim-and-fill method to fill in missing studies. For the ORR analysis, 25 missing publications were filled, resulting in a revised ORR of 45% (95% CI = 37%-53%) ( Fig. S4c and e ), indicating that the missing literature weakened the impact of oncolytic viruses on ORR. In the case of the odds ratio of ORR, three missing publications were filled, yielding a revised odds ratio of 1.90 (95% CI = 1.32–2.73) ( Fig. S5c and e ), suggesting that the three missing articles also weakened the impact of oncolytic viruses on the odds ratio of ORR. When analyzing PFS, two missing publications were filled, resulting in a revised hazard ratio of 0.83 (95% CI = 0.68–1.02) ( Fig. S7d and e ), indicating that the missing studies did not significantly affect the overall result. Furthermore, the funnel plot for overall survival (OS) demonstrated good symmetry, with only a few publications lying outside the dashed line ( Fig. S6c ). No missing articles were filled using the trim-and-fill method ( Fig. S6d and e ). The Galbraith plot indicated low publication bias ( Fig. S4f, S5f,g, S6f, and S7f ), and the L'Abbé plot indicated a general agreement among the majority of articles regarding the positive effect of oncolytic viruses in treating intermediate-to-advanced solid tumors ( Fig. S5g ).

DISCUSSION

Malignant tumors pose a significant threat to human health and life. Traditional treatments, including surgery, radiotherapy, and chemotherapy, have limitations such as incurability and serious side effects. In recent years, tumor immunotherapy has witnessed significant advancements. By reactivating and sustaining the tumor-immune cycle, it activates the body’s natural anti-tumor immune response, aiming to control and eliminate tumors ( 102 ). Currently, anti-tumor immunotherapies encompass various approaches, including immune checkpoint inhibitors, therapeutic antibodies, cancer vaccines, and cell-based therapies ( 102 ). Among these approaches, OV therapy holds a prominent position within tumor immunotherapy and has found extensive application in the field of cancer treatment. OV therapy primarily targets solid tumors, such as melanoma, hepatocellular carcinoma, colon cancer, breast cancer, glioma, multiple myeloma, head and neck cancer, and malignant pleural mesothelioma ( 5 ). In 2005, China made a significant milestone in the field of oncolytic virus therapy by approving the use of recombinant human adenovirus 5 (H101) in combination with chemotherapy for patients with advanced nasopharyngeal carcinoma. This approval marked the first clinical application of an oncolytic virus product worldwide ( 103 ). Andtbacka et al. ( 48 ) conducted a phase I clinical trial evaluating the use of Coxsackie virus V937 in the treatment of advanced melanoma. The study reported promising results, with a DRR of 21.1%. Additionally, the 12-month PFS and OS rates reached 32.9% and 75.4%, respectively. These findings suggest the potential effectiveness of Coxsackie virus V937 in improving outcomes for patients with advanced melanoma. Similarly, in a phase I clinical trial evaluating the use of OV monotherapy for patients with unresectable melanoma, the ORR reached 33%. These results demonstrate the significant efficacy of OV treatment as a standalone therapy. However, a phase IIb clinical trial conducted in 2019 evaluating the use of JX-594 showed no significant differences in median OS, total RR, and time to progression (TTP) between the experimental group receiving Pexa-Vec in combination with best supportive care and the control group receiving only best supportive care ( 36 ). Therefore, conducting a meta-analysis is crucial to summarize and obtain meaningful results from relevant clinical studies at a statistical level. Upon literature search, it was found that only one meta-analysis on the use of oncolytic virus therapy for intermediate and advanced solid tumors had been published in 2021 ( 104 ). After reviewing the existing literature, it became evident that the previous meta-analysis focused more on statistical analysis of research and virus design but did not further summarize and analyze the survival information of patients. As this is a highly active and emerging field, there has been a large number of new clinical studies published recently, and many clinical studies were not included in the previous meta-analysis ( 5 , 105 ). Therefore, it is necessary to include these missed and newly published studies to provide an updated analysis of the results.

In this meta-analysis, a total of 87 studies were included to explore the effectiveness of OVs in treating patients with intermediate-to-advanced solid tumors. We found that 11% of patients achieved CR (95%CI = 7%–14%), 18% achieved PR (95%CI = 14%–22%), 32% achieved SD (95%CI = 27%–37%), and 32% experienced PD (95%CI = 26.3%–37.4%) after OV treatment. The ORR of patients was 29% (95% CI = 23%–35%), and the DRR was 39% (95% CI = 24%–56%). The OR of ORR was 1.62 (95% CI = 1.14–2.31). Besides, the HR of OS for patients was 0.86 (95% CI = 0.74–1.01), and the PFS was HR = 0.88 (95% CI = 0.78–0.99). However, in the subgroup analysis, it was found that the ORR after T-VEC monotherapy and combined therapy reached 41% (95% CI = 30%–53%), indicating promising therapeutic potential. The higher ORR observed in T-VEC monotherapy and combined therapy could be attributed to the fact that T-VEC is one of the first oncolytic viruses approved by the FDA for treatment ( 106 ). It is worth noting that the observed heterogeneity in efficacy across different viruses and cancer types may partly stem from differences in sample size among cancer types. Additionally, the fact that T-VEC has been approved by the FDA may lead to greater confidence in its efficacy in clinical trials,, which may contribute to the observed therapeutic efficacy. T-VEC, belonging to herpes simplex virus type 1 (HSV-1), has been found to induce the release of high mobility group box 1 (HMGB1) protein and calmodulin in human melanoma cells. In addition, it has been shown to promote the proliferation of melanoma tumor antigen in mouse models with melanoma. The injection of T-VEC can selectively induce immunogenic cell death (ICD) of tumor cells, leading to the release of tumor-associated antigens upon tumor cell lysis and triggering a T-cell-mediated immune response ( 107 ). Furthermore, in the subgroup analysis, we observed that the ORR among melanoma patients was notably high, reaching 43% (95% CI = 33%–53%). This finding may be attributed to the high immunogenicity of melanoma and its increased sensitivity to immunotherapy, rendering it more responsive to treatment compared to other types of solid tumors ( 108 ). It is worth noting that Erin et al. reported that the OS and PFS of patients treated with NTX-010 in SCLC did not seem to be better than those of the placebo group, although the difference was not statistically significant. They also found that viremia was associated with poor prognosis, suggesting that treatment failure might be due to viral clearance ( 7 ).

Although OVs have shown clinical benefits in various types and stages of solid tumors, there are still challenges when using OVs as a standalone therapy. The use of OV monotherapy can mitigate issues related to biosafety, complexity, and costs since only one drug needs to be produced and administered. However, to achieve optimal therapeutic effects, OVs must be present in the body for a sufficient duration. Unfortunately, the host immune system can clear the virus from circulation, limiting its presence to a short period. As a result, OV monotherapy may only yield low-to-moderate efficacy ( 109 ). To overcome the limitations of single-drug OV treatment, researchers have explored combination therapies, such as radiotherapy, chemotherapy, immune checkpoint inhibitors, CAR-T cell treatments, etc. ( 106 ). In a phase II trial targeting recurrent head and neck cancer, a combination therapy involving a modified oncolytic adenovirus ONYX-015, cisplatin, and 5-fluorouracil (5-FU) demonstrated an enhanced anti-tumor effect. The effective rate of the combined therapy was 63% in patients receiving the combination (OVs + cisplatin + 5FU), whereas the effective rate was only 15% in patients receiving ONYX-015 alone. This highlights the potential of combining OVs with traditional chemotherapy agents to achieve improved treatment outcomes ( 110 ). CAR-T cell therapy utilizes genetically modified T cells to express CARs, enabling them to recognize and eliminate tumor cells with specific antigens. When combined with gene-modified OVs, CAR-T cell therapy exhibits enhanced efficacy by promoting CAR-T cell infiltration into the TME and improving their anti-tumor activity against solid tumors. This combination approach holds great promise for improving the therapeutic outcomes of CAR-T cell therapy in the treatment of solid tumors ( 111 ). A preclinical study combining oncolytic adenovirus Ad-mTNFα-mIL2 (expressing TNF-α and IL-2) with CAR-T cells targeting mesothelin showed significant tumor attenuation in pancreatic ductal adenocarcinoma models ( 112 ).

This meta-analysis has several limitations. First, heterogeneity exists due to variations in treatment methods between the experimental and control groups, which could have been addressed through network meta-analysis. Second, the objective response rate could not be calculated due to incomplete data in some articles, limiting the synthesis of results. Third, the safety evaluation of oncolytic viruses was not performed, which is crucial for patients and clinicians. Future studies will include the analysis of side effects. Fourth, some studies provided survival curves without HR values for OS and PFS, leading to the exclusion of survival information. This decision was made to avoid errors associated with the artificial extraction of HR values from survival curves, as outlined by Tierney ( 113 ). However, it is important to note that this exclusion may have influenced the results of our analysis. Finally, access to full-text articles may have been restricted, limiting their inclusion and analysis. Although these limitations bring some uncertainties, we believe that their impact on the main conclusions is limited. In future studies, methods such as network meta-analysis and the inclusion of more outcome measures can be adopted to make the research more comprehensive and meticulous.

Conclusion

In conclusion, this meta-analysis reveals that OVs demonstrate a significant therapeutic effect in the treatment of solid tumors. Specifically, T-VEC has shown to be the most effective type of OV currently. However, no significant association was found between OVs and OS or PFS in patients. Future studies should focus on investigating the side effects of OVs. Furthermore, we encourage more research centers to participate in clinical trials of OVs to generate additional evidence regarding their therapeutic efficacy.

ACKNOWLEDGMENTS

We thank all R programming package developers.

Jinzhou Xu, Jianxuan Sun, Chenqian Liu, Jia Hu, Shaogang Wang, and Qidong Xia contributed to developing the main research question, conducting the literature search, and collecting the included studies’ information. Jinzhou Xu, Jianxuan Sun, and Chenqian Liu performed the meta-analysis and wrote the draft of the manuscript. Jinzhou Xu, Sihan Zhang, Ye An, and Mengyao Xu contributed to developing the main research question. Jinzhou Xu, Ruoyan Pan, Xingyu Zhong, Na Zeng, Siyang Ma, and Haodong He revised the manuscript. All authors contributed to the article and approved the submitted version. Jianxuan Sun, Chenqian Liu, Jia Hu, Shaogang Wang, and Qidong Xia contributed equally to this study.

Contributor Information

Shao-Gang Wang, Email: sgwangtjm@163.com.

Qi-Dong Xia, Email: qidongxia_md@163.com.

Koenraad van Doorslaer, College of Agriculture & Life Sciences, University of Arizona, Tucson, Arizona, USA.

DATA AVAILABILITY

All the original data were collected from online databases.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/jvi.00640-25 .

jvi.00640-25-s0001.docx

Figures S1 to S7.

jvi.00640-25-s0002.docx

Details of search strategy.

jvi.00640-25-s0003.docx

Inclusion/exclusion criteria.

jvi.00640-25-s0004.docx

JADAD scale for assessing the quality of clinical trials.

jvi.00640-25-s0005.docx

Minors scale for assessing the quality of single-arm clinical trials.

jvi.00640-25-s0006.doc

Newcastle-Ottawa scale (NOS) for assessing the quality of cohort studies.

jvi.00640-25-s0007.docx

Meta-regression analysis of factors affecting heterogeneity for the association between OVs use and cancer risk.

ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.

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Associated Data

Supplementary Materials

jvi.00640-25-s0001.docx

Figures S1 to S7.

jvi.00640-25-s0002.docx

Details of search strategy.

jvi.00640-25-s0003.docx

Inclusion/exclusion criteria.

jvi.00640-25-s0004.docx

JADAD scale for assessing the quality of clinical trials.

jvi.00640-25-s0005.docx

Minors scale for assessing the quality of single-arm clinical trials.

jvi.00640-25-s0006.doc

Newcastle-Ottawa scale (NOS) for assessing the quality of cohort studies.

jvi.00640-25-s0007.docx

Meta-regression analysis of factors affecting heterogeneity for the association between OVs use and cancer risk.

Data Availability Statement

All the original data were collected from online databases.