Long Sui

The causal effect of cytokine cycling levels on the risk of cervical cancer: A bidirectional 2-sample Mendelian randomization study

Increasing evidence suggests that inflammatory protein factors are closely associated with the underlying mechanisms of cervical cancer. Therefore, 2-sample Mendelian randomization (MR) analysis was performed to assess the potential correlation between circulating inflammatory protein levels and cervical cancer risk. A 2-sample MR study, using genetic variants related to inflammatory proteins as instrumental variables, was conducted to improve the accuracy of cervical cancer diagnosis. By analyzing 14,824 individuals, 91 plasma proteins having strong association with single nucleotide polymorphisms were chosen as instrumental variables, with cervical cancer (909 cases and 238,249 controls) serving as outcome variables. The analysis of causal effects was completed using random effect inverse variance weighted, weighted median/mode, and MR-Egger. Sensitivity analysis was performed using Cochran Q test, funnel plots, leave-one-out analyses, MR-Egger intercept tests, as well as reverse MR analysis. Our analysis showed that C-C motif chemokine ligand 19 (CCL19), monocyte chemotactic protein-3 (MCP-3), and interleukin-12 (IL-12) was related to the risk of cervical cancer. Additionally, the inverse variance weighted method indicated that both CCL19 (OR: 1.479, 95% CI: 1.207–1.813, P  = .0002) and IL-12 (OR: 1.171, 95% CI: 1.019–1.345, P  = .0253) significantly increased the risk of cervical cancer. Nevertheless, MCP-3 levels may protect individuals from developing cervical cancer (OR, 0.647; 95% CI: 0.442–0.947, P  = .0253). Furthermore, consistent outcomes were achieved in the sensitivity analysis. In our study, MR analysis of 91 inflammatory proteins revealed potential causal associations between CCL19, MCP-3, IL-12, and the etiology of cervical cancer. We believe that related inflammatory proteins will provide potential treatment opportunities for clinical interventions in cervical cancer.

Patterns of Co‐infection of HPV52 With Other HPV Genotypes and Their Risks of Cervical Precancer and Carcinoma

ABSTRACT Human papillomavirus 52 (HPV52) is the second most frequent HPV type in high‐grade squamous intraepithelial lesion (HSIL) cases in China. However, few researchers have explored the co‐infection of HPV52 with other HPV genotypes and their correlation with cervical lesions. In this study, 13,809 HPV52‐positive patients visiting the Obstetrics and Gynecology Hospital of Fudan University from 2018 to 2023 were included in the first stage to investigate the risk of cervical lesions among different multiple infection patterns. Another 443 HPV52‐positive patients were further included for sequence alignment and phylogenetic analysis. In the current study, the most common HPV52 dual‐infection patterns were as follows: HPV16 + HPV52, HPV52 + HPV58, HPV52 + HPV53, and HPV52 + HPV81. Compared with HPV52 single infection, the risk of HSIL+ was increased in HPV16 + HPV52 (OR = 3.47, 95% CI: 2.56, 4.69) and HPV52 + HPV58 (OR = 1.99, 95% CI: 1.35, 2.92) groups. The most common triple‐infection patterns were HPV16 + HPV52 + HPV53 and HPV52 + HPV53 + HPV81, followed by HPV52 + HPV53 + HPV58. HPV53 was the most common co‐infection type with HPV52 in cases of triple or more multiple infections. However, compared with dual infection, the addition of HPV53 did not affect the risk of HSIL+. Two synonymous mutations, G207A ( p = 0.029) and C1203T ( p = 0.021), showed statistically significant differences in distribution between single and multiple infection groups. Our results demonstrated that HPV52 showed preferences for co‐infection with HPV16, 585,381. HPV52 co‐infection with HPV16 and HPV58 increased the risk of HSIL+, while co‐infection with HPV53 did not increase the risk of HSIL+. Virus variants with certain mutations may be more susceptible to multiple infections.

Risk‐stratified management of cervical high‐grade squamous intraepithelial lesion based on machine learning

AbstractThe concordance rate between conization and colposcopy‐directed biopsy (CDB) proven cervical high‐grade squamous intraepithelial lesion (HSIL) were 64−85%. We aimed to identify the risk factors associated with pathological upgrading or downgrading after conization in patients with cervical HSIL and to provide risk‐stratified management based on a machine learning predictive model.This retrospective study included patients who visited the Obstetrics and Gynecology Hospital of Fudan University from January 1 to December 31, 2019, were diagnosed with cervical HSIL by CDB, and subsequently underwent conization. A wide variety of data were collected from the medical records, including demographic data, laboratory findings, colposcopy descriptions, and pathological results. The patients were categorized into three groups according to their postconization pathological results: low‐grade squamous intraepithelial lesion (LSIL) or below (downgrading group), HSIL (HSIL group), and cervical cancer (upgrading group). Univariate and multivariate analyses were performed to identify the independent risk factors for pathological changes in patients with cervical HSIL. Machine learning prediction models were established, evaluated, and subsequently verified using external testing data.In total, 1585 patients were included, of whom 65 (4.1%) were upgraded to cervical cancer after conization, 1147 (72.4%) remained having HSIL, and 373 (23.5%) were downgraded to LSIL or below. Multivariate analysis showed a 2% decrease in the incidence of pathological downgrade for each additional year of age and a 1% increase in lesion size. Patients with cytology > LSIL (odds ratio [OR] = 0.33; 95% confidence interval [CI], 0.21–0.52), human papillomavirus (HPV) infection (OR = 0.33; 95% CI, 0.14–0.81), HPV 33 infection (OR = 0.37; 95% CI, 0.18–0.78), coarse punctate vessels on colposcopy examination (OR = 0.14; 95% CI, 0.06–0.32), HSIL lesions in the endocervical canal (OR = 0.48; 95% CI, 0.30–0.76), and HSIL impression (OR = 0.02; 95% CI, 0.01–0.03) were less likely to experience pathological downgrading after conization than their counterparts. The independent risk factors for pathological upgrading to cervical cancer after conization included the following: age (OR = 1.08; 95% CI, 1.04–1.12), HPV 16 infection (OR = 4.07; 95% CI, 1.70–9.78), the presence of coarse punctate vessels during colposcopy examination (OR = 2.21; 95% CI, 1.08–4.50), atypical vessels (OR = 6.87; 95% CI, 2.81–16.83), and HSIL lesions in the endocervical canal (OR = 2.91; 95% CI, 1.46–5.77). Among the six machine learning prediction models, the back propagation (BP) neural network model demonstrated the highest and most uniform predictive performance in the downgrading, HSIL, and upgrading groups, with areas under the curve (AUCs) of 0.90, 0.84, and 0.69; sensitivities of 0.74, 0.84, and 0.42; specificities of 0.90, 0.71, and 0.95; and accuracies of 0.74, 0.84, and 0.95, respectively. In the external testing set, the BP neural network model showed a higher predictive performance than the logistic regression model, with an overall AUC of 0.91. Therefore, a web‐based prediction tool was developed in this study.BP neural network prediction model has excellent predictive performance and can be used for the risk stratification of patients with CDB‐diagnosed HSIL.

Hypermethylated PCDHGB7 as a universal cancer only marker and its application in early cervical cancer screening

25‐hydroxycholesterol inhibits human papillomavirus infection in cervical epithelial cells by perturbing cytoskeletal remodeling

AbstractPersistent high‐risk human papilloma virus (HR‐HPV) infection is the main risk factor for cervical cancer, threatening women's health. Despite growing prophylactic vaccination, annual cervical cancer cases are still increasing and show a trend of younger onset age. However, therapeutic approaches towards HPV infection are still limited. 25‐hydrocholesterol (25HC) has a wide‐spectrum inhibitory effect on a variety of viruses. To explore efficient interventions to restrict HPV infection at an early time, we applied different pseudoviruses (PsV) to evaluate anti‐HPV efficacy of 25HC. We tested PsV inhibition by 25HC in cervical epithelial‐derived HeLa and C‐33A cells, using high‐risk (HPV16, HPV18, HPV59), possibly carcinogenic (HPV73), and low‐risk (HPV6) HPV PsVs. Then we established murine genital HPV PsV infection models and applied IVIS to evaluate anti‐HPV efficacy of 25HC in vivo. Next, with the help of confocal imaging, we targeted 25HC activity at filopodia upon HPV exposure. After that, we used RNA‐seq and Western blot analysis to investigate (1) how 25HC disturbs actin cytoskeleton remodeling during HPV infection and (2) how prenylation regulates the cytoskeletal remodeling signaling pathway. Our findings suggest that 25HC perturbs F‐actin rearrangement by reducing small GTPase prenylation. In this way, the phenomenon of HPV virion surfing was restricted, leading to failed infection.