Ping Li

Therapeutic targeting of interleukin-17C signaling in carcinogenesis of endometriosis

Endometriosis-associated ovarian carcinoma (EAOC) predominantly arises from the malignant transformation of endometriomas, yet the mechanism is incompletely defined. Spatial transcriptomic analysis of human specimens of normal endometrium, endometriomas, and EAOC identified interleukin-17C (IL-17C) signaling activation, with higher IL-17 receptor E (IL-17RE) expression in EAOC. Additionally, the IL-17C concentration was significantly increased in the peritoneal fluid of women with ovarian cancer. Using an endometriosis mouse model overexpressing IL-17RE, IL-17C levels were elevated in the peritoneal fluid. Furthermore, IL-17C knockout reduced the peritoneal fluid IL-17C concentration and inhibited ectopic lesion growth in endometriosis mice. In addition, the role of IL-17C signaling in promoting endometriosis carcinogenesis was investigated by blocking and modulating the IL-17C/IL-17RE pathway in human endometriotic epithelial cells, endometrial organoids, and ovarian endometriosis mice. These data identified IL-17C signaling as a driver of endometriosis carcinogenesis and propose IL-17C/IL-17RE as promising therapeutic targets, particularly for EAOC cases characterized by high IL-17C expression.

CT-based Machine Learning Radiomics Modeling: Survival Prediction and Mechanism Exploration in Ovarian Cancer Patients

To create a radiomics model based on computed tomography (CT) to predict overall survival in ovarian cancer patients. To combine Rad-score with genomic data to explore the association between gene expression and Rad-score. Imaging and clinical data from 455 patients with ovarian cancer were retrospectively analyzed. Patients were categorized into training cohort, validation cohort and test cohort. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) methods were utilized to identify characteristics and develop the Rad-score. Radiomics models were developed and evaluated for predictive efficacy and clinical incremental value. Application of genomic data from the cancer genome atlas (TCGA) to reveal differential genes in different Rad-score groups. Screening hub genes and exploring the functions of hub genes through bioinformatics analysis and machine learning. Prognostic models based on FIGO, tumor residual disease and Rad-score were developed. The receiver operating characteristic (ROC) curves showed that the 1, 3, and 5 year area under curves (AUCs) of the model were in the training group (0.816, 0.865 and 0.862, respectively), validation group (0.845, 0.877, 0.869, respectively) and test group (0.899, 0.906 and 0.869, respectively) had good predictive accuracy. Calibration curves showed good agreement between observations and predictions. Decision curve analysis revealed a high net benefit of the clinical-radiomics model. The clinical impact curve (CIC) showed good clinical applicability of the clinical-radiomics model. Analysis of sequencing data from the TCGA database revealed EMP1 as a hub gene for radiomics modeling. It revealed that its biological function may be associated with extracellular matrix organization and focal adhesion. Prognostic models based on FIGO, Tumor residual disease, and Rad-score can effectively predict the overall survival (OS) of ovarian cancer patients. Rad-score may enable prognostic prediction of ovarian cancer patients by revealing the expression level of EMP1 and its biological function.

Unveiling the ZNF384-INTS13-hnRNPC axis as a therapeutic vulnerability in cervical cancer

Abstract Cervical cancer remains a major global health burden, necessitating the identification of novel therapeutic targets to overcome the limitations of current treatments. Here, we comprehensively investigated the role of integrator complex subunit 13 (INTS13) in cervical cancer progression. Our analysis of publicly available The Cancer Genome Atlas (TCGA) datasets revealed that INTS13 is significantly overexpressed in cervical cancer tissues across various histological subtypes, correlating with advanced tumor T-stage and predicting poorer overall survival. Single-cell RNA sequencing further localized INTS13 expression predominantly to malignant epithelial cells within the tumor microenvironment, where its expression correlated with genes involved in critical cellular processes. Furthermore, elevated expression has been observed in cervical cancer tissues from surgically-treated patients and in various primary human cervical cancer cells. In vitro functional studies demonstrated that genetic silencing or CRISPR/Cas9-mediated knockout of INTS13 significantly inhibited the proliferation, migration, and invasion of primary cervical cancer cells, while selectively inducing apoptosis. Conversely, ectopic INTS13 overexpression markedly enhanced these malignant phenotypes. Mechanistically, we identified heterogeneous nuclear ribonucleoprotein C (hnRNPC) as a critical downstream effector, with INTS13 regulating hnRNPC expression, and the restoration of hnRNPC effectively reversing the anti-cervical cancer effects observed upon INTS13 silencing. Furthermore, the transcription factor ZNF384 (zinc finger protein 384) was identified as an upstream regulator that directly binds to and positively governs INTS13 expression. Finally, in vivo animal models confirmed that targeted silencing of INTS13 significantly impeded cervical cancer xenograft growth in nude mice, reduced cellular proliferation, and augmented apoptosis, consistently accompanied by a reduction in hnRNPC expression. These findings collectively establish INTS13 as a crucial precancerous gene in cervical cancer, promoting malignant phenotypes primarily through the ZNF384-INTS13-hnRNPC signaling axis.

Intratumoral and Peritumoral Radiomics for Predicting the Prognosis of High-grade Serous Ovarian Cancer Patients Receiving Platinum-Based Chemotherapy

This study aimed to develop a deep learning (DL) prognostic model to evaluate the significance of intra- and peritumoral radiomics in predicting outcomes for high-grade serous ovarian cancer (HGSOC) patients receiving platinum-based chemotherapy. A DL model was trained and validated on retrospectively collected unenhanced computed tomography (CT) scans from 474 patients at two institutions, which were divided into a training set (N = 362), an internal test set (N = 86), and an external test set (N = 26). The model incorporated tumor segmentation and peritumoral region analysis, using various input configurations: original tumor regions of interest (ROIs), ROI subregions, and ROIs expanded by 1 and 3 pixels. Model performance was assessed via hazard ratios (HRs) and receiver operating characteristic (ROC) curves. Patients were stratified into high- and low-risk groups on the basis of the training set's optimal cutoff value. Among the input configurations, the model using an ROI with a 1-pixel peritumoral expansion achieved the highest predictive accuracy. The DL model exhibited robust performance for predicting progression-free survival, with HRs of 3.41 (95% CI: 2.85, 4.08; P < 0.001) in training set, 1.14 (95% CI: 1.03, 1.26; P = 0.012) in internal test set, and 1.32 (95% CI: 1.07, 1.63; P = 0.011) in external test set. KM survival analysis revealed significant differences between the high-risk and low-risk groups (P < 0.05). The DL model effectively predicts survival outcomes in HGSOC patients receiving platinum-based chemotherapy, offering valuable insights for prognostic assessment and personalized treatment planning.

Gap junction protein beta 5 interacts with Gαi3 to promote Akt activation and cervical cancer cell growth

Abstract Identifying novel therapeutic targets for cervical cancer is crucial for improving patient outcomes and reducing the global burden of this disease. Gap junction protein beta 5 (GJB5) is a member of the connexin family of proteins involved in cell-to-cell communication. This study investigated GJB5’s expression and functional significance in cervical cancer. Analysis of The Cancer Genome Atlas (TCGA) data demonstrated significantly increased GJB5 mRNA expression in cervical cancer tissues compared to normal cervical epithelium. Moreover, high GJB5 expression correlated with reduced overall survival and other adverse clinical outcomes. Single-cell RNA sequencing corroborated GJB5 overexpression within the malignant tumor cell population. The downregulation of GJB5 through shRNA or CRISPR/Cas9 gene knockout techniques significantly impaired the viability, proliferation, and migratory capacity of cervical cancer cells, while concurrently inducing apoptotic processes. Conversely, the forced overexpression of GJB5 resulted in enhanced malignant behaviors. Investigations into the underlying mechanisms revealed that GJB5 is integral to the activation of the Akt-mTOR (mammalian target of rapamycin) signaling pathway. GJB5 knockdown or knockout led to diminished phosphorylation of Akt and S6 kinase, whereas GJB5 overexpression correlated with increased Akt-mTOR signaling in primary human cervical cancer cells. Additionally, we identified a novel interaction between GJB5 and the Gαi3 (G alpha inhibitory protein 3), underscoring the crucial role of GJB5 in mediating Akt activation via Gαi3. In vivo studies utilizing xenograft models provided further evidence for the oncogenic function of GJB5. The knockdown of GJB5 resulted in a marked reduction in the growth of cervical cancer xenografts. Observations of proliferation arrest, inactivation of the Akt-mTOR pathway, and the induction of apoptosis were noted in GJB5-depleted cervical cancer xenograft tissues. Collectively, these findings underscore GJB5 as a key oncogenic driver in cervical cancer and indicate that targeting GJB5 could be a promising therapeutic approach for this disease.

Co-evolution of vaginal microbiome and cervical cancer

Abstract Background Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models. Methods This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs). Results In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set. Conclusions The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

Changes of miRNA Expression Profiles from Cervical‐Vaginal Fluid‐Derived Exosomes in Response to HPV16 Infection

As an oncogenic virus, HPV16 can lead to the dysfunction of cervical epithelial cells and contribute to the progression of cervical cancer. Components from the cervical‐vaginal fluid (CVF) could be used as the basis for cervical cancer screening. Exosomes are widely present in various body fluids and participate in intercellular communication via its cargos of proteins, mRNAs, and miRNAs. This study was conducted to explore the changes of miRNAs in exosomes isolated form the cervical‐vaginal fluid during HPV16 infection and to predict the potential effects of exosomal miRNAs on the development of cervical cancer. CVF was collected from volunteers with or without HPV16 infection. The exosomes in CVF were identified by electron microscopy. Microarray analysis was subjected to find the differentially expressed miRNAs in CVF exosomes. To confirm the results, 16 miRNAs were randomly selected to go through real‐time quantitative polymerase chain reaction. In addition, GO and pathway analyses were conducted to reveal potential functions of differentially expressed miRNAs. A total of 2548 conserved miRNAs were identified in the cervical‐vaginal fluid‐derived exosomes. In response to HPV16 infection, 45 miRNAs are significantly upregulated and 55 miRNAs are significantly downregulated (P &lt; 0.05). The GO and KEGG pathway analyses revealed that these differentially expressed miRNAs are tightly associated with cervical cancer tumorigenesis, through interaction with the Notch signaling pathway, TNF signaling pathway, and TGF‐β signaling pathway. These results suggest that exosomal miRNAs in CVF are differentially expressed in HPV16 infection patients and HPV16‐free volunteers. It provided a novel insight to understand the underlying mechanism of HPV16 infection in regulating cervical cancer progression.

Prevalence characteristics of cervical human papillomavirus (HPV) infection in the Zhoupu District, Shanghai City, China

Abstract Background Human papillomavirus (HPV) infection is the leading cause of genital diseases. It can cause a series of cervical lesions. The distribution of HPV genotypes indicates that the increased prevalence of high-risk HPV (HR-HPV) is positively correlated with the severity of cervical lesions. In addition, persistent HR-HPV infection is associated with the risk of cervical cancer. Considering the latest approval of homemade HPV vaccine in China and the prevalence of HPV distribution, this is of great significance for guiding HPV vaccination work. Objective Our study’s purpose was to examine trends of cervical HPV infection rate in each 5-year age group from 2011 to 2019. Methods Retrospective analysis of human papillomavirus prevalence rate of 59,541 women from 2011 to 2019 in the District Zhoupu of Shanghai City in China. HPV genotype testing is performed using a commercial kit designed to detect 15 high-risk HPV genotypes and 6 low-risk HPV genotypes. Trends were examined for each 5-year age group. Results In the District Zhoupu of Shanghai City in China, the prevalence rate of cervical HPV increased significantly among women aged 15–34 years. The most prevalent HR-HPV genotypes were 52, 16, 58, 53, 39, and 51. Conclusion Cervical HPV prevalence rate is very high in younger women in suburb Shanghai. Due to significant differences in infection rates between specific age groups and HPV subtypes, timely intervention is required for these vulnerable populations.

Endothelial cell-specific molecule 1 drives cervical cancer progression

AbstractThe expression, biological functions and underlying molecular mechanisms of endothelial cell-specific molecule 1 (ESM1) in human cervical cancer remain unclear. Bioinformatics analysis revealed that ESM1 expression was significantly elevated in human cervical cancer tissues, correlating with patients’ poor prognosis. Moreover, ESM1 mRNA and protein upregulation was detected in local cervical cancer tissues and various cervical cancer cells. In established and primary cervical cancer cells, ESM1 shRNA or CRISPR/Cas9-induced ESM1 KO hindered cell proliferation, cell cycle progression, in vitro cell migration and invasion, and induced significant apoptosis. Whereas ESM1 overexpression by a lentiviral construct accelerated proliferation and migration of cervical cancer cells. Further bioinformatics studies and RNA sequencing data discovered that ESM1-assocaited differentially expressed genes (DEGs) were enriched in PI3K-Akt and epithelial-mesenchymal transition (EMT) cascades. Indeed, PI3K-Akt cascade and expression of EMT-promoting proteins were decreased after ESM1 silencing in cervical cancer cells, but increased following ESM1 overexpression. Further studies demonstrated that SYT13 (synaptotagmin 13) could be a primary target gene of ESM1. SYT13 silencing potently inhibited ESM1-overexpression-induced PI3K-Akt cascade activation and cervical cancer cell migration/invasion. In vivo, ESM1 knockout hindered SiHa cervical cancer xenograft growth in mice. In ESM1-knockout xenografts tissues, PI3K-Akt inhibition, EMT-promoting proteins downregulation and apoptosis activation were detected. In conclusion, overexpressed ESM1 is important for cervical cancer growth in vitro and in vivo, possibly by promoting PI3K-Akt activation and EMT progression. ESM1 represents as a promising diagnostic marker and potential therapeutic target of cervical cancer.