Tao Liu

SRSF9 Forms Phase-Separated Condensates to Promote Ovarian Cancer Progression by Inducing RNA Alternative Splicing That Is Inhibited by m6A Modification

Abstract Deregulation of RNA alternative splicing and modification can play an important role in tumor initiation and progression. Elucidation of the interplay between alternative splicing and modifications of RNA could provide important insights into cancer biology. In this study, we showed that serine/arginine-rich splicing factor 9 (SRSF9) recognized non-N6-methyladenosine (m6A)–modified NUMB mRNA and induced an oncogenic isoform switch in ovarian cancer. NUMB mRNA m6A modification antagonized SRSF9-mediated alternative splicing. Notably, SRSF9 formed phase-separated condensates within the nucleus, which was indispensable for its splicing function as well as its tumor-promoting effect in ovarian cancer. Furthermore, SRSF9 was aberrantly upregulated in ovarian cancer, correlating with poor patient prognosis. Loss of SRSF9 or antisense oligonucleotide–mediated isoform switch of NUMB mRNA inhibited ovarian cancer growth in vitro and in vivo. In conclusion, this study reveals that SRSF9 condensation promotes ovarian cancer progression through modulation of alternative splicing, in competition with m6A modification. Significance: Phase separation increases activity of the splicing factor SRSF9 to support progression of ovarian cancer by generating an oncogenic isoform of NUMB mRNA competitively with m6A modification, which provides promising therapeutic targets.

hnRNPL phase separation activates PIK3CB transcription and promotes glycolysis in ovarian cancer

Ovarian cancer has the highest mortality rate among gynecologic tumors worldwide, with unclear underlying mechanisms of pathogenesis. RNA-binding proteins (RBPs) primarily direct post-transcriptional regulation through modulating RNA metabolism. Recent evidence demonstrates that RBPs are also implicated in transcriptional control. However, the role and mechanism of RBP-mediated transcriptional regulation in tumorigenesis remain largely unexplored. Here, we show that the RBP heterogeneous ribonucleoprotein L (hnRNPL) interacts with chromatin and regulates gene transcription by forming phase-separated condensates in ovarian cancer. hnRNPL phase separation activates PIK3CB transcription and glycolysis, thus promoting ovarian cancer progression. Notably, we observe that the PIK3CB promoter is transcribed to produce a non-coding RNA which interacts with hnRNPL and promotes hnRNPL condensation. Furthermore, hnRNPL is significantly amplified in ovarian cancer, and its high expression predicts poor prognosis for ovarian cancer patients. By using cell-derived xenograft and patient-derived organoid models, we show that hnRNPL knockdown suppresses ovarian tumorigenesis. Together, our study reveals that phase separation of the chromatin-associated RBP hnRNPL promotes PIK3CB transcription and glycolysis to facilitate tumorigenesis in ovarian cancer. The formed hnRNPL-PIK3CB-AKT axis depending on phase separation can serve as a potential therapeutic target for ovarian cancer.

ALYREF condensation stabilizes m5C-modified PARP10 mRNA and promotes PI3K-AKT signaling in ovarian cancer

Abstract The role of epigenetic regulation of RNAs in the tumorigenesis remains incompletely understood. This study uncovers a critical function of the 5-methylcytosine (m 5 C) RNA modification reader protein ALYREF (also termed, ALY; BEF) in ovarian cancer. ALYREF is elevated in ovarian cancer patient samples, and its depletion reduces ovarian tumorigenesis and metastasis in mice in a m 5 C-dependent manner. Mechanistically, ALYREF binds to the m 5 C-modified mRNA of ADP-ribosyltransferase PARP10, competing with exosome complex component MTR4, and enhancing the stability and nuclear export of PARP10 mRNA. Further, ALYREF forms condensates in the nucleus of ovarian cancer cells, and depletion or mutation of ALYREF’s intrinsically disordered regions rescues its control on PARP10 mRNA nucleoplasmic distribution and stability, reduces tumor growth and is required for promotion of ovarian cancer aggressiveness and proliferation. Finally, ALYREF and PARP10 expression correlate with poor prognosis in ovarian cancer patients. Together, these findings suggest that ALYREF phase separation facilitates the malignant progression of ovarian cancer by promoting PARP10 expression and thereby enhancing PARP10-dependent proliferative pathways in a m 5 C-dependent manner.

Genome‐wide profiling of N6‐methyladenosine‐modified pseudogene‐derived long noncoding RNAs reveals the tumour‐promoting and innate immune‐restraining function of RPS15AP12 in ovarian cancer

AbstractBackgroundPseudogene‐derived lncRNAs are widely dysregulated in cancer. Technological advancements have facilitated the functional characterization of increasing pseudogenes in cancer progression. However, the association between pseudogenes and RNA N6‐methyladenosine (m6A) modification in cancer, as well as the underlying mechanisms, remains largely unexplored.MethodsWe analyzed the expression of 12 146 pseudogenes and comprehensively examined the m6A modification of RNAs derived from them and their paralogs. Through integrative analysis of multi‐omics data, we explored the associations between pseudogene dysregulation and m6A, identifying critical pseudogenes involved in HGSOC progression. Tumour promotion role of RPS15AP12 and its cognate parent gene was characterized by cell proliferation, transwell assays, and scratch assays in ovarian cells and xenograft nude mice. RNA decay assays were used to reveal the participation of m6A in decreasement of RPS15AP12 lncRNA stability. Luciferase reporter assays were performed to verify that RPS15AP12 enhances RPS15A expression by competitively binding to miR‐96‐3p. Western blot and phosphorylation assays were performed to investigate the impairment of RPS15AP12 towards the sensors of MAVS (RIG‐I and MDA5), and downstream p‐TBK1 and p‐IRF3. Finally, ELISA assays were performed to explore the regulatory role of RPS15AP12 in IFN‐β expression.ResultsM6A is distributed across over a thousand pseudogenes, and hypomethylation leads to their upregulation in HGSOC. We identified a processed pseudogene, RPS15AP12, upregulated by FTO‐mediated m6A demethylation. RPS15AP12 enhances the growth ability and metastatic capabilities of ovarian cancer (OC) cells via functioning as a competitive endogenous RNA (ceRNA) for its host gene, RPS15A, through the sequestration of miR‐96‐3p. Importantly, the deletion of RPS15AP12 diminishes the expression of RPS15A, leading to the upregulation of anti‐tumour immune responses by activating RIG‐I and MDA5 and downstream p‐TBK1 and p‐IRF3 as well as IFN‐β levels.ConclusionOur findings expand the understanding of m6A‐modulated pseudogenes in tumour growth and anti‐tumour innate immunity in OC.Key Points Genome‐wide profiling reveals the redistribution of m6A modification on pseudogene‐derived lncRNAs and m6A redistribution‐relevant dysregulation of pseudogenes in HGSOC. RPS15AP12, as a representative processed pseudogene, is up‐regulated by FTO‐mediated demethylation and acts as a miRNA sponge to promote RPS15A expression via competitively binding to miR‐96‐3p. RPS15AP12/RPS15A axis inhibits MAVS sensors (RIG‐I and MDA5) and downstream IFN‐β levels in ovarian cancer.

m6A-driven NAT10 translation facilitates fatty acid metabolic rewiring to suppress ferroptosis and promote ovarian tumorigenesis through enhancing ACOT7 mRNA acetylation

RNA epigenetic modifications have been implicated in cancer progression. However, the interplay between distinct RNA modifications and its role in cancer metabolism remain largely unexplored. Our study demonstrates that N-acetyltransferase 10 (NAT10) is notably upregulated in ovarian cancer (OC), correlating with poor patient prognosis. IGF2BP1 enhances the translation of NAT10 mRNA in an m

A Study on Automatic O-RADS Classification of Sonograms of Ovarian Adnexal Lesions Based on Deep Convolutional Neural Networks

This study explored a new method for automatic O-RADS classification of sonograms based on a deep convolutional neural network (DCNN). A development dataset (DD) of 2,455 2D grayscale sonograms of 870 ovarian adnexal lesions and an intertemporal validation dataset (IVD) of 426 sonograms of 280 lesions were collected and classified according to O-RADS v2022 (categories 2-5) by three senior sonographers. Classification results verified by a two-tailed z-test to be consistent with the O-RADS v2022 malignancy rate indicated the diagnostic performance was comparable to that of a previous study and were used for training; otherwise, the classification was repeated by two different sonographers. The DD was used to develop three DCNN models (ResNet34, DenseNet121, and ConvNeXt-Tiny) that employed transfer learning techniques. Model performance was assessed for accuracy, precision, and F1 score, among others. The optimal model was selected and validated over time using the IVD and to analyze whether the efficiency of O-RADS classification was improved with the assistance of this model for three sonographers with different years of experience. The proportion of malignant tumors in the DD and IVD in each O-RADS-defined risk category was verified using a two-tailed z-test. Malignant lesions (O-RADS categories 4 and 5) were diagnosed in the DD and IVD with sensitivities of 0.949 and 0.962 and specificities of 0.892 and 0.842, respectively. ResNet34, DenseNet121, and ConvNeXt-Tiny had overall accuracies of 0.737, 0.752, and 0.878, respectively, for sonogram prediction in the DD. The ConvNeXt-Tiny model's accuracy for sonogram prediction in the IVD was 0.859, with no significant difference between test sets. The modeling aid significantly reduced O-RADS classification time for three sonographers (Cohen's d = 5.75). ConvNeXt-Tiny showed robust and stable performance in classifying O-RADS 2-5, improving sonologists' classification efficacy.

The application of organoids in cancers associated with pathogenic infections

AbstractCancers associated with pathogen infections are gradually becoming important threats to human health globally, and it is of great significance to study the mechanisms of pathogen carcinogenesis. Current mechanistic studies rely on animal and two-dimensional (2D) cell culture models, but traditional methods have been proven insufficient for the rapid modeling of diseases caused by new pathogens. Therefore, research focus has shifted to organoid models, which can replicate the structural and genetic characteristics of the target tissues or organs in vitro, providing new platforms for the study of pathogen-induced oncogenic mechanisms. This review summarizes the application of organoid technology in the studies of four pathogen-associated cancers: gastric cancer linked to Helicobacter pylori, liver cancer associated with hepatitis B virus or hepatitis C virus, colorectal cancer caused by Escherichia coli, and cervical cancer related to human papillomavirus. This review also proposes several limitations of organoid technology to optimize organoid models and advance the treatment of cancer associated with pathogen infections in the future.

The m6A reader YTHDF1 promotes ovarian cancer progression via augmenting EIF3C translation

Abstract N 6-Methyladenosine (m6A) is the most abundant RNA modification in mammal mRNAs and increasing evidence suggests the key roles of m6A in human tumorigenesis. However, whether m6A, especially its ‘reader’ YTHDF1, targets a gene involving in protein translation and thus affects overall protein production in cancer cells is largely unexplored. Here, using multi-omics analysis for ovarian cancer, we identified a novel mechanism involving EIF3C, a subunit of the protein translation initiation factor EIF3, as the direct target of the YTHDF1. YTHDF1 augments the translation of EIF3C in an m6A-dependent manner by binding to m6A-modified EIF3C mRNA and concomitantly promotes the overall translational output, thereby facilitating tumorigenesis and metastasis of ovarian cancer. YTHDF1 is frequently amplified in ovarian cancer and up-regulation of YTHDF1 is associated with the adverse prognosis of ovarian cancer patients. Furthermore, the protein but not the RNA abundance of EIF3C is increased in ovarian cancer and positively correlates with the protein expression of YTHDF1 in ovarian cancer patients, suggesting modification of EIF3C mRNA is more relevant to its role in cancer. Collectively, we identify the novel YTHDF1-EIF3C axis critical for ovarian cancer progression which can serve as a target to develop therapeutics for cancer treatment.

Relationship between dietary diversity and oral frailty in elderly gynecologic tumor patients

This study explores the relationship between dietary diversity and oral frailty in elderly gynecologic tumor patients. A total of 180 gynecologic tumor patients treated in our hospital from January 2021 to December 2024 were selected. Patients were divided into an oral frailty group (n = 71) and a non-oral frailty group (n = 109) based on the occurrence of oral frailty ( ≥ 4). Influencing factors were analyzed using univariate and binary logistic regression analysis. The correlation between variables was assessed with Pearson correlation analysis. The predictive value of Dietary Diversity Score (DDS) for the occurrence of oral frailty in elderly gynecologic tumor patients was evaluated using the receiver operating characteristic curve, and the dietary intake of patients with different oral frailty statuses was observed. The average DDS score among the 180 patients was 3.96 ± 1.39. There was no statistically significant difference in DDS among patients with different general characteristics (P > .05). Similarly, there was no statistically significant difference in general characteristics between the oral frailty group and the non-oral frailty group (P > .05). However, a statistically significant difference was observed between DDS and smoking status (P < .05). Binary logistic regression analysis indicated that DDS was a significant factor influencing the occurrence of oral frailty in elderly gynecologic tumor patients (P < .05). Receiver operating characteristic analysis showed that the area under the DDS predictive curve was 0.883, with a standard error of 0.025 (95% confidence interval: 0.834–0.932, P < .001); the Youden index was 0.60, with a sensitivity of 70.42% and specificity of 89.91%. The optimal cutoff value was 4.5. There was no statistically significant difference in the intake of eggs, fish, milk, and dairy products (P > .05), while there were statistically significant differences in the intake of meat, legumes, yogurt, vegetables, and fruits (P < .05). Pearson linear correlation analysis showed that Oral Frailty Index-8 was negatively correlated with vegetable intake, fruit intake, and DDS (r = −0.300, −0.233, −0.338, respectively; P < .05). Increased severity of oral frailty in elderly gynecologic tumor patients is associated with reduced dietary diversity.