Use of SNRPF-DDX24-E2F4 loop in preparation of ovarian cancer drug or ovarian cancer prognosis evaluation product

Abstract

The application discloses application of an SNRPF-DDX24-E2F4 loop in preparation of an ovarian cancer drug or an ovarian cancer prognosis evaluation product, and belongs to the field of biomedical technologies.The SNRPF-DDX24-E2F4 loop is used as a drug target for preparing a drug for treating ovarian cancer and / or as a biomarker for preparing a product for evaluating the prognosis of ovarian cancer; wherein the SNRPF-DDX24-E2F4 loop refers to a self-reinforcing positive feedback regulation loop composed of SNRPF, DDX24 and E2F4; SNRPF maintains the expression of a DDX24 protein coding type transcript by regulating the correct splicing of intron 6 of the DDX24 gene; DDX24 maintains the expression of an E2F4 protein coding type transcript by regulating the correct splicing of intron 2 of the E2F4 gene; E2F4 directly combines with the SNRPF promoter and activates the transcription thereof as a transcription factor, thereby forming a closed positive feedback regulation loop; the application discloses the existence of the loop in ovarian cancer and the core driving effect thereof, provides a new target and a new application for preparing an ovarian cancer drug, provides a new biomarker and a detection method for evaluating the prognosis of ovarian cancer, and has important clinical transformation value and a wide application prospect.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of the SNRPF-DDX24-E2F4 loop in the preparation of ovarian cancer drugs or ovarian cancer prognostic assessment products. Background Technology

[0002] Ovarian cancer is one of the most common gynecological malignancies, ranking eighth in both incidence and mortality among female cancers worldwide. Epithelial ovarian cancer is the most prevalent histological subtype, accounting for nearly 90% of all ovarian cancer cases, with high-grade serous carcinoma accounting for approximately 70%–80%. Due to the lack of reliable early detection methods, about 80% of patients are diagnosed at an advanced stage, and the five-year survival rate has long hovered at a low level of 30%–40%. Currently, the standard treatment strategy for advanced epithelial ovarian cancer includes cytoreductive surgery combined with systemic chemotherapy, supplemented by individualized maintenance therapy. Although targeted therapies, represented by anti-angiogenic drugs and PARP inhibitors, have become an important part of the precision treatment system for ovarian cancer, the beneficiary population is limited, drug resistance is widespread, and the overall efficacy remains unsatisfactory. The core reason for this predicament is that the current understanding of the molecular mechanisms of ovarian cancer pathogenesis is still insufficient, which limits the development of biomarker-driven diagnostic and treatment strategies.

[0003] Alternative splicing, a key post-transcriptional regulatory mechanism, can lead to the generation of oncogenic splice variants when dysregulated, thereby enhancing tumor cell proliferation, metastasis, and drug resistance. Intron retention, a special type of alternative splicing, is characterized by the aberrant retention of intron sequences in mature transcripts. This typically introduces premature stop codons and triggers nonsense-mediated mRNA decay (NMD), subsequently downregulating protein expression levels. Recent studies have shown that intron retention plays an important regulatory role in tumorigenesis and development, but systematic research on this event in ovarian cancer remains lacking.

[0004] The spliceosome is a highly complex ribonucleoprotein complex composed of small nuclear ribonucleoproteins (snRNPs), with its core Sm proteins including SNRPB, SNRPD1, SNRPD2, SNRPD3, SNRPE, SNRPF, and SNRPG. Dysregulation of Sm protein-related genes is frequently observed in malignant tumors and can directly participate in tumorigenesis by regulating intron retention and its coupled NMD pathway. For example, in non-small cell lung cancer, SNRPB promotes tumorigenesis by regulating RAB26 intron retention; in endometrial cancer, SNRPB affects POLD1 expression through intron retention; and in hepatocellular carcinoma, SNRPD2 synergistically regulates DDX39A intron retention with HNRNPL. However, the biological functions of Sm proteins in ovarian cancer, particularly their specific roles in intron retention regulation, lack systematic investigation.

[0005] DDX24, a member of the DEAD-box helicase family, has been shown to be closely related to tumorigenesis in recent years. Patent CN111254130A discloses the oncogenic effect of wild-type DDX24 in ovarian cancer and its application as a therapeutic target; however, this patent only involves a single DDX24 molecule and does not reveal its upstream regulatory mechanisms and downstream effector networks. E2F4, as a transcription factor, plays a well-established role in cell cycle regulation, but its function in tumors is rarely reported. Furthermore, current research lacks information on the core regulatory nodes driving intron retention abnormalities in ovarian cancer; the existence of a synergistic regulatory network between splicing factors and transcription factors remains unclear, and intervention strategies based on this network have not been reported.

[0006] To address the aforementioned technological gaps, there is an urgent need to conduct in-depth research on the regulatory mechanisms of intron retention events in ovarian cancer, to uncover core regulatory factors and their interaction networks, and to provide new technical solutions for the preparation of drugs and prognostic assessment products for ovarian cancer. Summary of the Invention

[0007] The purpose of this invention is to provide the application of the SNRPF-DDX24-E2F4 loop in the preparation of ovarian cancer drugs or ovarian cancer prognostic assessment products, in order to solve the technical problems in the prior art, such as the lack of core regulatory nodes of intron retention events in ovarian cancer, the unclear synergistic regulatory network of splicing factors and transcription factors, and the lack of intervention strategies based on this network. This invention reveals for the first time the existence of the SNRPF-DDX24-E2F4 positive feedback loop in ovarian cancer and its core mechanism for driving malignant progression, and provides new applications of drug targets and biomarkers based on this loop, providing a new technical solution for the treatment and prognostic assessment of ovarian cancer.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] This invention provides the application of the SNRPF-DDX24-E2F4 loop in the preparation of ovarian cancer drugs or ovarian cancer prognostic assessment products. The key is that the above-mentioned SNRPF-DDX24-E2F4 loop is used as a drug target to prepare drugs for treating ovarian cancer, and / or as a biomarker to prepare products for ovarian cancer prognostic assessment.

[0010] Furthermore, the aforementioned SNRPF-DDX24-E2F4 loop refers to a self-reinforcing positive feedback regulatory loop composed of SNRPF, DDX24, and E2F4. Specifically, SNRPF maintains the expression of the DDX24 protein-coding transcript by regulating the correct splicing of intron 6 of the DDX24 gene, DDX24 maintains the expression of the E2F4 protein-coding transcript by regulating the correct splicing of intron 2 of the E2F4 gene, and E2F4, as a transcription factor, directly binds to the SNRPF promoter and activates its transcription, forming a closed positive feedback regulatory loop.

[0011] Furthermore, the aforementioned use of drug targets to prepare drugs for treating ovarian cancer refers to the preparation of reagents that interfere with the integrity of the aforementioned SNRPF-DDX24-E2F4 positive feedback loop, and the application of these reagents in the preparation of drugs for treating ovarian cancer.

[0012] Furthermore, the reagents for ensuring the integrity of the SNRPF-DDX24-E2F4 positive feedback loop are selected from any one or more of the following: reagents that inhibit SNRPF gene expression, reagents that inhibit DDX24 gene expression, or reagents that inhibit E2F4 gene expression.

[0013] Furthermore, the agents mentioned above for inhibiting SNRPF gene expression include antisense oligonucleotides or small interfering RNAs.

[0014] Furthermore, the reagents mentioned above that inhibit DDX24 gene expression include small interfering RNA.

[0015] Furthermore, the reagents mentioned above that inhibit E2F4 gene expression include small interfering RNA.

[0016] Specifically, the aforementioned products used as biomarkers for ovarian cancer prognostic assessment are achieved by detecting the expression level of the SNRPF gene or protein; among them, SNRPF, as a representative biomarker of the aforementioned SNRPF-DDX24-E2F4 loop, reflects the activation state of the loop at its expression level.

[0017] More specifically, the above-mentioned detection of the expression level of the SNRPF gene or protein is achieved by real-time quantitative PCR or immunohistochemistry.

[0018] More specifically, the above-mentioned detection of the activation status of the SNRPF-DDX24-E2F4 loop is achieved by detecting the expression level of the SNRPF gene or protein; the expression level of SNRPF in ovarian cancer tissue is significantly higher than that in normal ovarian or fallopian tube tissue, and high expression of SNRPF is significantly associated with shortened overall survival and progression-free survival.

[0019] The present invention discloses the following technical effects:

[0020] First, this invention reveals for the first time the existence and core driving role of the SNRPF-DDX24-E2F4 positive feedback loop in ovarian cancer. This invention discovers and confirms the existence of a self-reinforcing positive feedback regulatory loop of SNRPF-DDX24-E2F4 in ovarian cancer. Specifically, SNRPF maintains its protein-coding expression by regulating intron 6 splicing of DDX24, and DDX24 maintains its protein-coding expression by regulating intron 2 splicing of E2F4. E2F4, as a transcription factor, directly binds to the SNRPF promoter and activates its transcription. The findings of this invention fill a research gap in the study of core regulatory nodes of intron retention events in ovarian cancer and the synergistic regulatory network of splicing factors and transcription factors.

[0021] Secondly, this invention provides novel targets and applications for ovarian cancer drug development. This invention confirms that SNRPF, DDX24, and E2F4 can serve as drug targets for ovarian cancer. Functional experiments show that silencing SNRPF, DDX24, or E2F4 can significantly inhibit ovarian cancer cell proliferation, colony formation, migration, and invasion; nude mouse tumorigenesis experiments confirm that silencing SNRPF or DDX24 can inhibit tumor growth in vivo. Based on the characteristics of a positive feedback loop, this invention proposes drug applications that interfere with the loop integrity; rescue experiments confirm that blocking downstream processes can reverse the malignant phenotype driven by upstream overexpression. Furthermore, this invention uses antisense oligonucleotides (ASO) to target SNRPF, significantly inhibiting the malignant phenotype of ovarian cancer cells at the cellular level. In CDX and PDX animal models, intratumoral injection of SNRPF-ASO significantly inhibits tumor growth, providing a promising candidate drug for ovarian cancer treatment.

[0022] Third, this invention provides novel biomarkers and detection methods for prognostic assessment of ovarian cancer. This invention confirms that SNRPF is significantly highly expressed in ovarian cancer tissues, and its expression level is positively correlated with FIGO stage and significantly negatively correlated with overall survival and progression-free survival. As a representative biomarker of this circuit, the expression level of SNRPF can reflect the circuit's activation status and can be used for prognostic assessment of ovarian cancer patients. This invention also discloses various technical means for detecting the circuit's activation status, including detecting the expression level of the SNRPF gene or protein, detecting the retention level of intron 6 in DDX24, and detecting the retention level of intron 2 in E2F4. Among these, the intron retention level, as a detection indicator discovered based on the unique mechanism of this invention, has higher specificity.

[0023] In summary, this invention, through clinical sample analysis, cell function verification, molecular mechanism elucidation, and in vivo animal experiments, systematically reveals for the first time the core driving role of the SNRPF-DDX24-E2F4 positive feedback loop in the malignant progression of ovarian cancer. Based on the complete mechanistic analysis of this loop, this invention provides novel drug targets and prognostic biomarkers for ovarian cancer and verifies the feasibility of ASO-targeted intervention. It provides a complete theoretical basis and technical solution for the precision diagnosis and treatment of ovarian cancer, possessing significant clinical translational value and broad application prospects. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 The study found that high expression of SNRPF in ovarian cancer tissues was significantly associated with poor patient prognosis. Figure 1 A shows the differences in mRNA expression levels of 17 splicing-related genes obtained through KEGG spliceosome pathway screening in ovarian cancer (n=426), normal fallopian tube (n=5), and normal ovary (n=88) tissues based on the TCGA-GTEx joint database. Figure 1 B presents a comparison of the protein expression differences of the above 17 splicing-related genes in ovarian cancer (n=84) and normal ovarian (n=19) tissues based on CPTAC proteomics data. Figure 1 C uses the TCGA-GTEx dataset to analyze the expression levels of SNRPF mRNA in ovarian cancer (n=426), normal fallopian tube (n=5), and normal ovary (n=88) tissues; Figure 1D shows the comparison of SNRPF protein expression levels in ovarian cancer (n=84) and normal control (n=19) tissues based on CPTAC proteomics data; Figure 1 E demonstrated the use of the CSIOVDB database to analyze the expression differences of SNRPF mRNA in ovarian cancer and normal ovarian surface epithelium and fallopian tube epithelium. Figure 1 F shows the expression level of SNRPF mRNA in different stages of ovarian cancer based on the CSIOVDB database and FIGO staging. Figure 1 G shows the differential expression of SNRPF mRNA in proliferative (n=79), mesenchymal (n=68), immunoreactive (n=84), differentiated (n=68) subtypes and normal ovarian (n=8) tissues using TCGA-OV (AffyU133a) data analysis; Figure 1 H uses the GEPIA3 database to perform pan-cancer analysis on the expression level of SNRPF mRNA in various human malignant tumors and corresponding normal tissues; Figure 1 I demonstrates the use of real-time quantitative PCR to detect the mRNA expression levels of SNRPF in normal fallopian tubes (n=10), normal ovaries (n=10), and ovarian cancer tissues (n=16); Figure 1 J demonstrates the use of Western blotting to detect the protein expression levels of SNRPF in normal fallopian tubes (n=6), normal ovaries (n=6), and ovarian cancer tissues (n=14); Figure 1 K-pair Figure 1 Semi-quantitative analysis of grayscale values ​​was performed on the immunoblot band shown in J; Figure 1 L is a representative image of SNRPF immunohistochemical staining in ovarian cancer tissue; Figure 1 M- Figure 1 N shows the immunohistochemical scores based on tissue microarrays and corresponding clinical follow-up data. The Kaplan-Meier method was used to analyze the correlation between SNRPF expression levels and patients' overall survival and progression-free survival.

[0026] Figure 2 The results of a study on the ability of SNRPF to silencing and inhibit the proliferation and metastasis of ovarian cancer cells include... Figure 2 A shows the knockdown efficiency of SNRPF mRNA in SKOV3, HEY and OVCAR8 cells after transient transfection with two independent siRNAs, using real-time quantitative PCR. Figure 2 B shows the effect of siRNA-mediated SNRPF protein silencing in the above cell lines as detected by Western blotting. Figure 2 C shows the effect of SNRPF deficiency on the proliferative capacity of ovarian cancer cells using the MTT assay; Figure 2 D shows the effect of SNRPF silencing on the clonogenic ability of ovarian cancer cells using a plate colony formation assay. Figure 2 E shows the change in the proportion of cells in the DNA synthesis phase (S phase) of ovarian cancer cells after SNRPF downregulation, determined by an EdU incorporation assay; Figure 2 F shows the effect of Transwell chamber assay on the inhibitory effect of SNRPF knockdown on the migration and invasion of SKOV3, HEY and OVCAR8 cells. Figure 2 G shows the construction of a nude mouse xenograft model using HEY cells stably transfected with shRNA-mediated SNRPF knockdown (PLKO.1-shSNRPF#1, PLKO.1-shSNRPF#2) or corresponding controls (PLKO.1-shNC). The figure shows a representative image of the obtained subcutaneous tumor. Figure 2 H shows the volume differences of xenografts in PLKO.1-shSNRPF#1 (n=5), PLKO.1-shSNRPF#2 (n=5) and the control group (n=5) based on the measurements of the long and short diameters of the tumors; Figure 2 I. After tumor ex vivo, the tumor weight of the xenografts in the PLKO.1-shSNRPF#1 (n=5), PLKO.1-shSNRPF#2 (n=5), and PLKO.1-shNC (n=5) groups was compared.

[0027] Figure 3 The results of the study on the identification of downstream genes regulated by SNRPF and alternative splicing events, among which, Figure 3 A is a heatmap showing differentially expressed genes identified by RNA-seq after transient silencing of SNRPF in SKOV3 cells (screening threshold: |log2FC|≥1 and q value<0.05). Figure 3 B shows the gene ontology enrichment analysis of differentially expressed genes (DEGs) after SNRPF silencing in SKOV3 cells; Figure 3 Summary of variable splicing (AS) events detected in C using rMATS software (filter criteria: |IncLevelDifference|>0.1 and FDR<0.05). Figure 3 D is a Venn diagram showing the intersection of differentially expressed genes downregulated after SNRPF silencing (log2FC≤–0.6, q<0.05) and genes with significant splicing changes; Figure 3 E is... Figure 3 Gene ontology enrichment analysis was performed on the intersection gene set identified in D; Figure 3 F is a comparative analysis of genes whose expression was downregulated after SNRPF silencing and accompanied by significant intron retention events, and a total of 18 candidate genes were identified; Figure 3 G is Figure 3 A heatmap showing the 18 candidate genes identified in F; Figure 3H is the IGV Sashimi diagram, showing the intron retention of the DDX24 transcript in SKOV3 cells transfected with SNRPF siRNA; Figure 3 I demonstrates real-time quantitative PCR detection of DDX24 mRNA levels in fresh frozen ovarian cancer (n=16), normal ovary (n=10), and normal fallopian tube (n=10) tissues; Figure 3 J shows the correlation analysis of SNRPF and DDX24 expression levels in fresh frozen ovarian cancer tissues (n=16) as determined by real-time quantitative PCR; Figure 3 K shows the real-time quantitative PCR detection of DDX24 mRNA levels in SKOV3, HEY, and OVCAR8 cells after SNRPF knockdown; Figure 3 L shows the immunoblotting analysis of DDX24 protein levels in SKOV3, HEY, and OVCAR8 cells after SNRPF knockdown.

[0028] Figure 4 The results of a study on the malignant behavior of DDX24-driven ovarian cancer cells, including... Figure 4 A shows the knockdown efficiency of DDX24-targeting siRNA in SKOV3, HEY, and OVCAR8 cells as verified by immunoblotting. Figure 4 B shows the effect of DDX24 silencing on the proliferation ability of ovarian cancer cells as detected by the MTT assay. Figure 4 C shows the changes in the proportion of ovarian cancer cells in the DNA synthesis phase (S phase) after DDX24 knockdown, as detected by the EdU incorporation assay. Figure 4 D-plate colony formation assay to evaluate the effect of DDX24 deficiency on the clonogenic ability of ovarian cancer cells; Figure 4 E shows the Transwell assay evaluating the inhibitory effect of DDX24 silencing on the migration and invasion of ovarian cancer cells; Figure 4 F shows the MTT assay used to evaluate the proliferation of ovarian cancer cells overexpressing SNRPF with or without DDX24 siRNA co-transfection; Figure 4 G shows clonogenicity and Transwell assays evaluate the clonogenicity and invasiveness of ovarian cancer cells overexpressing SNRPF with or without DDX24 silencing. Figure 4 H shows representative images of subcutaneous xenografts in nude mice injected with ovarian cancer cells overexpressing SNRPF, with or without stable DDX24 silencing (groups: PLVX-shNC, n=5; SNRPF-shNC, n=5; SNRPF-shDDX24, n=5). Figure 4 I am Figure 4 H shows the tumor weight measurement results of the xenograft model; Figure 4 J is Figure 4H shows the tumor volume measurement results of the xenograft model.

[0029] Figure 5 The results of a study on the regulation of alternative splicing of intron 6 of the DDX24 gene by SNRPF in ovarian cancer cells, in which... Figure 5 A is a schematic diagram of the DDX24 gene transcript isoform structure based on the Ensembl database; Figure 5 B is a differential expression analysis of three transcriptomic subtypes, DDX24-001, DDX24-004 and DDX24-005, in ovarian cancer tissues based on transcriptome sequencing data from the TCGA-OV cohort (n=426). Figure 5 C presents the results of differential expression analysis of different transcript subtypes of DDX24 in ovarian cancer cell lines (n=47) from the CCLE database; Figure 5 D shows the comparison results of DDX24-005 transcript expression levels among ovarian cancer (n=426), normal ovary (n=88), and normal fallopian tube (n=5) tissues in the TCGA-OV dataset; Figure 5 E is based on the TCGA pan-cancer dataset and analyzes the differential expression levels of the DDX24-005 transcript in various solid tumors and paired normal tissues. Figure 5 F is a schematic diagram of the primer design strategy for real-time quantitative PCR for the specific detection of DDX24 normal splice type and intron 6 preserved type transcripts; Figure 5 G~ Figure 5 H shows the expression changes of the intron 6 normal splicing (-) subtype DDX24-L and the intron 6 retention (+) subtype DDX24-S in ovarian cancer cells after SNRPF silencing, as detected by real-time quantitative PCR. Figure 5 I demonstrate the effect of SNRPF knockdown on the DDX24-S / DDX24-L subtype ratio in ovarian cancer cells; Figure 5 J demonstrated the use of a mini gene reporter system containing exon 6, truncated intron 6 and exon 7 sequences, which, after co-transfecting ovarian cancer cells with SNRPF-targeting siRNA, showed the change in DDX24 splicing mode by RT-PCR analysis. Figure 5 K is based on Figure 5 The ratio of DDX24-S to DDX24-L transcripts calculated from the J-plot results; Figure 5 L shows the changes in the stability of DDX24 transcripts in ovarian cancer cells after SNRPF knockdown, as detected by the actinomycin D transcriptional repression tracking experiment. Figure 5 M shows the difference in stability between intron 6-retained (DDX24-S) and intron 6-normally spliced ​​(DDX24-L) transcripts after treatment with actinomycin D; Figure 5 N shows the effect of real-time quantitative PCR detection on the homeostasis level of DDX24 mRNA in SNRF-deficient cells; Figure 5 O demonstrates the effect of real-time quantitative PCR on the recovery of DDX24 mRNA abundance in SNRPF-deficient cells by treatment with the NMD pathway inhibitor cycloheximide. Figure 5 P is the detection of the relative abundance of DDX24 mRNA in the enriched product of RNA immunoprecipitation using anti-Flag antibody in ovarian cancer cells overexpressing Flag-SNRPF. Figure 5 Q is a schematic diagram of the construction of DDX24-L and DDX24-S eukaryotic overexpression vectors and the functional rescue experimental procedure: first knock down endogenous DDX24, and then reintroduce the corresponding isotypes to perform the MTT experiment; Figure 5 R demonstrates evaluation of plate cloning experiments. Figure 5 Q: The effect of each treatment group on the clonogenic ability of ovarian cancer cells; Figure 5 S demonstrates Transwell migration experiment evaluation Figure 5 Q: The effect of each treatment group on the migration ability of ovarian cancer cells.

[0030] Figure 6 The results of screening and functional validation of downstream effect factors of DDX24 are as follows, Figure 6 A is a heatmap showing differentially expressed genes identified by RNA sequencing in SKOV3 cells transfected with DDX24-specific siRNA or control siRNA. Figure 6 B is the corresponding Figure 6 The volcano plot visualization results of differentially expressed genes in A; Figure 6 C display is based on Figure 6 Gene ontology enrichment analysis of differentially expressed genes in A; Figure 6 D is a Venn diagram showing the intersection of differentially expressed genes after DDX24 silencing and genes that experienced significant intron retention events (|IncLevelDifference|>0.1, FDR<0.05), identifying a total of 6 candidate downstream genes; Figure 6 E is an analysis of the expression profiles of six candidate genes in ovarian cancer (n=585) and normal ovarian (n=8) tissues based on the TCGA-OV dataset (AffyU133a) (SLC9A3-AS1 data missing). Figure 6 F is a heatmap showing the changes in the expression levels of six candidate genes based on RNA-seq after DDX24 knockdown in SKOV3 cells; Figure 6 G is the IGV Sashimi diagram, showing the retention event of intron 2 of the E2F4 transcript in SKOV3 cells after DDX24 silencing; Figure 6 H shows the mRNA expression level of E2F4 in fresh frozen ovarian cancer (n=16), normal ovary (n=10), and normal fallopian tube (n=10) tissues detected by real-time quantitative PCR. Figure 6I shows the correlation between DDX24 and E2F4 expression in fresh frozen ovarian cancer tissue (n=16) based on real-time quantitative PCR analysis; Figure 6 J demonstrates real-time quantitative PCR detection of E2F4 mRNA expression levels in SKOV3, HEY, and OVCAR8 cells after DDX24 knockdown; Figure 6 K shows the immunoblotting detection of E2F4 protein expression levels in SKOV3, HEY, and OVCAR8 cells after DDX24 knockdown.

[0031] Figure 7 The results of a study on the pro-cancer function of E2F4 in the progression of ovarian cancer, among which, Figure 7 A demonstrates the knockdown efficiency of E2F4 after specific siRNA transfection into ovarian cancer cells, verified by real-time quantitative PCR. Figure 7 B shows the immunoblotting validation of the knockdown efficiency of E2F4 after specific siRNA transfection into ovarian cancer cells. Figure 7 C shows the changes in DNA synthesis capacity of SKOV3, HEY and OVCAR8 cells after transient silencing of E2F4 using the EdU incorporation assay. Figure 7 D shows the Transwell migration and invasion assay to evaluate the motility and invasiveness of ovarian cancer cells after E2F4 knockdown; Figure 7 E-display MTT assay was used to detect the effect of DDX24 overexpression combined with or without E2F4 siRNA co-transfection on the proliferation ability of ovarian cancer cells; Figure 7 F shows a plate colony formation assay to evaluate the effect of DDX24 overexpression with or without E2F4 silencing on the colony-forming ability of ovarian cancer cells; Figure 7 G demonstrated the effects of DDX24 overexpression with or without E2F4 silencing on the migration and invasion capabilities of ovarian cancer cells using Transwell migration and invasion assays.

[0032] Figure 8 The study results show that DDX24 maintains its high expression in ovarian cancer cells by promoting efficient E2F4 splicing. Figure 8 A is a schematic diagram of the E2F4 gene transcript isoforms included in the Ensembl database; Figure 8 B shows the expression levels of the E2F4-001, E2F4-002, and E2F4-013 isoforms in ovarian cancer tissues in the TCGA-OV cohort (n=426); Figure 8 C shows the expression levels of the E2F4-001, E2F4-002, and E2F4-013 isoforms in the CCLE database ovarian cancer cell lines (n=47); Figure 8D shows a comparison of the expression of E2F4-002 and E2F4-013 isoforms in ovarian cancer tissues (n=426), normal ovaries (n=88), and normal fallopian tubes (n=5) in the TCGA-OV cohort; Figure 8 E is a schematic diagram of the primer positions for isoform-specific real-time quantitative PCR used for E2F4 transcript quantification analysis; Figure 8 F shows the real-time quantitative PCR detection of the transcript levels of the normal spliced ​​E2F4-L (intron 2 has been removed) in SKOV3, HEY and OVCAR8 cells after DDX24 knockdown; Figure 8 G demonstrates the real-time quantitative PCR detection of the transcript level of intron 2-retained E2F4-S in ovarian cancer cells after DDX24 knockdown; Figure 8 H is the ratio of the abundance of E2F4-S to E2F4-L transcripts in ovarian cancer cells after DDX24 knockdown; Figure 8 I shows the changes in the expression of E2F4-L and E2F4-S transcripts in ovarian cancer cells after DDX24 knockdown, detected by RT-PCR. Figure 8 J is the ratio of E2F4-S to E2F4-L transcript abundance calculated based on the RT-PCR results in (I); Figure 8 K shows the changes in the stability of E2F4 mRNA in ovarian cancer cells treated with actinomycin D after DDX24 knockdown; Figure 8 L shows a comparison of the stability of the E2F4-L and E2F4-S transcript isoforms after treatment with actinomycin D; Figure 8 M shows the effect of silencing UPF1 on E2F4 mRNA levels in DDX24-deficient ovarian cancer cells as detected by real-time quantitative PCR. Figure 8 N shows the changes in E2F4 mRNA expression levels in DDX24-deficient ovarian cancer cells after treatment with cycloheximide (CHX) using real-time quantitative PCR. Figure 8 O demonstrated the effect of MTT assay on cell proliferation after knockdown of endogenous E2F4, and transfection with pcDNA3.1-control, pcDNA3.1-E2F4-L, or pcDNA3.1-E2F4-S, respectively. Figure 8 P-display plate cloning experiment evaluation Figure 8 O treatment enhances the cell's ability to form clones; Figure 8 Q demonstrates the Transwell migration and invasion experimental evaluation results. Figure 8 The motility and invasive ability of cells after O treatment.

[0033] Figure 9 E2F4 directly activates SNRPF transcription by binding to the promoter, whereby... Figure 9A is E2F4 binding site information predicted in the SNRPF promoter region (-1484 to +45 bp) based on the JASPAR database; Figure 9 B shows the E2F4 predicted binding motif (MA0470) identified using the JASPAR database. Figure 9 C is a ChIP-seq dataset obtained using Cistrome Data Browser, showing the binding peak of E2F4 in the SNRPF promoter region in HeLa-S3, K562, and MCF-7 cells; Figure 9 D shows the correlation analysis of E2F4 and SNRPF expression in fresh frozen ovarian cancer tissue (n=16) detected by real-time quantitative PCR; Figure 9 E is a schematic diagram of the construction of the luciferase reporter gene vector: it contains the wild-type SNRPF promoter region (PGL4.26-SNRPF-WT, -1332 to +45 bp) and the mutant promoter (PGL4.26-SNRPF-MT) which lacks two predicted E2F4 binding sites. Figure 9 F-tests showed changes in promoter activity in HEK293T cells transfected with PGL4.26-SNRPF-WT or PGL4.26-SNRPF-MT vectors, with or without E2F4 silencing. Figure 9 G shows the real-time quantitative PCR detection of SNRPF mRNA expression levels in SKOV3, HEY, and OVCAR8 cells after E2F4 knockdown; Figure 9 H shows the expression level of SNRPF protein in SKOV3, HEY and OVCAR8 cells after E2F4 knockdown, as detected by immunoblotting. Figure 9 I demonstrates the MTT assay for detecting the proliferation capacity of ovarian cancer cells overexpressing E2F4 with or without SNRPF-targeting siRNA co-transfection; Figure 9 J demonstrates the colony-forming ability of ovarian cancer cells treated with (I) above as detected by a plate colony-forming assay; Figure 9 K demonstrates the Transwell migration and invasion assay to detect the motility and invasion capabilities of ovarian cancer cells treated as described in (I).

[0034] Figure 10 The results of a study on ASO-mediated SNRPF silencing inhibition of ovarian cancer cell proliferation include... Figure 10 A shows the real-time quantitative PCR detection of SNRPF mRNA expression levels in SKOV3, HEY, and OVCAR8 cells after SNRPF-targeted ASO treatment; Figure 10 B shows the detection of SNRPF and DDX24 protein expression levels in SKOV3, HEY, and OVCAR8 cells after SNRPF-ASO treatment using Western blotting. Figure 10C shows the MTT assay used to detect the proliferation ability of ovarian cancer cells after treatment with SNRPF-ASO or control ASO; Figure 10 D shows the plate colony formation assay to evaluate the colony formation ability of ovarian cancer cells after SNRPF-ASO treatment; Figure 10 E shows the EdU incorporation assay used to detect the DNA synthesis capacity of ovarian cancer cells after SNRPF-ASO treatment; Figure 10 F demonstrates the Transwell migration and invasion assays used to assess the motility and invasiveness of ovarian cancer cells after SNRPF-ASO treatment; Figure 10 G is a schematic diagram of the cell-derived xenograft model construction: HEY cells were subcutaneously inoculated into NCG mice, followed by intratumoral injection of SNRPF-ASO or ASO-NC (n=5 per group). Figure 10 H is Figure 10 Tumor volume at the experimental endpoint in the CDX model in G; Figure 10 I am Figure 10 Tumor weight at the experimental endpoint in the CDX model in G; Figure 10 J is a representative photograph of tumors resected from PDX models that have undergone SNRPF-ASO (n=5) or ASO-NC (n=5) treatment; Figure 10 K is Figure 10 Tumor volume at the experimental endpoint in the PDX model of J; Figure 10 L is Figure 10 Tumor weight at the experimental endpoint in the PDX model of J. Detailed Implementation

[0035] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0036] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0037] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0038] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0039] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0040] Example 1

[0041] This study investigates the high expression of SNRPF in ovarian cancer tissues and its association with poor patient prognosis. To systematically screen key splicing factors potentially involved in ovarian cancer progression, this part of the study utilizes the GEPIA 3 platform and performs differential expression analysis between ovarian cancer and normal ovarian tissues based on the TCGA-Ovary Cancer (TCGA-OV) dataset (screening threshold: |log2FC|≥0.8, q<0.05).

[0042] A total of 7,156 differentially expressed genes were identified, including 2,837 upregulated transcripts and 4,319 downregulated transcripts. Intersection of the upregulated genes with the KEGG spliceosome pathway gene set yielded 17 upregulated splicing factors, four of which are members of the core Sm protein family: SNRPB, SNRPD1, SNRPF, and SNRPG.

[0043] Expression profiling analysis based on the TCGA-GTEx combined dataset showed that, compared with normal ovarian and fallopian tube tissues, all four Sm proteins exhibited consistently high expression in high-grade serous ovarian cancer (see [link to relevant data]). Figure 1 A and Figure 1 B); CPTAC proteomics data further validated this upregulation trend at the protein level, with the most significant increases in the expression of SNRPF and SNRPG. Given that the expression pattern and functional role of SNRPF in ovarian cancer are still unknown, this study selected SNRPF as the focus of future research.

[0044] A comprehensive analysis of TCGA-GTEx transcriptomics, CPTAC proteomics, and the CSIOVDB database consistently showed that SNRPF was significantly overexpressed in ovarian cancer tissues (see [link to relevant database]). Figure 1 C~ Figure 1 E). FIGO staging analysis showed that the SNRPF expression level in FIGO stage III tumors was significantly higher than that in stage I tumors (see Figure 1). Figure 1 F). According to molecular subtype stratification analysis, SNRPF showed an upregulated trend in proliferative, mesenchymal, immunoreactive, and differentiation subtypes, with the highest expression level in the proliferative subtype (see F). Figure 1 G). Pan-cancer analysis further showed that SNRPF expression trended upregulated in most human malignancies compared to matched normal tissues (see G). Figure 1 H).

[0045] In this part of the study, the unpaired t-test was used. Figure 1 C Figure 1 D、 Figure 1 E, Figure 1 F, Figure 1 G, Figure 1 I and Figure 1 K; Log-rank test is used for Figure 1 M and Figure 1 N. *P<0.05, **P<0.01.

[0046] Example 2

[0047] This study investigated the ability of SNRPF silencing to inhibit the proliferation and metastasis of ovarian cancer cells. To explore the biological function of SNRPF in the development and progression of ovarian cancer, this part of the study used RNA interference technology to downregulate SNRPF expression and systematically evaluated its impact on the malignant phenotype of ovarian cancer cells. Two independently designed SNRPF-targeting siRNAs (the specific gene sequences of the SNRPF-targeting siRNAs are shown in SEQ ID NO.2 and SEQ ID NO.3) were transfected into three ovarian cancer cell lines: SKOV3, HEY, and OVCAR8, respectively, successfully achieving efficient silencing of SNRPF at both the mRNA and protein levels. Real-time quantitative polymerase chain reaction (qPCR) and Western blotting experiments verified the knockdown efficiency (see [link to qPCR]). Figure 2 A and Figure 2 B).

[0048] The siRNA sequences used in this embodiment are as follows: si-NC: UUCUCCGAACGUGUCACGUTT (SEQ ID NO. 1); si-SNRPF#1: ACAAGGGCUAUCUGGUAUCTT (SEQ ID NO. 2); si-SNRPF#2: UGUAAUAAUGUCCUUUAUATT (SEQ ID NO. 3). si-NC is the control siRNA and has no specific target.

[0049] Functional assessment using the MTT assay showed that the loss of SNRPF significantly inhibited the proliferation of ovarian cancer cells (see [link to MTT assay]). Figure 2 C). Consistent with these results, knocking down SNRPF significantly reduced the colony-forming ability of cells (see C). Figure 2 D).

[0050] Furthermore, EdU incorporation experiments revealed that silencing SNRPF in ovarian cancer cells reduced the proportion of cells in the DNA synthesis phase (see...). Figure 2 E). Transwell migration and invasion assays also showed that the migration and invasion abilities of SKOV3, HEY, and OVCAR8 cells were inhibited after SNRPF knockdown (see E). Figure 2 F).

[0051] To validate the above in vitro findings in vivo, this part of the study constructed a subcutaneous xenograft model in nude mice. HEY cells with stably knocked-down SNRPF via shRNA and control cells were inoculated into the axillae of mice, respectively. After 15 days of feeding in a specific pathogen-free environment, the mice were sacrificed and tumor tissue was removed (see...). Figure 2 G). The results showed that the volume and weight of xenografts derived from the SNRPF silencing group were significantly smaller than those from the control group (see G). Figure 2 H~ Figure 2 (I) This further confirms the carcinogenic effect of SNRPF on the in vivo growth of ovarian cancer.

[0052] In summary, the results confirm that SNRPF plays a crucial role in promoting the malignant behavior of ovarian cancer cells. Given the close association between aberrant expression of the spliceosome core components and oncogenic alternative splicing events, future research will focus on elucidating the molecular pathways regulated by SNRPF and the alternative splicing network it mediates, particularly the mechanism of intron-preserving events in the malignant progression of ovarian cancer.

[0053] In this part of the study, all quantitative data were compared between two groups using unpaired t-tests. *P<0.05, **P<0.01.

[0054] Example 3

[0055] This study investigated the identification of downstream genes regulated by SNRPF and alternative splicing events. To elucidate the molecular mechanism by which SNRPF exerts its oncogenic effect in ovarian cancer, RNA sequencing was performed on SKOV3 cells with SNRPF knocked down by siRNA and cells transfected with control siRNA (the specific gene sequence of the control siRNA is shown in SEQ ID NO. 1). Differential expression analysis identified 1,215 differentially expressed genes, including 772 genes upregulated and 443 genes downregulated after SNRPF silencing (screening criteria: |log2FC|≥1, q<0.05). Figure 3 The heatmap for A illustrates the overall expression pattern. Gene ontology enrichment analysis revealed that these genes are closely related to biological processes such as chromosome organization, sister chromatid separation, nuclear chromosome separation, mitotic cell cycle progression, and DNA replication initiation (see [link to heatmap]). Figure 3 (B) This suggests that SNRPF may promote tumor progression by regulating mitosis and DNA replication pathways.

[0056] Then, the effect of SNRPF silencing on alternative splicing was evaluated using rMATS (screening criteria: |IncLevelDifference|>0.1, FDR<0.05). A total of 1,204 differential splicing events were detected, with exon skipping accounting for the majority (56.06%), followed by intron retention (21.51%), mutually exclusive exons (5.48%), and other types (16.95%) (see [link to relevant documentation]). Figure 3 C). To identify genes simultaneously affected by transcriptional downregulation and splicing alterations, the intersection of differentially expressed downregulated genes (log2FC ≤ –0.6, q < 0.05) and genes exhibiting significant splicing alterations was used to screen for 79 candidate downstream targets (see [link to C]). Figure 3 D). Gene ontology analysis of these genes showed that they are mainly enriched in processes such as DNA repair, cell division, proliferation, double-strand break repair, chromosome segregation, and condensation (see D). Figure 3 E).

[0057] Given that aberrant intron retention is known to promote tumorigenesis by disrupting tumor suppressor function, activating oncogenes, and altering cell proliferation, this study focused its analysis on genes related to intron retention. By intersecting differentially expressed genes downregulated with those showing significant changes in intron retention after SNRPF knockdown, 18 candidate genes were identified (see...). Figure 3 (F); among them, DDX24 is a noteworthy candidate molecule due to its significant intron retention alterations and its known roles in RNA metabolism and cancer biology. Figure 3 G shows the expression profiles of these 18 genes after SNRPF silencing.

[0058] The Sashimi plot generated by IGV was used to verify the intron retention event, confirming that the DDX24 transcript exhibited significant intron 6 retention in SNRPF-knockdown SKOV3 cells (see [link to SKOV3 transcript]). Figure 3 H). Real-time quantitative PCR analysis of clinical samples showed that DDX24 levels were significantly elevated in ovarian cancer tissue compared to normal ovaries and fallopian tube controls (see H). Figure 3 I); Correlation analysis showed a strong positive correlation between SNRPF and DDX24 expression (correlation coefficient 0.6662) (see I). Figure 3 J).

[0059] Finally, this part of the study confirms that inhibiting SNRPF reduces the mRNA and protein expression levels of DDX24 in SKOV3, HEY, and OVCAR8 cells (see [link to study]). Figure 3 K~ Figure 3 This result identifies DDX24 as a key downstream effector of SNRPF in ovarian cancer, and its mechanism may be achieved through intron-preserved transcriptomic regulation.

[0060] In this study, all quantitative data were compared between two groups using unpaired t-tests. *P<0.05, **P<0.01.

[0061] Example 4

[0062] This study investigated the malignant behavior of ovarian cancer cells driven by DDX24. To explore the role of DDX24 in the proliferation and metastasis of ovarian cancer cells, a series of in vivo and in vitro experiments were conducted. DDX24-specific siRNAs (the specific gene sequences of which are shown in SEQ ID NO.4 and SEQ ID NO.5) were used to transiently reduce their expression levels in ovarian cancer cell lines (see [link to study]). Figure 4 A). Cell proliferation experiments showed that knockdown of DDX24 significantly inhibited the growth of ovarian cancer cells (see...). Figure 4 B). EdU incorporation experiments further revealed a significant decrease in the proportion of cells during DNA synthesis (see B). Figure 4 C). Consistent with this, clonogenic assays showed that silencing DDX24 significantly reduced the clonogenic capacity of cells (see C). Figure 4 D). Furthermore, Transwell experiments further confirmed that knocking down DDX24 weakened the migration and invasion abilities of these ovarian cancer cells (see D). Figure 4 E).

[0063] The siRNA sequences used in this embodiment are as follows: si-DDX24#1: UGACAAACUGGACAUCCUUTT (SEQ ID NO. 4); si-DDX24#2: CACGUACCUCGGAGAUUUATT (SEQ ID NO. 5).

[0064] To determine whether DDX24 mediates SNRPF-driven proliferative and colony-forming effects, this part of the study involved transfecting DDX24 siRNA into ectopic SNRPF-overexpressing ovarian cancer cells. Deletion of DDX24 attenuated the enhanced growth, colony formation, and metastatic ability induced by SNRPF overexpression (see [link to study]). Figure 4 F and Figure 4 G).

[0065] Furthermore, salvage experiments using a subcutaneous tumorigenesis model showed that inhibiting DDX24 expression could partially reverse the tumor growth advantage driven by SNRPF overexpression (see...). Figure 4 H~ Figure 4 J).

[0066] In summary, this study confirms that DDX24, as a key downstream functional molecule of SNRPF, drives the malignant progression of ovarian cancer by promoting cell proliferation and metastasis.

[0067] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0068] Example 5

[0069] This study investigates the alternative splicing of intron 6 in the DDX24 gene regulated by SNRPF in ovarian cancer cells. To further elucidate the potential mechanism of intron 6 retention in DDX24, this section analyzes the annotated isotypes in the Ensembl database. The protein-coding isotypes DDX24-001 (ENST00000330836, encoding 859 amino acids) and DDX24-004 (ENST00000555054, encoding 816 amino acids) do not contain the intron 6 sequence; however, DDX24-005 (ENST00000555762) retains the intron completely, thus introducing a premature stop codon, theoretically preventing it from encoding a full-length functional protein (see...). Figure 5 A).

[0070] TCGA-OV transcriptome data showed that in ovarian cancer tissues, the expression levels of protein-coding isoforms DDX24-001 and DDX24-004 were significantly higher than those of the intron-preserving transcript DDX24-005 (see [link to TCGA-OV transcriptome data]). Figure 5B). CCLE database analysis confirmed that DDX24-001 is the most abundant subtype expressed among various ovarian cancer cell lines (see [link]). Figure 5 C). Furthermore, DDX24-005 was significantly downregulated in ovarian cancer tissue compared to normal ovarian and fallopian tube samples (see [link to relevant documentation]). Figure 5 D), this expression pattern remained consistent across multiple tumor types in pan-cancer analysis (see D). Figure 5 E).

[0071] To experimentally assess the changes in subtype specificity, real-time quantitative PCR primers spanning the exon 6-exon 7 junction / exon 7 were designed (for detecting the normally spliced ​​long subtype DDX24-L; the specific gene sequences of the DDX24-L detection primers are shown in SEQ ID NO. 6 and SEQ ID NO. 7), and primers targeting intron-6 retention were designed (for detecting the intron-retaining short subtype DDX24-S; the specific gene sequences of the DDX24-S detection primers are shown in SEQ ID NO. 8 and SEQ ID NO. 9) (see [link to PCR results]). Figure 5 F). SNRPF knockdown significantly reduced the level of DDX24-L, i.e., intron 6 retention (-), while increasing the abundance of DDX24-S, i.e., intron retention (+) (see F). Figure 5 G~ Figure 5 I), leading to an increased DDX24-S / DDX24-L ratio. A mini-gene (the specific gene sequence of the DDX24 mini-gene construction is shown in SEQ ID NO. 10) was constructed using exon 6, a truncated intron 6, and exon 7. RT-PCR experiments further confirmed that SNRPF deletion drives a shift in the DDX24 splicing mode from normal splicing to intron retention (see [link to RT-PCR]). Figure 5 (J-Figure K). The C-terminus of the DDX24 protein contains a highly conserved helicase domain (Helicase_C), essential for the ATP-dependent RNA binding and unwinding activity of typical DEAD-box helicases. This domain is encoded by a portion of exon 5, the complete exon 6, and a portion of exon 7. SNRPF silencing-induced intron 6 retention results in an interruption of the coding sequence of this domain and the introduction of a premature stop codon, thereby directly disrupting the integrity of the DDX24 helicase functional domain.

[0072] The sequences used in this part of the embodiments are as follows:

[0073] DDX24-LF: ATCCATTACCAGGTCCCACG (SEQ ID NO. 6), DDX24-LR: ATCACATCCTCAGGCCCAAT (SEQ ID NO. 7);

[0074] DDX24-S-F:GCTTTGAATCCCCAGTCTGC(SEQ ID NO. 8),DDX24-S-R:GTACTTACACTGTGCTACCGG(SEQ ID NO. 9);

[0075]

[0076] Actinomycin D tracking experiments showed that SNRPF knockdown reduced the stability of DDX24 transcripts, with DDX24-S showing significantly lower stability than DDX24-L (see [link to study]. Figure 5 L~ Figure 5 M). Since intron 6 retention introduces a premature stop codon, it is speculated that these aberrant transcripts will be degraded via a nonsense-mediated mRNA degradation pathway. Experiments confirmed that silencing the key UPF1 effectively restored the overall expression level of DDX24 in SNRPF-deficient cells (see [link to study]. Figure 5 N), and treatment with the NMD-specific inhibitor cyclohexylimide can also significantly increase the homeostatic abundance of DDX24 transcripts (see N). Figure 5 To determine whether SNRPF interacts with the DDX24 transcript, RNA immunoprecipitation was performed in cells overexpressing SNRPF using an anti-Flag antibody. The results showed that the anti-Flag antibody enriched the complex with significantly enriched DDX24 mRNA (see Figure 5P).

[0077] Given the differences in stability and coding potential between these two isoforms, this study also constructed the pcDNA3.1-DDX24-L and pcDNA3.1-DDX24-S vectors. After silencing endogenous DDX24, overexpression of DDX24-L significantly promoted cell proliferation and migration, while DDX24-S showed extremely low oncogenic activity (see...). Figure 5 Q~ Figure 5 S).

[0078] In summary, these findings confirm that SNRPF ensures proper splicing of DDX24 intron 6 to maintain the production of the oncogenic protein-coding isoform. Loss of SNRPF leads to a splicing shift towards intron retention, generating unstable non-coding transcripts that are degraded via the NMD pathway, thereby impairing the function of pro-tumorigenic DDX24 in ovarian cancer.

[0079] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0080] Example 6

[0081] This embodiment screened and validated the functions of downstream effectors of DDX24. To further elucidate the molecular mechanism by which DDX24 promotes the malignant progression of ovarian cancer, this part of the study performed transcriptome sequencing on SKOV3 cells transfected with DDX24-targeting siRNA and control siRNA. Using |log2FC|≥1 and q<0.05 as thresholds, a total of 440 differentially expressed genes were identified, of which 225 were upregulated and 215 were downregulated (see...). Figure 6 A and Figure 6 B). Gene ontology enrichment analysis showed that these differentially expressed genes are involved in key biological processes such as signal transduction, cell proliferation and cell cycle regulation, DNA damage response, cell migration and growth, and regulation of the PI3K-PKB / Akt signaling pathway (see B). Figure 6 C).

[0082] Given the growing interest in the regulatory role of intron retention in tumorigenesis and progression, this study intersected differentially expressed genes with genes showing significant alterations in intron retention after DDX24 knockdown (|Inclusion Level Difference|>0.1, FDR<0.05), identifying six candidate genes that may be regulated by DDX24 through aberrant intron retention (see...). Figure 6 D). Comparative analysis based on TCGA-OV transcriptome data showed that E2F4, HDAC3, and DDIT3 were upregulated in ovarian cancer tissues, OVGP1 was downregulated, BECN1 showed no significant difference, and SLC9A3-AS1 was missing information in the dataset (see [link to dataset]). Figure 6 E). RNA-seq data heatmaps summarize the differential expression of these candidate genes after DDX24 knockdown (see...). Figure 6 F).

[0083] Among the candidate genes, E2F4 was selected as a key target for in-depth research due to its central role in cell cycle regulation and its previously reported oncogenic function. IGV Sashimi diagram visualization analysis showed that DDX24 knockdown resulted in significant retention of intron 2 of the E2F4 transcript (see...). Figure 6 (G) suggests that DDX24 may be involved in the splicing regulation of E2F4 precursor mRNA. Real-time quantitative PCR results based on fresh frozen clinical tissues showed that, compared to normal ovarian and fallopian tube tissues, the level of E2F4 mRNA was significantly increased in ovarian cancer tissues (see G). Figure 6 Correlation analysis further revealed a strong positive correlation between the expression levels of DDX24 and E2F4 in ovarian cancer samples (correlation coefficient 0.6871, see H). Figure 6 I).

[0084] To verify the dependence of DDX24 on E2F4 expression regulation, this study silenced DDX24 in ovarian cancer cells and used real-time quantitative PCR and Western blotting to detect E2F4 expression levels. The results showed that DDX24 deficiency significantly downregulated E2F4 mRNA and protein levels (see...). Figure 6 J~ Figure 6 (K), further supporting the scientific hypothesis that DDX24 maintains E2F4 expression by regulating intron splicing.

[0085] In summary, this study is the first to systematically demonstrate that E2F4 is a key downstream effector molecule of DDX24 in ovarian cancer, and further expands the scientific connotation of the SNRPF-DDX24-E2F4 regulatory axis: SNRPF maintains the expression of DDX24 by ensuring the correct splicing of the DDX24 transcript, while DDX24 maintains the expression level of E2F4 by regulating the intron 2 retention state. The two work together to drive the malignant progression of ovarian cancer.

[0086] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0087] Example 7

[0088] This study investigated the pro-cancer function of E2F4 in ovarian cancer progression. To explore the biological function of E2F4 in the proliferation and metastasis of ovarian cancer cells, this study used specific siRNAs (the specific gene sequences of the specific siRNAs are shown in SEQ ID NO. 11 and SEQ ID NO. 12) to transiently silence E2F4 expression in various ovarian cancer cell lines (see [link to study]). Figure 7 A and Figure 7 B). Cell proliferation assays showed that E2F4 deficiency significantly inhibited the growth of ovarian cancer cells. Consistent with this, plate colony formation assays indicated that E2F4 knockdown significantly reduced cell colony formation ability. EdU incorporation assays further confirmed that E2F4 silencing significantly reduced the proportion of cells in the DNA synthesis phase in SKOV3, HEY, and OVCAR8 cells (see [link to assay]). Figure 7 C). Furthermore, Transwell assays showed that downregulation of E2F4 expression significantly weakened the migration and invasion abilities of the aforementioned cells (see [link to Transwell assay]). Figure 7 D).

[0089] The siRNA sequences used in this embodiment are as follows: si-E2F4#1: GAUUUACGACAUUACCAAUTT (SEQ ID NO. 11); si-E2F4#2: AAGAACUAGACCAGCACAATT (SEQ ID NO. 12).

[0090] To clarify whether E2F4 acts as a downstream effector molecule of DDX24 to mediate its oncogenic function, this study conducted a functional rescue experiment by introducing E2F4-targeting siRNA into DDX24-overexpressing ovarian cancer cells. The results showed that E2F4 silencing significantly attenuated the accelerated cell proliferation, enhanced colony formation, and increased metastatic potential induced by DDX24 overexpression (see...). Figure 7 E~ Figure 7 G).

[0091] In summary, this study confirms that E2F4 plays a key driving role in the malignant progression of ovarian cancer by promoting cell proliferation, colony formation, and metastasis, and is an important functional molecule in the downstream signaling pathway of DDX24.

[0092] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0093] Example 8

[0094] This study investigates how DDX24 maintains high expression of E2F4 in ovarian cancer cells by promoting efficient splicing of E2F4. To elucidate the molecular mechanism by which DDX24 regulates the retention of intron 2 in the E2F4 transcript, this part of the study first systematically annotates transcript isoforms of the E2F4 gene based on the Ensembl database. The protein-coding transcript E2F4-001 (ENST00000379378) encodes a full-length functional protein of 413 amino acids, while E2F4-002 (ENST00000567007) and E2F4-013 (ENST00000561904) both fully retain intron 2 and introduce an early stop codon, thus losing their protein-coding potential (see...). Figure 8 A).

[0095] Transcriptome analysis of TCGA-OV data showed that the expression abundance of the protein-coding E2F4-001 in ovarian cancer tissues was significantly higher than that of the intron-conserving transcripts E2F4-002 and E2F4-013 (see Transcriptome). Figure 8 B). The CCLE database's ovarian cancer cell line expression profiles further confirm that E2F4-001 is the dominant expression subtype (see [link]). Figure 8 C). Furthermore, compared to normal fallopian tube and ovarian tissue, the expression levels of E2F4-002 and E2F4-013 were significantly downregulated in ovarian cancer tissue (see [link]). Figure 8 D). The above results indicate that the protein-coding E2F4-001 transcript is the predominant form found in ovarian cancer.

[0096] To quantitatively assess the impact of DDX24 deletion on E2F4 isotype switching, this study designed isotype-specific real-time quantitative PCR primers: For the normally spliced, intron-2-deficient E2F4 long isotype (E2F4-L, IR⁻), the upstream primer spans the exon 2–exon 3 splicing junction, and the downstream primer is located in exon 3 (the specific gene sequences of the E2F4-L detection primers are shown in SEQ ID NO. 13 and SEQ ID NO. 14); for the intron-2-preserved E2F4 short isotype (E2F4-S, IR⁺), the upstream primer is anchored to the preserved intron 2 sequence, and the downstream primer is located in exon 3 (the specific gene sequences of the E2F4-S detection primers are shown in SEQ ID NO. 15 and SEQ ID NO. 16). Furthermore, to assess the splicing status of E2F4 intron 2 after DDX24 knockdown via RT-PCR, upstream and downstream primers targeting exon 2 and exon 3 were designed respectively. The design strategies for each primer pair are illustrated as follows: Figure 8 As shown in E.

[0097] The sequences used in this part of the embodiments are as follows:

[0098] E2F4-LF: CATCCAGTGGAAGGGTGTGG (SEQ ID NO. 13), E2F4-LR: CTGCAGCTCCTCGATCTCTG (SEQ ID NO. 14);

[0099] E2F4-SF: TGGGCATAGTGGGAGGGTAG (SEQ ID NO. 15), E2F4-SR: CCTCGATCTCTGCCTTGAGC (SEQ ID NO. 16).

[0100] DDX24 silencing significantly downregulated E2F4-L abundance while slightly increasing E2F4-S levels (see...) Figure 8 F~ Figure 8 G), leading to a significant increase in the E2F4-S / E2F4-L isotype ratio (see G). Figure 8 H). RT-PCR further confirmed this splicing mode shift: DDX24 knockdown weakened the E2F4-L band and enhanced the E2F4-S band (see H). Figure 8 I), the E2F4-S / E2F4-L ratio increased significantly (see I), Figure 8 J). The above results suggest that the loss of DDX24 impairs the normal splicing fidelity of E2F4 and drives the splicing mode to shift towards intron retention.

[0101] Given that intron retention events often affect transcript stability via nonsense-mediated mRNA degradation pathways, this study used actinomycin D transcriptional repression tracking assays to assess the half-life of E2F4 mRNA. DDX24 knockdown significantly shortened the overall stability of E2F4 transcripts, and the decay rate of the E2F4-S isoform was significantly faster than that of the E2F4-L isoform (see [link to study].) Figure 8 K~ Figure 8 Consistent with the NMD pathway-mediated degradation mechanism, silencing the core NMD factor UPF1 or treatment with the NMD-specific inhibitor cyclohexylimide effectively restored the expression level of the E2F4 transcript in DDX24-deficient cells (see L). Figure 8 M~ Figure 8 N).

[0102] To clarify the differences in biological function between the two subtypes, this part of the study also constructed eukaryotic overexpression vectors for E2F4-L and E2F4-S, respectively. Under endogenous E2F4 knockdown, E2F4-L reinjection significantly restored the proliferation and metastasis of ovarian cancer cells, while E2F4-S only exhibited very weak pro-cancer activity (see...). Figure 8 O~ Figure 8 Q).

[0103] In summary, this study confirms that DDX24 maintains the dominant expression of the protein-coding E2F4-L isoform by ensuring efficient and precise splicing of E2F4 intron 2, thereby driving the malignant phenotype of ovarian cancer cells. DDX24 deletion leads to a splicing pattern shift towards intron retention, generating non-coding transcripts targeting the NMD pathway, which in turn weakens E2F4-mediated oncogenic signaling output. These findings establish E2F4 as a key splice-dependent functional node in the SNRPF-DDX24-E2F4 regulatory axis, providing a complete molecular analysis of the hierarchical regulatory network driving ovarian cancer progression through this axis.

[0104] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0105] Example 9

[0106] This study investigates how E2F4 directly activates SNRPF transcription by binding to its promoter. Example 8 has already demonstrated that DDX24 maintains high expression of E2F4 in ovarian cancer by ensuring proper splicing of the E2F4 transcript. Given the classic function of E2F4 as a transcription factor, this study further hypothesizes that E2F4 may directly regulate SNRPF transcription, thereby forming a positive feedback regulatory loop driving ovarian cancer progression.

[0107] To verify this hypothesis, this part of the study first used the JASPAR database to scan the transcription factor binding motifs in the SNRPF promoter region (-1484 to +45 bp), discovering two potential E2F4 binding sites (MA0470 motif) (see...). Figure 9 A~ Figure 9 B). To obtain functional evidence, further analysis was performed on E2F4 ChIP-seq datasets from multiple cell lines in the Cistrome Data Browser. The results showed that significant E2F4 enrichment peaks were present in the SNRPF promoter region in HeLa-S3, K562, and MCF-7 cells (see B). Figure 9 C). The above bioinformatics analysis strongly suggests a direct regulatory link between E2F4 and the SNRPF promoter. Consistent with this, correlation analysis in clinical ovarian cancer samples showed a strong positive correlation between E2F4 and SNRPF expression levels (r=0.6730) (see [link to relevant documentation]). Figure 9 D).

[0108] To experimentally verify whether E2F4 regulates SNRPF transcription by directly binding to the promoter, this study constructed two luciferase reporter gene vectors: one containing a wild-type SNRPF promoter fragment (PGL4.26-SNRPF-WT, -1332 to +45 bp) with two predicted E2F4 binding sites (the specific gene sequences of the SNRPF promoter are shown in SEQ ID NO.17 and SEQ ID NO.18); the other being a mutant promoter vector lacking the two binding sites (PGL4.26-SNRPF-MT) (see...). Figure 9 E). Dual-luciferase reporter gene assays in HEK293T cells showed that silencing E2F4 significantly inhibited transcriptional activity of the wild-type promoter, while the activity of the mutant promoter was only slightly affected (see [link to study]). Figure 9 F). This result confirms that E2F4 can directly activate SNRPF transcription by specifically binding to the promoter motif. Subsequently, this regulatory relationship was further verified in ovarian cancer cells: knockdown of E2F4 using siRNA significantly downregulated the mRNA and protein expression levels of SNRPF (see F). Figure 9 G and Figure 9 H).

[0109] The sequences used in this part of the embodiments are as follows:

[0110] PGL4.26-SNRPF-WT:

[0111] TAATTTCAAATGCACGTATGAAGGCCGTGCGGGTTTCTGCAGGCGAAGCAAAAGAAAGGTGGGGCCGGCAGGGTCGTGGGGCGGGGGTGGGGCATGACACCGTATCCCAGACGCAACTCGACCTCGTTTGCAAGAAATTCTGTATCCAACTCTCTTCAGTGCGTTTCGCCGCCCCTAGTCTAGTCTGGCTAGCCCCCAATTCCTTTAAAAGGGGTTAAAACGGGTGTTGCAGAAGAGAGAACACTCAGGTAGAAAGGGCCTGCGGCAGAGGCGCCCCCAAATCATGGAACCTGTTGGAAGAGTGAGGAAGGAACTCTGGGACCCCGAACATTCTCACGGGTGTAAAAAGACAAAGTAAATCCCCATTAGCCAAGTTGGGGAGAAGTCACATCCGTTACACAGCCTCTCCCAGACCGCCCTCTAGGAAGGACACCACATTTAAAAACACCGGCCTGCCTCCAGTCCTTGGCTGCCTCACAGCGATGCTCAGAGCCGGTTTCCCAAGGTCCGCGCGCCGCCTGCAAGGCGCAAACCCAAGCCGCTGCTGGTTGCTAGGAGATACTGGGCCAGCCATCTCCTCCAATCAGCACGCACCTCTTTGCGTCCAATCACAGAGGCAGGAAGGGCCGTGGTGGGAGGTGAAAGGTCATAGTCCTGTTTGGCGGCCATTTCTCTTGAAACTGCGGCTCGGGACCTGCGG

[0112] (SEQ ID NO. 17);

[0113] PGL4.26-SNRPF-MT: (SEQ ID NO. 18).

[0114] To clarify whether SNRPF is a key downstream effector molecule of E2F4's oncogenic function, this study also conducted functional rescue experiments. Overexpression of E2F4 significantly enhanced the malignant phenotype of ovarian cancer cells, while co-transfection with SNRPF-targeting siRNA significantly attenuated the oncogenic effect induced by E2F4 overexpression (see...). Figure 9 I~ Figure 9 K).

[0115] In summary, this study confirms that E2F4 drives the malignant progression of ovarian cancer, at least in part, by directly binding to and activating the SNRPF promoter. This finding, together with the aforementioned SNRPF–DDX24–E2F4 regulatory axis, constitutes a complete positive feedback loop locked between splicing events and transcriptional regulation, providing a new theoretical model for elucidating the molecular network of ovarian cancer progression.

[0116] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0117] Example 10

[0118] This study investigated the inhibitory effect of antisense oligonucleotides (ASOs) on SNRPF silencing on ovarian cancer cell proliferation. ASOs offer a promising therapeutic strategy for targeting proteins and non-coding RNAs that are difficult to treat with conventional therapies. To evaluate the therapeutic potential of silencing SNRPF, this part of the study treated ovarian cancer cell lines SKOV3, HEY, and OVCAR8 with SNRPF-specific ASOs synthesized by GenePharma (the specific gene sequences of the SNRPF-ASOs are shown in SEQ ID NO. 20 and SEQ ID NO. 21). Real-time quantitative PCR results confirmed that SNRPF mRNA levels were significantly reduced after ASO treatment (see [link to study].) Figure 10 A); meanwhile, immunoblotting showed that the abundance of SNRPF protein was also correspondingly downregulated (see A). Figure 10 B). Consistent with the molecular mechanism proposed in this study, SNRPF-ASO treatment also led to a decrease in DDX24 protein levels (see B). Figure 10 B).

[0119] The ASO sequence used in this section of the embodiments is as follows:

[0120] ASO-NC: GCGUATTATAGCCGATTAAC (SEQ ID NO.19);

[0121] ASO#1: TAGTCATTGAGGAAAGGTT (SEQ ID NO. 20);

[0122] ASO #2: ATTTGCAAGCTGCATGTTCA (SEQ ID NO. 21).

[0123] ASO-NC is a nonsense control sequence.

[0124] Functional experiments showed that SNRPF silencing significantly inhibited the proliferation and colony formation abilities of ovarian cancer cells (see...). Figure 10 C~ Figure 10E), and significantly weakened the cell migration and invasion capabilities detected by Transwell assays (see E). Figure 10 F). To evaluate its antitumor effect in vivo, a HEY cell-derived xenograft model was established in immunodeficient NCG mice. Intratumoral injection of SNRPF-ASO significantly inhibited tumor growth compared to control ASO, as evidenced by a significant reduction in tumor volume and wet weight (see F). Figure 10 G~ Figure 10 I). Similar tumor-suppressing effects were also validated in patient-derived xenograft models: SNRPF-ASO treatment significantly reduced both tumor volume and weight (see [link]). Figure 10 J~Figure L).

[0125] The above in vitro and in vivo experimental results together demonstrate that ASO-mediated SNRPF knockdown can effectively inhibit the proliferation and metastatic potential of ovarian cancer cells, proving that SNRPF is a very promising therapeutic target for ovarian cancer.

[0126] In this part of the study, unpaired t-tests were used to compare all quantitative data between two groups. *P<0.05, **P<0.01.

[0127] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. The application of the SNRPF-DDX24-E2F4 loop in the preparation of ovarian cancer drugs or ovarian cancer prognostic assessment products, characterized in that, The SNRPF-DDX24-E2F4 loop can be used as a drug target to prepare drugs for the treatment of ovarian cancer, and / or as a biomarker to prepare products for the prognostic assessment of ovarian cancer.

2. The application according to claim 1, characterized in that, The SNRPF-DDX24-E2F4 loop refers to a self-reinforcing positive feedback regulatory loop composed of SNRPF, DDX24, and E2F4. SNRPF maintains the expression of the DDX24 protein-coding transcript by regulating the correct splicing of intron 6 of the DDX24 gene. DDX24 maintains the expression of the E2F4 protein-coding transcript by regulating the correct splicing of intron 2 of the E2F4 gene. E2F4, as a transcription factor, directly binds to the SNRPF promoter and activates its transcription, forming a closed positive feedback regulatory loop.

3. The application according to claim 1, characterized in that, The use of the drug target as a drug target for the preparation of drugs for treating ovarian cancer refers to the preparation of a reagent that interferes with the integrity of the positive feedback loop of the SNRPF-DDX24-E2F4, and the application of the reagent in the preparation of drugs for treating ovarian cancer.

4. The application according to claim 3, characterized in that, The reagent for ensuring the integrity of the SNRPF-DDX24-E2F4 positive feedback loop is selected from any one or more of the following: reagents that inhibit SNRPF gene expression, reagents that inhibit DDX24 gene expression, or reagents that inhibit E2F4 gene expression.

5. The application according to claim 4, characterized in that, The agents used to inhibit SNRPF gene expression include antisense oligonucleotides or small interfering RNA.

6. The application according to claim 4, characterized in that, The reagent used to inhibit DDX24 gene expression includes small interfering RNA.

7. The application according to claim 4, characterized in that, The reagents used to inhibit E2F4 gene expression include small interfering RNA.

8. The application according to claim 1, characterized in that, The product used as a biomarker for ovarian cancer prognostic assessment is achieved by detecting the expression level of the SNRPF gene or protein; wherein, SNRPF serves as a representative biomarker of the SNRPF-DDX24-E2F4 loop, and its expression level reflects the activation state of the loop.

9. The application according to claim 8, characterized in that, The detection of the expression level of the SNRPF gene or protein is achieved by real-time quantitative PCR or immunohistochemistry.

10. The application according to claim 8, characterized in that, The activation status of the SNRPF-DDX24-E2F4 loop is detected by detecting the expression level of the SNRPF gene or protein. The expression level of SNRPF in ovarian cancer tissue is significantly higher than that in normal ovarian or fallopian tube tissue, and high SNRPF expression is significantly associated with shortened overall survival and progression-free survival.

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