Abstract

   Gastric cancer (GC) ranks as the fifth most common cancer and the
   fourth leading cause of cancer-related deaths globally. Despite
   advancements in molecular profiling, the mechanisms driving GC
   proliferation and metastasis remain unclear. This study identifies
   Early 2 Factor 4 (E2F4) as a key transcription factor that promotes GC
   cell proliferation, migration, and invasion by upregulating DNA
   Replication and Sister Chromatid Cohesion 1 (DSCC1) expression.
   Bioinformatics and transcription factor analyses revealed E2F4 as a
   significant regulator of DSCC1. Functional assays confirmed E2F4's role
   in enhancing GC cell malignancy in vitro and in vivo. Knockdown and
   overexpression experiments demonstrated that E2F4 positively regulates
   DSCC1 at the transcriptional level, with ChIP-qPCR and dual luciferase
   reporter assays validating the binding sites on the DSCC1 promoter.
   These findings highlight the E2F4-DSCC1 axis as a potential therapeutic
   target to mitigate GC progression.

   Keywords: Gastric Cancer, DNA Replication and Sister Chromatid Cohesion
   1, Early 2 Factor 4, transcription, Proliferation

Introduction

   Gastric cancer (GC) is a profoundly deadly disease on a global scale,
   standing as the fifth most commonly detected cancer and the fourth
   leading contributor to cancer-related mortality internationally[47]^1.
   In recent times, advancements in high-throughput and genomic
   technologies have enabled the study of gastric cancers at the molecular
   level. The availability of molecular profiling data has significantly
   aided in pinpointing potential gene driver alterations in GC, including
   gene mutations, chromosomal changes, alterations in transcription
   patterns, and irregularities in epigenetic modifications[48]^2^-[49]^4.
   Therefore, comprehending the process of GC metastasis and pinpointing
   molecular markers and therapeutic targets associated with metastasis
   becomes imperative.

   Cell proliferation, migration, and invasion are essential biological
   processes vital for both normal physiological functions and disease
   states such as cancer. These dynamic cellular activities are integral
   to tissue development, wound healing, immune responses, and the spread
   of cancerous cells. When these processes become dysregulated, they fuel
   tumor progression and metastasis across various cancer types, rendering
   them attractive targets for therapeutic intervention[50]^5^-[51]^7. A
   comprehensive understanding of the molecular mechanisms governing these
   cellular behaviors is crucial for the development of effective cancer
   treatments. Multiple signaling pathways and molecular components
   participate in orchestrating these processes, including those governing
   cell cycle regulation, epithelial-mesenchymal transition (EMT), and
   remodeling of the extracellular matrix (ECM)[52]^8^-[53]^10.
   Investigating the intricate interplay between these pathways and their
   perturbations in cancer cells provides valuable insights into cancer
   biology and unveils potential therapeutic avenues.

   DNA Replication and Sister Chromatid Cohesion 1 (DSCC1, also referred
   to as DCC1) is a key molecular component in the formation of the
   Chromosome Transmission-Fidelity Protein 18 (CTF18)-DSCC1-CTF8
   (CTF18-1-8) module. DSCC1 is primarily localized within the nucleus of
   cells[54]^11. It exhibits a strong correlation with the proliferation
   and metastasis of colon or prostate cancer cells[55]^12^-[56]^14. This
   protein complex is instrumental in various cellular processes including
   sister chromatid cohesion, DNA replication, spindle checkpoints, DNA
   repair, and maintenance of genome stability during the S phase of the
   cell cycle. Numerous studies have highlighted its role in promoting
   cell proliferation and its association with unfavourable prognosis in
   hepatocellular carcinoma[57]^15^-[58]^17. However, no research has yet
   explored the involvement of DSCC1 in gastric cancer or the significance
   of molecular systems in this context.

   Early 2 Factor 4 (E2F4) gene encodes a transcription factor crucial for
   regulating cell cycle advancement and proliferation. As a member of the
   E2F family, E2F4 intricately manages genes vital for DNA synthesis and
   cell division[59]^18. Its aberrant activity has been linked to diverse
   human cancers like breast, lung, and bladder cancers[60]^19^-[61]^21.
   Understanding E2F4's function and regulation is pivotal for deciphering
   the molecular intricacies of tumorigenesis and devising targeted cancer
   treatments. Nevertheless, the association of E2F4 with GC and its
   impact on GC cell proliferation have seldom been explored in research.

   In this research, the relationship between DSCC1 and the proliferation
   of GC cells was investigated. E2F4 was identified as a transcription
   factor regulating DSCC1 expression. It was validated that E2F4
   activated the cell cycle pathway in a DSCC1-dependent manner,
   consequently amplifying the proliferation, migration, and invasion
   capacities of GC cells.

Materials and Methods

Bioinformatic analysis

   To identify genes associated with GC in humans, we searched the GEO
   database (www.ncbi.nlm.nih.gov/geo/) for expression profiling by array
   datasets. We retrieved five microarray datasets ([62]GSE103236,
   [63]GSE118916, [64]GSE79973, [65]GSE112369, and [66]GSE26899) for
   analysis. The affy package in R was utilized for microarray dataset
   normalization and background noise removal. Subsequently, the limma
   package was employed to identify differentially expressed mRNAs (DEGs)
   in GC using a t-test with a significance threshold of P-value < 0.05
   and logFC[67]^22^, [68]^23. The MCODE plugin was used to identify hub
   genes from the clusters of DEGs[69]^24. Transcription factors (TFs)
   were predicted from the protein-protein interaction (PPI) network using
   the iRegulon plugin in Cytoscape[70]^25. The regulatory network was
   constructed and visualized using Cytoscape. We conducted functional
   enrichment analyses of hub genes using Enrichr for gene ontology (GO)
   and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways[71]^26.
   Additionally, we obtained Hallmark gene sets for gene set enrichment
   analysis (GSEA) and classified samples into high and low groups based
   on the median expression levels of DSCC1 and E2F4 genes. Upregulated
   and downregulated genes were determined by comparing differences in
   gene expression levels between the two groups. Gene sets with | NES |>
   1 and FDR < 0.05 were regarded as significant. Additionally, three
   machine learning algorithms (SVM-REF, randomForest, and LASSO) were
   used to analyze hub genes, and common genes identified by all three
   algorithms were considered significant for GC. Expression profiles and
   protein levels of DSCC1 and E2F4 in stomach adenocarcinoma (STAD) were
   obtained from the TCGA database, and patient survival analysis was
   conducted using the KaplanMeier plotter[72]^27. STAD gene mutation data
   and RNA-seq data were also acquired from the TCGA database. GenVisR
   data package was utilized to construct and visualize the "waterfall
   map" of STAD gene mutations[73]^28. The UCSC Genome Browser was
   employed to search for DSCC1 promoter sequences and analyze
   transcription factors regulating DSCC1[74]^29. Pearson correlation
   analysis between DSCC1 expression and its transcription factor E2F4 was
   performed using the "ggstatsplot" and "ggplot2" packages. Finally, the
   JASPAR database was consulted to identify the binding site of
   transcription factor E2F4 to the DSCC1 promoter[75]^30.

Clinical samples

   Human surgical specimens, including formalin-fixed paraffin-embedded
   specimens of GC (n=80, aged 43 to 77 years) and freshly excised GC
   tissues (n=16; comprising 8 males and 8 females, aged 46 to 69 years),
   were obtained from Northern Jiangsu People's Hospital Affiliated to
   Yangzhou University (Yangzhou, China) between 2021 and 2023. None of
   the patients received preoperative chemotherapy. Two qualified
   pathologists confirmed the GC diagnosis. Disease staging was determined
   according to the American Joint Committee on Cancer TNM staging system
   (AJCC-8 TNM). Clinicopathological data including age, sex, tumor size,
   TNM stage, degree of differentiation, histological grade, venous and
   nerve invasion were collected from hospital records. The study was
   approved by The Northern Jiangsu People's Hospital Affiliated to
   Yangzhou University, and the Ethics Committee of Yangzhou University.
   All procedures were conducted in accordance with the ethical standards
   of the institutional research committee and the Declaration of
   Helsinki. Written informed consent was obtained from each patient prior
   to sample collection.

Real-time quantitative PCR (RT-qPCR)

   Total RNA extraction from GC cells and tissue samples was carried out
   using Trizol reagent (Vazyme, Nanjing, China) following the
   manufacturer's instructions. Subsequently, reverse transcription of the
   total RNA was conducted using a reverse transcription kit (Vazyme,
   Nanjing, China). Quantitative analysis was performed utilizing SYBR
   Green PCR Master Mix (Yeasen, Shanghai, China) on a Applied Biosystems
   StepOnePlus No.4376600 (Applied Biosystems, Waltham, MA, USA). GAPDH
   served as an internal reference gene for normalization. Fold changes
   were determined using the 2-ΔΔCt method. All experiments were
   replicated in triplicate. Primer sequences are detailed in
   [76]Supplementary Table 1.

Immunohistochemistry (IHC)

   The process involved in handling GC tissue samples included fixation,
   embedding, slicing, dewaxing, antigen retrieval, primary antibody
   incubation, secondary antibody incubation, DAB staining, hematoxylin
   staining, and alcohol denaturation. Primary antibodies used were
   anti-DSCC1 (1:50, Abmart, PHU7133) and anti-E2F4 (1:100, Bioss,
   bs-1399R). Slides were examined and imaged with an Olympus BX53
   fluorescent microscope (Olympus Corporation), and staining for DSCC1
   and E2F4 was assessed by cell positivity and intensity. Staining
   intensity was graded from 0 to 3 (colorless to dark brown), while the
   percentage of positive cells was scored from 0 to 4 (0-5% to >75%). The
   final score was derived from a combination of staining intensity and
   the percentage of positive cells. Cases with a score of ≥5 were
   categorized as having "high expression," while those with a score of ≤4
   were considered to have "low expression".

Western blot

   Total protein from GC cells and tissue samples was extracted using
   radio immunoprecipitation assay (RIPA, Solarbio, Beijing, China).
   Following extraction, total protein was separated on 10-12% SDS/PAGE
   gels and transferred onto polyvinylidene difluoride (PVDF) membranes
   (Millipore, Boston, MA, USA). Subsequently, membranes were blocked
   using 5% skim milk and then incubated overnight at 4 °C with primary
   antibodies. The following day, PVDF membranes were incubated for 1.5
   hours at room temperature with suitable secondary antibodies, and ECL
   substrate was applied. Images were captured using a Tianneng automatic
   chemiluminescence image processing system (TANON, Shanghai, China).
   Primary antibodies are listed in [77]Supplementary Table 2. The Western
   blot bands' intensity was measured using ImageJ software (version 1.51;
   National Institutes of Health).

Cell culture and drug treatment

   GC cell lines AGS, NCI-N87, HGC-27, and BGC-823, along with the gastric
   mucosal normal epithelial cell line GES-1, were procured from the Cell
   Bank of Type Culture Collection of Chinese Academy of Sciences,
   Shanghai Institute of Cell Biology, Chinese Academy of Sciences. These
   cell lines were cultured in Roswell Park Memorial Institute (RPMI) 1640
   medium (Gibco, Waltham, MA, USA) supplemented with 10% fetal bovine
   serum (FBS; Gibco) and 1% penicillin-streptomycin (Solarbio, Beijing,
   China) under standard conditions of 37 °C temperature, 5% CO2, and
   humidified atmosphere. Palbociclib (MCE, HY-50767; Monmouth Junction,
   USA) and HLM006474 (MCE, HY-16667; Monmouth Junction, USA) were
   dissolved in DMSO and added to the culture medium at the designated
   concentration. The cells were subsequently exposed to the drugs for 2
   days at 37°C, unless otherwise indicated.

Cell transfection

   Small hairpin RNA (shRNA) directed against DSCC1 and E2F4, along with a
   negative control shRNA (sh-Ctrl), were synthesized by General Boil
   (Shanghai, China). The coding sequence of DSCC1 or E2F4 was amplified
   and inserted into the pCDH-CMV-MCS-EF1-copGFP plasmid (General Boil)
   and then packaged into lentivirus. The lentivirus was transfected into
   selected GC cell lines. Positive cells were selected using puromycin (2
   μg/mL) for two weeks, and the efficacy of knockdown and overexpression
   was assessed via RT-qPCR and western blotting.

Cell proliferation assay

   The cell counting kit 8 (CCK-8, Yeasen, Shanghai, China) was employed
   to assess the proliferation capacity of GC cells following the
   manufacturer's protocol. Cells from both the sh-Ctrl and shRNA groups
   were seeded into 96-well plates (Corning, USA) at a density of 1×10^3
   cells per well. At 0h, 24h, 48h, 72h, and 96h time points, 10 μL of
   CCK8 solution was added to each well and incubated for 2 hours at 37 °C
   to evaluate proliferation. The absorbance was then measured at 450 nm
   using a BioTek microplate reader (BioTek, Winooski, VT). The Colony
   formation assay was conducted in 6-well plates (Corning). 5x10^2 cells
   from various experimental groups were seeded into each well and
   incubated at 37°C for 14 days. After incubation, colonies were fixed
   with 4% paraformaldehyde for 10 minutes and then stained with 5%
   crystal violet solution (Solarbio, Beijing, China) for 5 minutes. The
   number of visible colonies (comprising >50 cells) was manually counted
   from three independent replicates after the plates had dried.

EdU assay

   An EdU assay was conducted utilizing an EdU assay kit (Beyotime,
   Shanghai, China). Cells were exposed to a 10 μM EdU solution at room
   temperature for 2 hours. Following this, the cells were fixed with 4%
   neutral paraformaldehyde at room temperature for 30 minutes.
   Subsequently, cell permeabilization was achieved using PBS containing
   0.5% Triton X-100 for 20 minutes, followed by three washes with PBS.
   The cells were then stained using the BeyoClick EdU Assay kit
   (Beyotime, Shanghai, China) for 30 minutes and washed three times with
   washing solution. Finally, cell nuclei were counterstained with Hoechst
   33342 (Beyotime, Shanghai, China) and observed under a microscope.

Transwell assay

   Transwell chambers (Corning, USA) were utilized to evaluate cell
   migratory capacity. Cells were suspended in RPMI 1640 medium devoid of
   FBS, and 1×10^4 cells were seeded into the upper chamber of the
   Transwell insert. Subsequently, 500 μL of RPMI 1640 medium supplemented
   with 10% FBS was added to the lower chamber. After 24 hours, cells that
   had migrated to the lower surface of the membranes were fixed with 4%
   paraformaldehyde for 10 minutes and stained with 5% crystalline violet
   solution (Solarbio, Beijing, China) for 5 minutes. The average number
   of migrated cells was determined by examining six randomly chosen
   fields of view under the microscope.

Wound healing assay

   The wound healing assay was conducted using 6-well plates (Corning,
   USA). Cells from different experimental groups were seeded into the
   wells and allowed to grow until reaching 90% confluence. A scratch was
   created using a pipette tip, followed by washing away the detached
   cells with PBS. Subsequently, cells were cultured in RPMI 1640 medium
   without FBS for 24 hours. The width of the wound was measured at 0
   hours, 24 hours, and 48 hours using a light microscope, and ImageJ
   software (version 1.51; National Institutes of Health) was utilized to
   quantify the relative area of wound closure.

Cell cycle assay

   Cell cycle analysis was conducted using flow cytometry. Cells were
   initially washed twice with PBS and then treated with trypsin, followed
   by a 4-hour exposure to 70% ethanol at 24°C. Afterward, the cells were
   incubated in 500 μL of a prepared propidium iodide (PI) staining
   solution (Yeasen, Shanghai, China) at 37°C for 30 minutes, and then
   analysed using a flow cytometer (BD LSRFortessa; BD Biosciences,
   Franklin Lakes, USA). The resulting data were analyzed using FlowJo
   software (v10.8.1).

Immunofluorescence (IF)

   Cells cultured on confocal culture dishes (NEST, Wuxi, China) were
   fixed using 4% paraformaldehyde and permeabilized with 0.01% Triton
   X-100 for 15 minutes. Subsequently, the dishes were treated with
   primary antibodies, anti-DSCC1 (1:100, Bioss, bs-7720R) or anti-E2F4
   (1:100, Bioss, bs-1399R), overnight in a humidified chamber at 37 °C in
   the dark. Following this, cell nuclei were stained with DAPI. The
   fluorescence distribution and intensity of the cells were then assessed
   using a Zeiss LSM 880 laser microscope (Carl Zeiss AG, Oberkochen,
   Germany).

Chromatin-immunoprecipitation (ChIP)

   ChIP assays were performed using the SimpleChIP Enzymatic Chromatin IP
   Kit (CST, Massachusetts, MA, USA). Cells were fixed with 1%
   paraformaldehyde for 10 minutes, followed by addition of 0.125 M
   glycine to stop DNA-protein crosslinking at room temperature for 5
   minutes. SDS lysis buffer containing protease inhibitors was used to
   lyse the cells, and chromatin fragments were generated using an
   ultrasonic fragmentation device. A portion of the lysates was
   designated as “Input.” The remaining lysates were incubated with E2F4
   antibody (CST, #40291) and Protein G magnetic beads to form a
   DNA-antibody-magnetic bead complex. After elution and purification, the
   DNA was labelled as “Target.” Rabbit IgG (CST, Massachusetts, MA, USA)
   was used as a negative control. The final purified DNA fragments were
   analysed using RT-qPCR. Primer sequences for qPCR targeting the DSCC1
   promoter binding site are provided in [78]Supplementary Table 3.

Luciferase activity assay

   The luciferase activity assay was conducted following the protocol
   outlined in the Dual Luciferase Reporter Gene Assay Kit (Yeasen,
   Shanghai, China). Initially, the wild-type (WT) and mutated DSCC1 UTR
   were inserted into the PGL3-Basic Vector to generate vecE2F4+DSCC1Wt
   and vecE2F4+DSCC1Mut constructs. Subsequently, 293T cells were
   co-transfected with various plasmids for 48 hours. Finally, luciferase
   activity was measured using the Synergy LX (BioTek) instrument.

Co-immunoprecipitation (co-IP) assay

   The Co-IP assay was conducted following previously established
   protocols[79]^31. Lysates were mixed with Flag affinity beads
   (Sigma-Aldrich, MA, USA), and the interacting proteins were identified
   using western blot analysis.

Xenograft assay

   NCI-N87 cells were transfected with shRNA targeting DSCC1, E2F4, and
   corresponding scramble controls via lentiviral infection, with each
   plasmid tagged with luciferase. The cells, suspended in 100 µl PBS,
   were then subcutaneously injected into the left armpit of female BALB/c
   nude mice aged 4-5 weeks (Ethics Committee for Animal Experiments of
   Yangzhou University). To evaluate the combined effects of E2F4
   inhibitor HLM006474 (20 mg/kg) and cell cycle pathway inhibitor
   Palbociclib (2 mg/kg) on NCI-N87-induced tumors in vivo, 1×106 cells
   were injected subcutaneously into the mice. Tumor volumes were measured
   every 7 days using the formula (length × width^2)/2. After 28 days, the
   mice were euthanized by cervical dislocation under anesthesia,
   following Laboratory Animal Guidelines for Ethical Review of Animal
   Welfare[80]^32. Anesthesia was induced by CO2 asphyxiation, and death
   was confirmed by dilated pupils. Tumors were removed and weighed at the
   end of the experiment.

Statistical analysis

   Functional analysis was conducted using the SPSS software package
   (version 24.0, IBM SPSS, IL, USA) and GraphPad Prism (version 8.4.3,
   GraphPad Software, CA, USA). Student's t-test was employed to compare
   means between two groups, while two-tailed ANOVA was utilized for
   comparisons across multiple groups. The Chi-square test was applied for
   correlation analysis between gene expression and clinicopathological
   features. Additionally, the Kaplan-Meier method was utilized to assess
   overall survival differences among various groups. Cox regression
   analysis was employed to identify factors influencing patient
   prognosis. Statistical significance was set at a p-value < 0.05.

Results

Comprehensive analysis reveals key hub genes in GC

   Five GEO datasets (GSE1032326, [81]GSE118916, [82]GSE79973,
   [83]GSE112369, and [84]GSE26899) were independently analyzed to
   identify DEGs in GC. All identified DEGs were statistically significant
   based on P-values. To mitigate sample bias and exclude the effects of
   genetic conditions other than GC, we focused on the common DEGs across
   all datasets. A total of 432 common DEGs were identified, including 314
   upregulated and 118 downregulated genes (Figure [85]1A). The PPI
   network of the 314 upregulated genes consisted of 275 nodes and 2654
   edges. Cluster analysis using MCODE identified 12 clusters, with the
   major cluster (Figure [86]1B) having the highest MCODE score of 31.059.
   This primary cluster comprised 35 nodes and 528 edges. We generated a
   heatmap of these 35 hub genes to compare their expression between
   gastric tumor samples and normal tissues (Figure [87]1C). Using
   Enrichr, we conducted GO and pathway enrichment analysis of the hub
   genes. We identified 374 enriched GO terms under Biological Process, 75
   under Molecular Function, and 33 under Cellular Component. The top 10
   most significant GO enriched terms are presented in ([88]Supplementary
   Figures 1). Our findings indicate that the upregulated hub genes are
   primarily involved in biological processes such as ribosome biogenesis,
   DNA metabolic processes, and rRNA processing. In the Molecular Function
   category, RNA binding, DNA replication origin binding, and snoRNA
   binding were the most significantly enriched terms. Within the Cellular
   Component category, nuclear lumen, intracellular non-membrane-bound
   organelles, and nucleolus were the key enriched terms. Additionally,
   Enrichr identified 24 enriched KEGG pathways, with 5 pathways showing
   P<0.05, as detailed in ([89]Supplementary Table 4). To further identify
   the key hub genes in GC, we applied three machine learning algorithms:
   SVM-REF, random forest, and LASSO. We selected the common genes based
   on the top 6 genes from SVM-REF, the top 10 genes from random forest,
   and the results from LASSO (Figure [90]1D-G).

Figure 1.

   [91]Figure 1
   [92]Open in a new tab

   Identification and Prognostic Analysis of Hub Genes in Gastric Cancer.
   (A) Identification of common DEGs using GEO datasets. (B) Interaction
   network of differentially expressed upregulated mRNAs. (C) Heatmap
   showing the expression of 35 hub genes in GC and normal samples. (D)
   Machine learning and hub-gene screening through SVM-REF analysis. (E)
   randomForest analysis. (F) LASSO analysis. (G) Identified hub genes.

Elevated DSCC1 expression correlates with poor prognosis in GC

   To validate the expression of the DSCC1 gene and its relationship with
   overall survival in STAD patients, we used data from the TCGA database
   and the Kaplan-Meier plotter for analysis. The correlation between
   DSCC1 expression levels and overall survival in STAD patients is
   presented in (Figure [93]2A-B). The analysis revealed a significant
   difference in DSCC1 expression levels between STAD samples and normal
   tissues. Additionally, RT-qPCR and western blot experiments confirmed
   increased DSCC1 RNA and protein levels in 16 pairs of fresh specimens
   from GC patients, showing significantly higher DSCC1 expression in GC
   tissues (Figure [94]2C-D). Western blot analysis and qRT-PCR of various
   cell lines further validated the increased DSCC1 expression in four GC
   cell lines (AGS, NCI-N87, HGC-27, and BGC-823) compared to the normal
   cell line GES-1 (Figure [95]2E-F). IHC examination of 80 GC specimens
   demonstrated significant overexpression of DSCC1 in GC tissues compared
   to non-cancerous tissues. DSCC1 expression was notably higher in GC
   tissues than in adjacent non-cancerous tissues (Figure [96]2G). The
   relationship between DSCC1 expression and overall survival (OS) in 80
   GC patients was evaluated to investigate the connection between DSCC1
   expression and GC prognosis. Kaplan-Meier analysis showed that high
   DSCC1 protein expression was significantly associated with poor OS in
   GC patients (Figure [97]2H). Furthermore, statistical analysis of
   clinicopathological data from all GC patients revealed that high DSCC1
   expression was closely linked to tumor size, depth of invasion, lymph
   nodes metastasis, distant metastases, TNM stage, and degree of
   differentiation (P < 0.05). However, no significant association was
   found between DSCC1 expression and other clinicopathological features
   such as age, gender, lauren type, histological grade, venous invasion,
   and nerve invasion (P > 0.05) ([98]Supplementary Table 5).

Figure 2.

   [99]Figure 2
   [100]Open in a new tab

   Up-regulation of DSCC1 in GC tissues and cells. (A) Comparison of DSCC1
   expression between tumor tissues and adjacent tissues from the TCGA
   database. (B) Comparison of DSCC1 expression levels between high and
   low expression groups from the Kaplan-Meier Plotter. (C) Detection of
   DSCC1 mRNA expression in 16 pairs of GC and corresponding adjacent
   tissues using RT-qPCR. (D) Detection of DSCC1 protein expression in 16
   pairs of GC and corresponding adjacent tissues using Western blot. (E)
   Analysis of DSCC1 mRNA and protein expression in GES-1 and GC cell
   lines by RT-qPCR. (F) Analysis of DSCC1 mRNA and protein expression in
   GES-1 and GC cell lines by Western blot. (G) Detection of DSCC1
   expression in 80 paired GC and adjacent non-tumor tissues by IHC. (H)
   Survival analysis of GC patients with varying levels of DSCC1
   expression. (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001) -
   Significance levels denoted by asterisks.

DSCC1 promotes malignant behavior in GC cells

   To investigate the effects of DSCC1 on the malignant behaviour of GC
   cells, we measured its expression in normal gastric epithelial cells
   (GES-1) and various GC cell lines (AGS, NCI-N87, HGC-27, and BGC-823).
   DSCC1 expression was highest in NCI-N87 cells and lowest in AGS cells
   (Figure [101]2F). Based on these findings, we generated stable DSCC1
   knockdown and overexpressing cell lines. RT-qPCR and western blot
   analyses confirmed the successful knockdown and overexpression
   ([102]Supplementary Figures 2A-B). We then assessed the impact of DSCC1
   on GC cell proliferation using CCK-8, EdU, and colony formation assays.
   The results showed that DSCC1 knockdown inhibited NCI-N87 cell
   proliferation, whereas DSCC1 overexpression enhanced AGS cell
   proliferation (Figure [103]3A-B, 3D). Our primary objective was to
   understand the role of the DSCC1 gene in the cell cycle, particularly
   its involvement in the G1/S phase transition. Flow cytometry analysis
   revealed that DSCC1 knockdown inhibited cell cycle progression in
   NCI-N87 cells, while its overexpression promoted cell cycle progression
   in AGS cells (Figure [104]3C). Meanwhile, the mRNA expression level of
   DSCC1 in GC was divided into high and low expression groups based on
   the median value for GSEA enrichment analysis. This analysis revealed
   that upregulation of DSCC1 could activate the cell cycle signaling
   pathway (Figure [105]3G). Additionally, western blot analysis indicated
   that DSCC1 knockdown reduced the expression of cell cycle and
   proliferation-related proteins, including PCNA, Cyclin D1, CDK4, CDK6,
   CDC6, MCM3, and MCM4 in NCI-N87 cells. Conversely, DSCC1 overexpression
   increased the expression of these proteins in AGS cells (Figure
   [106]3H). To examine the relationship between DSCC1 and GC cell
   migration and invasion, we performed transwell and wound healing
   assays. DSCC1 knockdown inhibited migration and invasion in NCI-N87
   cells, whereas DSCC1 overexpression enhanced these behaviours in AGS
   cells (Figure [107]3D-E). Further western blot analysis showed that
   DSCC1 knockdown decreased the expression of migration and
   invasion-related proteins MMP2, MMP9, and N-cadherin, while increasing
   E-cadherin expression in NCI-N87 cells. In contrast, DSCC1
   overexpression increased the expression of MMP2, MMP9, and N-cadherin,
   while decreasing E-cadherin expression in AGS cells (Figure [108]3H).
   Overall, DSCC1 was found to promote the proliferation, migration, and
   invasion of GC cells.

Figure 3.

   [109]Figure 3
   [110]Open in a new tab

   Role of DSCC1 in Promoting Malignancy in GC Cells. (A-B) Impact of
   DSCC1 knockdown or overexpression on GC cell proliferation assessed
   through CCK-8 assay (A) and EdU assay (B). (C) Influence of DSCC1
   knockdown or overexpression on GC cell cycle analyzed using flow
   cytometry. (D) Effect of DSCC1 knockdown or overexpression on GC cell
   proliferation measured by colony formation assay. (E-F) Impact of DSCC1
   knockdown or overexpression on GC cell migration and invasion evaluated
   through transwell (E) and wound healing assay (F). (G) GSEA analysis
   illustrating the correlation between DSCC1 expression and the cell
   cycle pathway. (H) Alterations in cell cycle, proliferation, migration,
   and invasion-related proteins following DSCC1 knockdown or
   overexpression analyzed by Western blot in GC cells. (*P<0.05,
   **P<0.01, ***P<0.001, ****P<0.0001) - Significance levels denoted by
   asterisks.

DSCC1 promotes tumor growth and metastasis in vivo

   To further validate our in vitro findings, we investigated the role of
   DSCC1 in regulating tumor growth and metastasis in vivo. We established
   a subcutaneous tumor model in nude mice by injecting NCI-N87 cells
   transfected with either sh-Ctrl or sh-DSCC1. We monitored changes in
   tumor volume and plotted the growth curves accordingly. As anticipated,
   DSCC1 knockdown significantly slowed tumor growth in the nude mice
   (Figure [111]4A). Tumors were harvested, measured for volume, and
   weighed from the sacrificed animals, revealing that tumors in the
   sh-Ctrl group were significantly heavier than those in the sh-DSCC1
   group (Figure [112]4B-D). Additionally, Western blot analysis showed
   that DSCC1 knockdown reduced its expression in the tumor tissues
   (Figure [113]4E).

Figure 4.

   [114]Figure 4
   [115]Open in a new tab

   Role of DSCC1 in Promoting Gastric Cancer Process In Vivo. (A)
   Inhibition of DSCC1 suppressed the growth of subcutaneous xenograft
   tumors in nude mice. (B) Images of subcutaneous tumors. (C) Growth
   curve of subcutaneous tumors in nude mice. (D) Measurement of tumor
   weight post-sacrifice. (E) Western blot analysis of DSCC1 expression in
   xenograft tumors. (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001) -
   Significance levels denoted by asterisks.

E2F4-mediated transcriptional upregulation of DSCC1 in GC

   As previously mentioned, DSCC1 is upregulated in GC, contributing to
   tumor malignancy. To investigate the reasons behind DSCC1 upregulation
   in GC, we first ruled out DSCC1 mutations as a cause using
   bioinformatics ([116]Supplementary Figure 3). Given the significant
   role of abnormal transcriptional regulation in cancer development, we
   hypothesized that aberrant transcription leads to increased DSCC1
   expression in GC. Additionally, using the iRegulon package in Cytoscape
   for transcription factor analysis of the 35 hub genes, we identified 24
   highly significant transcription factors ([117]Supplementary Table 6),
   with E2F4 emerging as the most prominent. E2F4 displayed a normalized
   enrichment score (NES) of 14.705, originating from the major cluster.
   The specific tracks associated with E2F4 revealed 27 targets among the
   35 DEGs (Figure [118]5A), and TCGA-STAD-seq data to generate an
   expression heatmap depicting these transcription factors in GC (Figure
   [119]5B). Using the TCGA-STAD-seq data, we examined the relationship
   between DSCC1 expression and the E2F4 transcription factor in GC
   (Figure [120]5C). To explore the regulation of DSCC1 expression by E2F4
   at the transcriptional level, we studied the localization of E2F4 and
   DSCC1 proteins in NCI-N87 and AGS cells using IF staining. E2F4 protein
   was detected at various levels in the nucleus and cytoplasm of both
   cell types (Figures [121]5D). Analysis of fluorescence intensity
   revealed a positive correlation between E2F4 and DSCC1 expression.
   Subsequently, a co-immunoprecipitation (co-IP) assay was performed to
   determine if there was a direct protein-protein interaction between
   E2F4 and DSCC1, which could regulate abnormal DSCC1 expression in GC.
   The results indicated no direct interaction between E2F4 and DSCC1
   proteins ([122]Supplementary Figure 4). Given that cell nuclei are the
   primary sites of transcription, it can be deduced that E2F4 regulates
   DSCC1 expression at the transcriptional level. To validate E2F4's
   regulatory effect on DSCC1 expression, we performed E2F4 knockdown and
   overexpression experiments. Knockdown of E2F4 significantly inhibited
   DSCC1 expression in NCI-N87 cells, while its overexpression increased
   DSCC1 levels in AGS cells, as confirmed by RT-qPCR and western blot
   (Figure [123]5E-F).

Figure 5.

   [124]Figure 5
   [125]Open in a new tab

   E2F4 Regulation of DSCC1 Transcription and Expression in GC. (A)
   Detection of differentially expressed genes (DEGs) within the major
   cluster as targets identified by enriched motifs/tracks associated with
   the E2F4 transcription factor. (B) Heatmap illustrating the expression
   of transcription factors regulating DSCC1 in GC and normal samples. (C)
   Analysis of expression correlations between E2F4 and PLAUR based on
   TCGA-seq data. (D) Representative confocal microscopy image
   demonstrating the co-localization of E2F4 protein (green) and DSCC1
   (red) in AGS and NCI-N87 cell lines. (E-F) Positive regulatory effect
   of E2F4 on DSCC1 expression in GC cells. Evaluation of E2F4 silencing
   on DSCC1 mRNA and protein expression in GC cells using RT-qPCR (E) and
   Western blot (F), respectively. (G) Prediction of the E2F4 binding
   motif within the DSCC1 promoter through the JASPAR dataset. (H)
   ChIP-qPCR analysis indicating E2F4 binding to the DSCC1 promoter in AGS
   and NCI-N87 cell lines, with the first position being the most
   significant binding site. (I) Luciferase activity assessment following
   mutation of the first E2F4 site in the DSCC1 promoter in 293T cell
   lines. The luciferase activity of the wild-type DSCC1 promoter was
   notably higher than that of the mutant DSCC1 promoter. (*P<0.05,
   **P<0.01, ***P<0.001, ****P<0.0001) - Significance levels indicated by
   asterisks.

   This confirmed that E2F4 regulates DSCC1 expression in GC. To determine
   whether E2F4 directly binds to the DSCC1 promoter, we identified
   multiple binding sites using the JASPAR database (Figure [126]5G;
   [127]Supplementary Table 7). For the top two binding sites identified,
   we conducted ChIP analysis in NCI-N87 and AGS cells. Figure [128]5G
   shows that site#1 in the DSCC1 promoter region are primary binding
   sites for E2F4. To further validate E2F4's transcriptional activation
   effect on DSCC1, we performed a dual luciferase reporter assay using
   293T cells. Based on the ChIP-qPCR results, we mutated the primary E2F4
   binding sites in the DSCC1 promoter and examined luciferase activity.
   The wild-type DSCC1 promoter showed significantly higher luciferase
   activity than the mutant promoter (Figure [129]5I). This indicates that
   the transcription factor E2F4 positively regulates DSCC1 expression. In
   summary, E2F4 promotes DSCC1 expression through transcriptional
   upregulation.

E2F4 expression and its prognostic significance in GC

   To validate the expression of the E2F4 gene and its relationship with
   overall survival in STAD patients, we analyzed data from the TCGA
   database and utilized the Kaplan-Meier plotter. The correlation between
   E2F4 expression levels and overall survival in STAD patients is shown
   in (Figure [130]6A-B). The analysis revealed a significant difference
   in E2F4 expression levels between STAD samples and normal tissues.
   Additionally, RT-qPCR and western blot experiments confirmed that E2F4
   RNA and protein levels were significantly higher in 16 pairs of fresh
   GC specimens compared to normal tissues (Figure [131]6C-D). Further
   validation through RT-qPCR and western blot analysis of various cell
   lines showed increased E2F4 expression in four GC cell lines (AGS,
   NCI-N87, HGC-27, and BGC-823) compared to the normal cell line GES-1
   (Figure [132]6E-F). IHC examination of 80 GC specimens demonstrated
   significant overexpression of E2F4 in GC tissues relative to
   non-cancerous tissues, with notably higher E2F4 expression in GC
   tissues compared to adjacent non-cancerous tissues (Figure [133]6G). To
   investigate the statistical analysis of clinicopathological data from
   all GC patients revealed that high E2F4 expression was closely linked
   to tumor size, lymph nodes metastasis, distant metastases, and TNM
   stage, (P < 0.05). However, there was no significant association
   between E2F4 expression and other clinicopathological features such as
   age, gender, lauren type, depth of invasion, degree of differentiation,
   histological grade, venous invasion, and nerve invasion (P > 0.05)
   ([134]Supplementary Table 8). According to Kaplan-Meier analysis,
   increased E2F4 protein expression was significantly associated with
   poor overall survival (OS) in GC patients (Figure [135]6H). Univariate
   regression analysis identified several factors affecting OS (P < 0.05):
   tumor size, lymph nodes metastasis, distant metastases, TNM stage
   (stages I and II vs. stages III and IV), E2F4 expression (low vs.
   high), and DSCC1 expression (low vs. high). Multivariate regression
   analysis confirmed these factors as independent risk factors for GC
   progression (P < 0.05): tumor size, lymph nodes metastasis, distant
   metastases, TNM stage, E2F4 expression, and DSCC1 expression
   ([136]Supplementary Table 9). These results indicate that E2F4 is
   elevated in GC and that patients with high E2F4 levels have a poor
   prognosis.

Figure 6.

   [137]Figure 6
   [138]Open in a new tab

   Up-regulation of E2F4 in GC tissues and cells. (A) Comparison of F2F4
   expression between tumor tissues and adjacent tissues from the TCGA
   database. (B) Comparison of E2F4 expression levels between high and low
   expression groups from the Kaplan-Meier Plotter. (C) Detection of E2F4
   mRNA expression in 16 pairs of GC and corresponding adjacent tissues
   using RT-qPCR. (D) Detection of E2F4 protein expression in 16 pairs of
   GC and corresponding adjacent tissues using Western blot. (E) Analysis
   of E2F4 mRNA and protein expression in GES-1 and GC cell lines by
   RT-qPCR. (F) Analysis of E2F4 mRNA and protein expression in GES-1 and
   GC cell lines by Western blot. (G) Detection of E2F4 expression in 80
   paired GC and adjacent non-tumor tissues by IHC. (H) Survival analysis
   of GC patients with varying levels of E2F4 expression. (*P<0.05,
   **P<0.01, ***P<0.001, ****P<0.0001) - Significance levels denoted by
   asterisks.

The role of E2F4 in GC proliferation, migration, and invasion: in vitro and
in vivo studies

   We generated stable cell lines with E2F4 knockdown and overexpression.
   The successful manipulation was confirmed via RT-qPCR and Western blot
   analyses ([139]Supplementary Figures 2C-D). We then investigated E2F4's
   role in carcinogenesis both in vitro and in vivo. As shown in Figures
   [140]7A-B and 7D, E2F4 knockdown inhibited the proliferation of NCI-N87
   cells, while E2F4 overexpression promoted proliferation in AGS cells.
   Flow cytometry analysis revealed that E2F4 knockdown inhibited cell
   cycle progression in NCI-N87 cells, whereas overexpression enhanced it
   in AGS cells (Figure [141]7C). GSEA indicated that high E2F4 expression
   activated the cell cycle signaling pathway (Figure [142]7G). Western
   blot analysis demonstrated that E2F4 knockdown reduced the expression
   of cell cycle and proliferation-related proteins, including PCNA,
   Cyclin D1, CDK4, CDK6, CDC6, MCM3, and MCM4 in NCI-N87 cells, while
   E2F4 overexpression increased these proteins in AGS cells (Figure
   [143]7H). Transwell and wound healing assays showed that E2F4 knockdown
   inhibited migration and invasion in NCI-N87 cells, whereas
   overexpression enhanced these behaviors in AGS cells (Figures
   [144]7D-E). Further western blot analysis indicated that E2F4 knockdown
   decreased the expression of migration and invasion-related proteins
   MMP2, MMP9, and N-cadherin, while increasing E-cadherin expression in
   NCI-N87 cells. Conversely, E2F4 overexpression increased MMP2, MMP9,
   and N-cadherin expression while decreasing E-cadherin in AGS cells
   (Figure [145]7H). Briefly, our data indicate that E2F4 significantly
   influences the proliferation, migration, and invasion of gastric cancer
   cells.

Figure 7.

   [146]Figure 7
   [147]Open in a new tab

   Role of E2F4 in Promoting Malignancy in GC Cells. (A-B) Impact of E2F4
   knockdown or overexpression on GC cell proliferation assessed through
   CCK-8 assay (A) and EdU assay (B). (C) Influence of E2F4 knockdown or
   overexpression on GC cell cycle analyzed using flow cytometry. (D)
   Effect of E2F4 knockdown or overexpression on GC cell proliferation
   measured by colony formation assay. (E-F) Impact of E2F4 knockdown or
   overexpression on GC cell migration and invasion evaluated through
   transwell (E) and wound healing assay (F). (G) GSEA analysis
   illustrating the correlation between E2F4 expression and the cell cycle
   pathway. (H) Alterations in cell cycle, proliferation, migration, and
   invasion-related proteins following E2F4 knockdown or overexpression
   analyzed by Western blot in GC cells. (*P<0.05, **P<0.01, ***P<0.001,
   ****P<0.0001) - Significance levels denoted by asterisks.

   To elucidate the functions of E2F4 in vivo, we established subcutaneous
   tumor models in nude mice by injecting NCI-N87 cells, following
   previously reported methods. The sh-Ctrl group exhibited significantly
   heavier tumors compared to the sh-E2F4 group (Figure [148]8B). As shown
   in Figure [149]8C-[150]8D, measured for volume and tumor weights were
   measured after euthanizing the mice. Western blot analysis confirmed
   that E2F4 knockdown effectively reduced its expression in the tumor
   tissues (Figure [151]8E). In summary, E2F4 was found to promote the
   growth and metastasis of GC and regulate the expression of DSCC1 in
   vivo.

Figure 8.

   [152]Figure 8
   [153]Open in a new tab

   Role of E2F4 in Promoting Gastric Cancer Process In Vivo. (A)
   Inhibition of E2F4 suppressed the growth of subcutaneous xenograft
   tumors in nude mice. (B) Images of subcutaneous tumors. (C) Growth
   curve of subcutaneous tumors in nude mice. (D) Measurement of tumor
   weight post-sacrifice. (E) Western blot analysis of E2F4 expression in
   xenograft tumors. (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001) -
   Significance levels denoted by asterisks.

E2F4-DSCC1 axis in regulating proliferation, migration, and invasion of
NCI-N87 GC cells

   To further confirm that DSCC1 is a downstream target gene of E2F4, we
   conducted rescue experiments in sh-E2F4 NCI-N87 cells by overexpressing
   DSCC1 and observing changes in proliferation, migration, and invasion.
   CCK8, EdU, and colony formation assays demonstrated that E2F4 knockdown
   inhibited NCI-N87 cell proliferation, and this inhibition was reversed
   by co-transfection with DSCC1 (Figure [154]9A-B, D). Additionally, as
   shown in Figure [155]9C, E2F4 knockdown inhibited the cell cycle
   progression of NCI-N87 cells, which was partially reversed by DSCC1
   overexpression. Western blot analysis revealed that the sh-E2F4-induced
   decrease in the expression of PCNA, Cyclin D1, CDK4, CDK6, CDC6, MCM3,
   MCM4, MMP2, MMP9, E-Cadherin, and N-Cadherin in suspended NCI-N87 cells
   was partially reversed by DSCC1 overexpression (Figure [156]9F). In
   transwell assays, E2F4 knockdown inhibited the migration and invasion
   abilities of NCI-N87 cells, and this reduction was partially rescued by
   co-transfection with DSCC1 (Figure [157]9E). These results indicate
   that E2F4 promotes the proliferation, migration, and invasion of GC
   cells through a DSCC1-dependent mechanism.

Figure 9.

   [158]Figure 9
   [159]Open in a new tab

   DSCC1 is Crucial for E2F4-Mediated Malignant Phenotype in GC Cells.
   (A-B, D) Cell proliferation in NCI-N87 cells with E2F4 knockdown,
   control, and DSCC1 re-expression in E2F4-depleted cells, quantified
   using CCK-8 (A), EdU (B), and colony formation (D) assays. (C) Cell
   cycle analysis via flow cytometry in NCI-N87 cells with E2F4 knockdown,
   control, and DSCC1 re-expression in E2F4-depleted cells. (E) Assessment
   of cell migration and invasion through transwell assays in NCI-N87
   cells with E2F4 knockdown, control, and DSCC1 re-expression in
   E2F4-depleted cells. (F) Western blot analysis of cell cycle,
   proliferation, migration, and invasion-related proteins in NCI-N87
   cells with E2F4 knockdown, control, and DSCC1 re-expression in
   E2F4-depleted cells. (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001) -
   Significance levels denoted by asterisks.

Combination of E2F4 inhibitor and the cell cycle pathway inhibition for the
therapy of GC

   To assess the impact of cell cycle inhibitors on NCI-N87 cell
   viability, we treated the cells with varying concentrations of
   Palbociclib (1, 2, 4, 6, 8, and 10 μM) for 24, 48, and 72 hours, and
   E2F4 inhibitor HLM006474 (10, 20, 40, 60, 80, and 100 μM) for the same
   time periods ([160]Supplementary Figures 5A-B). To investigate the
   resistance mechanisms of NCI-N87 cells to HLM006474, we analyzed the
   expression and activation of related proteins. Treatment with HLM006474
   at 80 and 100 μM significantly reduced the activation of CDK4 and CDK6
   ([161]Supplementary Figures 5C). Compared to control groups, treatment
   with HLM006474 (2 μM) or Palbociclib (20 μM) alone had no effect on
   cell viability. However, the combination of HLM006474 (2 μM) and
   Palbociclib (20 μM) significantly inhibited cell viability compared to
   either HLM006474 or Palbociclib alone (Figure [162]10A-B, D-E).
   Compared to control groups, HLM006474 (2 μM) and Palbociclib (20 μM)
   alone had no significant effect on cell cycle progression. However, the
   combination of HLM006474 (2 μM) and Palbociclib (20 μM) significantly
   inhibited cell cycle progression compared to either treatment alone.
   These findings suggest that Palbociclib synergistically enhances the
   effects of HLM006474 on NCI-N87 cell cycle progression (Figure
   [163]10C).

Figure 10.

   [164]Figure 10
   [165]Open in a new tab

   (A-F) The effect of combination HLM006474 and Palbociclib on the
   Proliferation, Migration, Invasion and cell cycle in NCI-N87 cells.
   CCK8 assay (A), proliferation measured by EdU assay (B), cell cycle
   progression analysis (C), colony formation ability (D), migration and
   invasion capabilities determined through transwell assays (E), and
   Western blot analysis of proteins related to cell cycle, proliferation,
   migration, and invasion (F) in NCI-N87 cells treated with the
   combination of HLM006474 and Palbociclib. (*P<0.05, **P<0.01,
   ***P<0.001, ****P<0.0001) - Significance levels denoted by asterisks.

   Additionally, we examined the impact of the combination treatment on
   related proteins. HLM006474 (2 μM) and Palbociclib (20 μM) alone
   inhibited E-Cadherin activation and upregulated E2F4, DSCC1, PCNA,
   Cyclin D1, CDK4, CDK6, CDC6, MCM3, MCM4, MMP2, MMP9, and N-Cadherin. In
   contrast, the combination treatment significantly inhibited the
   expression of E2F4, DSCC1, PCNA, Cyclin D1, CDK4, CDK6, CDC6, MCM3,
   MCM4, MMP2, MMP9, and N-Cadherin (Figure [166]10F). These results
   indicate that HLM006474 inhibits E-Cadherin activation while promoting
   E2F4, DSCC1, and other proliferation and migration-related proteins.
   Palbociclib, as a cell cycle pathway inhibitor, combined with
   HLM006474, effectively blocks the activation of both pathways.

   To evaluate the effects of combined HLM006474 and Palbociclib treatment
   in vivo, tumors were harvested and examined after 28 days (Figure
   [167]11A-B). Tumor volume and weight were measured in NCI-N87 GC cells
   implanted in nude mice (Figure [168]11C-D). Starting from day 14,
   NCI-N87 cells developed large tumors in the mice. HLM006474 and
   Palbociclib individually did not significantly inhibit tumor growth
   compared to the control group (no treatment). However, the combination
   of HLM006474 and Palbociclib showed a significantly stronger inhibitory
   effect on both tumor volume and weight than either treatment alone.

Figure 11.

   [169]Figure 11
   [170]Open in a new tab

   The effects of combination treatment of HLM006474 and Palbociclib in
   vivo. (A) Combination treatment of HLM006474 and Palbociclib suppressed
   the growth of subcutaneous xenograft tumors in nude mice. (B) Images of
   subcutaneous tumors. (C) Growth curve of subcutaneous tumors in nude
   mice. (D) Measurement of tumor weight post-sacrifice. (*P<0.05,
   **P<0.01, ***P<0.001, ****P<0.0001) - Significance levels denoted by
   asterisks.

Discussion

   This research uncovered that E2F4 exhibits high expression levels in
   GC, correlating closely with unfavorable clinicopathological
   characteristics in patients. Elevated E2F4 expression independently
   predicts a poor prognosis for GC patients. In vitro experiments
   confirmed E2F4's role in promoting GC cell proliferation, migration,
   and invasion. In vivo studies further demonstrated that E2F4 enhances
   tumor growth and metastasis in nude mice. Notably, E2F4 acts as a key
   transcriptional regulator of DSCC1, activating it by binding to
   specific sites within the DSCC1 promoter region. Similarly, DSCC1 was
   found to promote malignancy in GC, particularly cell proliferation, in
   both in vitro and in vivo settings. Together, these findings underscore
   the dysregulated interplay between E2F4 and DSCC1 in GC, contributing
   significantly to GC cell proliferation, migration, and invasion.

   Members of the fibrinolytic system, including DSCC1, play essential
   roles in cellular processes such as chemotaxis, invasion, migration,
   regulation of cytokines and growth factors, immune response, and
   angiogenesis[171]^33^-[172]^35. DSCC1 specifically contributes
   significantly to the malignant advancement of GC, particularly in the
   migration of GC cells. The collagen fibrin network, established in the
   intercellular space through stromal cell secretion, serves as a barrier
   against tumor cell migration and invasion. However, tumor cells disrupt
   this network by activating the fibrinolytic system via DSCC1, thereby
   facilitating metastasis.[173]^13^, [174]^36^, [175]^37. Our study
   validated the role of DSCC1 in promoting GC malignancy both in vitro
   and in vivo. Additionally, Jin et al.[176]^36 demonstrated elevated
   DSCC1 expression in breast carcinoma tissues through semi-quantitative
   IHC. Through Cox analysis, they noted a significant negative
   correlation between DSCC1 expression and the survival duration of GC
   patients. Several other studies have also confirmed the high expression
   of DSCC1 in various cancers, suggesting its significant involvement in
   tumor distant metastasis[177]^17^, [178]^38^-[179]^40. Therefore, DSCC1
   emerges as a crucial regulator of cancer cell proliferation and
   migration. Interestingly, previous studies have not explored the
   correlation between DSCC1 expression and the proliferation, migration,
   and invasion of GC cells.

   We delved into the molecular mechanism behind DSCC1 upregulation in GC,
   pinpointing E2F4 as a potential transcriptional regulator. E2F4 protein
   predominantly localized in the nuclei of GC cells. Through ChIP-qPCR
   and dual luciferase reporter assays, we confirmed binding sites between
   E2F4 and the DSCC1 promoter region, validating E2F4's positive
   regulation of DSCC1 expression in GC. DSCC1's regulation involves
   various transcription factors; for instance, in colorectal cancer, the
   upregulated transcription factor E2F1 possesses binding sites within
   the DSCC1 promoter, resulting in DSCC1 upregulation and impacting
   colorectal cancer cells[180]^41. However, E2F4's role in GC remains
   uncertain, particularly regarding its involvement in GC cell
   proliferation, migration, and invasion.

   E2F4 is a key player in cell cycle progression and cell proliferation.
   When activated, E2F4 promotes the transcription of genes necessary for
   transitioning from the G1 phase to the S phase, including those
   involved in DNA replication and cell division. Conversely, E2F4 can
   also repress genes related to cell cycle arrest and
   differentiation[181]^42^-[182]^44. In head and neck squamous cell
   carcinoma, high E2F4 expression is linked to poor prognosis, with IHC
   analysis showing elevated levels of E2F4[183]^45. However, previous
   studies have not examined whether E2F4 binds to promoter regions to
   facilitate cancer cell growth and migration. Our study found that E2F4
   is upregulated in GC tissues and cells. In vitro experiments
   demonstrated that E2F4 enhances GC cell proliferation, migration, and
   invasion. In vivo experiments further confirmed that E2F4 promotes
   tumor growth and metastasis in nude mice. Functional rescue experiments
   verified that E2F4 significantly contributes to the malignancy of GC in
   a DSCC1 dependent manner.

   Cell division relies on the cell cycle, which is regulated by
   cyclin-dependent kinases (CDKs). Cyclin D's interaction with CDK4 and
   CDK6 promotes the phosphorylation of the retinoblastoma protein,
   advancing the cell cycle from the G1 phase to the S phase[184]^46.
   Targeting CDK6 and the closely related CDK4 kinase has garnered
   significant interest for cancer therapy. Previous research has shown
   that Palbociclib, a highly selective CDK4/6 inhibitor, can block the G1
   to S phase transition in the cell cycle[185]^47. HLM006474 is predicted
   to form hydrogen bonds with three conserved residues within the E2F
   family, suggesting that it is not specific to E2F4
   heterodimers[186]^48. Many E2F-regulated promoters are repressed by
   E2F4/Rb complexes during G0/G1 and are de-repressed at the G1/S
   boundary. Blocking E2F DNA-binding activity is predicted to upregulate
   these genes, potentially increasing cell growth[187]^49. Our results
   showed that low concentrations of HLM006474 did not significantly
   inhibit cell viability, thus there has been no advocacy for HLM006474
   as a monotherapy. Abnormalities in the cell cycle pathway contribute to
   the pathogenesis of several cancers[188]^50. In this study, we found
   that HLM006474 significantly inhibited the cell cycle pathway.
   Consequently, we used the cell cycle inhibitor Palbociclib to observe
   its combined effect with HLM006474. While HLM006474 alone and
   Palbociclib alone did not affect cell viability, their combination
   significantly inhibited cell viability compared to HLM006474 and
   Palbociclib, respectively.

Conclusions

   In conclusion, E2F4 is highly expressed in GC and serves as an
   independent predictor of poor prognosis in GC patients. E2F4 promotes
   the proliferation, migration, and invasion of GC cells through its
   regulation of DSCC1. Our study elucidates the role of DSCC1 in the
   upstream molecular regulation of GC, offering a theoretical foundation
   for targeting E2F4 and DSCC1 as potential therapeutic strategies for
   GC. Additionally, Combining the E2F4 inhibitor HLM006474 with the cell
   cycle inhibitor Palbociclib significantly reduced cell viability and
   inhibited cell cycle progression more effectively than HLM006474 alone.
   Palbociclib synergistically enhanced the effects of HLM006474 on GC in
   vitro and in vivo, supporting the strategy of combining these
   inhibitors to reduce drug resistance.

Supplementary Material

   Supplementary figures and tables.
   [189]ijbsv20p4978s1.pdf^ (1.7MB, pdf)

Figure 12.

   [190]Figure 12
   [191]Open in a new tab

   Schematic summary. Mechanistically, E2F4 is upregulated in GC and
   transcriptionally activates DSCC1, which promotes Proliferation,
   Migration, and Invasion of GC cells.

Acknowledgments