Silencing RNA binding protein OAS3 inhibits the progression of gastric cancer

DOI:doi.org/10.65281/736486

Silencing RNAbinding protein OAS3 inhibits the progression of gastric cancer

Zhixin Wan1†,Wenbin Zhang1†(co-first authors),Laibijiang·Wusiman1,,Peng Li1,2,Dingding
Song1,Feiyang Yan1,Wenfeng Wang1,Yin Shu1*
1The Third Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang Uygur
Autonomous Region, 830001
2People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang Uygur Autonomous
Region, 844000
Corresponding author : Yin Shu doctorshuyin@xjmu.edu.cn;
First author : Zhixin Wan; Co-first author : Wenbin Zhang
Abstract
ORCID：0009-0000-7470-8042
Background 2′-5′Oligoadenylate synthetase 3 (OAS3) is an RNA-binding protein (RBP) whose high
expression has been associated with prognosis and chemotherapy outcomes in multiple cancers.
However, the role of OAS3 in Gastric cancer (GC) remains to be elucidated. This study aimed to
elucidate the biological functions and potential regulatory mechanisms of OAS3 in GC.
Methods The expression level of OAS3 in GC and its correlation with prognosis were analyzed by
biological information. Furthermore, a correlation analysis was performed between highly expressed
OAS3 and immune‑infiltrating cells. The biological functions of OAS3 in GC were assessed using
CCK‑8, Transwell, and flow cytometry assays. Finally, RNA‑Seq data from five pairs of GC and1
adjacent normal tissues were cross-analyzed with RNA‑Seq data from OAS3‑silenced and siNC control
cells to explore the potential mechanisms by which OAS3 influences GC.
Results OAS3 was upregulated in GC cell lines, and high OAS3 levels were correlated with poorer
prognosis. High OAS3 expression showed a positive correlation with immune‑infiltrating cells,
although negative correlations were observed with certain immune cell subtypes, reflecting a state of
“pro‑tumorigenic activity”. Silencing OAS3 significantly inhibited GC cell proliferation, invasion, and
migration, while promoting apoptosis. Cross‑analysis of RNA‑seq datasets suggested that
downregulation of OAS3 may disrupt the “membrane protein binding– signal transduction– innate
immune response” cascade, thereby reshaping the critical cytokine‑cytokine receptor interaction
pathway and influencing GC development.
Conclusions Knocking down the expression of OAS3 may affect signal transduction through an
imbalance of binding proteins on the cell membrane, leading to immune imbalance and promoting the
progression of GC. These findings suggest that OAS3 may serve as a potential prognostic biomarker
and therapeutic target for GC.
[KeyWords] OAS3, Gastric Cancer，RBPs，Immune cell，Apoptosis
Background
GC is one of the most common malignant tumors worldwide. In 2022, there were over 968,000 new
cases of GC globally, with approximately 660,000 deaths, ranking fifth in both incidence and mortality
rates[1]. The incidence (22.4/100,000 population) and mortality (14.6/ 100,000) of GC in East Asia are
among the highest worldwide[2]. Epidemiological studies have identified Helicobacter pylori infection,
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high cholesterol intake, and a high-salt diet as risk factors for GC development[3]. In recent years,
although endoscopic screening has reduced GC mortality by 30%, it remains one of the malignancies
with the highest fatality rates, with a five-year survival rate of approximately 20%[4,5]. Despite
advances such as molecular subtyping and immunotherapy, which have improved the prognosis of
some patients, the majority of patients with advanced GC still have poor outcomes. This underscores
the need to identify novel molecular biomarkers to refine risk stratification and guide individualized
treatment[6–8]. In recent years, the role of signaling pathways mediated by molecular dysregulation in
the initiation, progression, metastasis, and therapeutic response of GC has become a major research
focus[9,10].
RNA-binding proteins (RBPs) are a broad class of proteins capable of binding to double or
single-stranded RNA to form ribonucleoprotein complexes, which play a crucial role in
post-transcriptional regulation in eukaryotes. Primarily through RNA binding, RBPs influence mRNA
biogenesis, modification, splicing, stability, transport, intracellular localization, translation, and
degradation[11,12]. Studies have found that the RNA-binding proteins NELFE, PTBP1, and ENO1
promote GC growth and metastasis[13–15]. OAS3, a member of the OAS family, binds to dsRNA and
subsequently triggers the synthesis of 2′-5-linked oligoadenylates (2-5A), which activate ribonuclease
L (RNase L), thereby exerting antiviral and antitumorigenic effects[16]. OAS3 expression positively
correlates with the infiltration of immunosuppressive cells. Clinical trial analyses have demonstrated
that patients with high OAS3 expression exhibit poorer prognosis in lung adenocarcinoma (LUAD),
kidney renal papillary cell carcinoma (KIRP), and liver hepatocellular carcinoma (LIHC) [17]. High
OAS3 expression is also associated with an unfavorable prognosis in breast cancer[18]. Recent studies
have indicated that upregulation of OAS3 induces the polarization of activated macrophages towards
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an M2d phenotype in pancreatic cancer (PC), impairs the function of CD8+ T cells ex vivo and in vivo,
and drives immune evasion in PC cells[19]. Although substantial evidence links OAS3 to various
cancers, its specific role in GC remains unclear.
In this study, we demonstrated that OAS3 is significantly overexpressed in GC and is closely
associated with poor prognosis in patients with GC. Bioinformatic analysis, ex vivo cellular
experiments, and dual RNA-seq integrative analysis were employed to elucidate the expression pattern,
biological functions, and underlying mechanisms of OAS3 in GC. Our findings provide novel insights
into the potential mechanisms of GC development and fill the gap in our understanding of the role of
OAS3 in GC, thereby laying a foundation for further exploration of OAS3 as a potential therapeutic
target.
Methods
Clinical specimen
Five pairs of gastric cancer tissues and their corresponding adjacent non-cancerous tissues were
obtained from the Affiliated Tumor Hospital of Xinjiang Medical University. All cases were confirmed
by histopathological examination, and none of the patient received chemotherapy or radiotherapy prior
to surgery. The resected specimens were immediately collected in liquid nitrogen and stored at-80°C.
This study was approved by the Ethics Committee of the Affiliated Tumor Hospital of Xinjiang
Medical University (Approval No. S-2024156). Written informed consent was obtained from all
patients.
Bioinformatics analysis
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The expression level of OAS3 in GC was retrieved from the online databases TIMER 2.0
(https://compbio.cn/timer2/) and Gene Expression Profiling Interactive Analysis (GEPIA)
(http://gepia2.cancer-pku.cn/#index). The survival curve of OAS3 in patients with GC was derived
from the Kaplan-Meier Plotter (https://kmplot.com/analysis/). Six genes (PLCB2, CXCL8, BIRC3,
NLRP7, TNFAIP3, and CARD16) were considered to be associated with the NOD-like receptor
signaling pathway. These six genes were incorporated as features into GEPIA to calculate their
correlations with OAS3. Gene Co-expression Network analysis tools were used to identify genes that
potentially interacted with OAS3.
Cell culture and transfection
The human gastric mucosal epithelial cell line (GES-1), the gastric adenocarcinoma cell line (AGS),
and Hela cells were purchased from Procell (Wuhan, China). All cells were cultured in DMEM, F12, or
RPMI 1640 basal medium (Shanghai, China), supplemented with 10% fetal bovine serum (Gibco,
10091148, USA) and 1% penicillin-streptomycin solution (100 U/mL, Hyclone, SV30010, USA). Cells
were maintained at 37°C in a humidified incubator with 5% CO₂. All cells used in the experiments
were in the logarithmic growth phase.
All siRNA duplexes were purchased from Gemma (Suzhou, China). Normally cultured cells
were seeded in 12-well plates at a density of 1×10⁵ cells per well, with three replicates per group, and
incubated overnight at 37°C with 5% CO₂. After overnight incubation, siRNA transfection was
performed (siRNA sequences are provided in the Supplementary Materials: Table S1). Plasmid
transfection was performed using Lipofectamine reagent according to the manufacturer’s protocol.
The final transfection volume was 1.2 mL, and the final siRNA concentration was 10 nM. The cells
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were then returned to the incubator (37°C, 5% CO₂) and cultured for 48h before sample collection.
Subsequently, the cells were treated with puromycin (#P8230-25mg;Solarbio,China) for one week.
Transfection efficiency was verified by western blotting and RT-qPCR, and the cells were used for
subsequent experiments.
Quantitative Real-Time PCR (RT-qPCR)
CDNAsynthesis was performed using a reverse transcription kit(R323-01, Vazyme, China) at 42˚C for
5min,37 ℃ for 15min,85 ℃ for 5s on a mycycler(T100, Bio-Rad, USA). qPCR was performed onABI
QuantStudio 5, followed by denaturation at 95℃ for 10 min, 40 cycles of denaturation at 95℃ for 15s
and annealing and extension at 60℃ for 1 min. Three technical replicates were used for each sample.
The concentration of each transcript was then normalized to GAPDH (glyceraldehyde-3-phosphate
dehydrogenase) and mRNA levels using the 2-ΔΔCT method to analysis (Livak and Schmittgen 2001).
Comparisons were performed with the two-way analysis of variance(ANOVA) using GraphPad Prism
software (Version number8.0, San Diego, CA, USA). Primers for quantitative (q)PCR analysis are
presented in Supplementary Materials(Table S2).
Western blot
The cells were lysed in ice-cold RIPA Buffer (PR20001, Proteintech, China) supplemented with a
protease inhibitor cocktail (4693116001, Sigma, USA) and incubate on ice for 30 min. Samples were
boiled for 10 min in boiling water with protein loading buffer(P1040, Solarbio, China) and loaded onto
a 10% SDS-PAGE gel and transferred onto 0.45mm PVDF membranes(ISEQ00010, Millipore, USA).
The PVDF membranes were then blocked with 5% nonfat dry milk for 1h at room temperature and
incubated overnight at 4℃ with primary antibodies against OAS3 (anti- Mouse, 1:1,000, SA00001-1,
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Proteintech, China) and ACTIN (1:5,000, antibody produced in rabbit, 20536-1-AP, Proteintech, China),
followed by incubation with horseradish peroxidase-conjugated secondary antibody (anti-rabbit,
1:10,000, SA00001-2, Proteintech, China)
for 45min at room temperature. Membranes were
visualized using an enhanced ECL reagent (P0018FM, Beyotime, China) through chemiluminescence.
CCK-8 assay
The cell proliferation assay was performed using a cell counting kit-8 (CCK-8,40203 ES 76, Yeasen,
Shanghai, China). Briefly, Transfected cells were seeded in 96 well plates at an appropriate cell volume
per well. Cells from the control and experimental groups were treated accordingly, and the medium
without cells was used as a blank control. After incubation at 37℃ and 5% CO2 for 0, 24, 48, and 72 h,
10 μL of CCK-8 solution was added and incubated for another 3h at 37℃. Subsequently, the optical
density of the cells was measured using a microplate reader (ELX800, Biotek, USA) at 450 nm. The
cell proliferation rate was calculated as follows: proliferation rate = (experimental OD blank OD) /
(control OD blank OD) ×100%.
Transwell Assay
In vitro invasion assays were performed using transwell chambers (3422, Corning, USA). Transwell
chambers with an 8 µm filter and precoated with a thin layer of Matrigel (356234, BD Biosciences,
USA), diluted 1:8 using serum-free medium, 100 µL diluted Matrigel in chambers was incubated for 1h
at 37℃ and 5% CO2, and the unsolidified supernatant was removed. A total of 40,000 cells in 200 µL
serum-free medium were added to the inserts, and the transwell chambers were inserted in medium
with 600 µL 10% FBS (10091148, Gibco, China), which served as a chemoattractant in the lower
chamber, and incubated for 24h at 37 ℃ and 5% CO2. Cells remaining on the upper membrane surface
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of the inserts were then removed with a cotton swab, and the total number of cells that invaded into the
lower chamber was fixed with 4% paraformaldehyde (P0099, Beyotime, China) for 20 min, and then
stained with 0.1% crystal violet (C0121, Beyotime, China). Invasive cells were observed and counted
under an inverted microscope (MF52-N; Mshot, China) at 200× magnification.
RNASequencing
RNA-seq assays were performed by Wuhan Ruixing Biotechnology Co. Ltd. (http://www.rxbio.cc). For
each sample, 1 μg of total RNAwas treated with RQ1 DNase (M6101, Promega, USA) to remove DNA
before directional RNA-seq library preparation using the VAHTS® Universal V8 RNA-seq Library
Prep Kit for Illumina (NR605;Vazyme, China). mRNAs were captured using VAHTS mRNA capture
beads (N401;Vazyme, China). The fragmented RNAs were converted into double-stranded cDNA.
Following end repair and A-tailing, the DNAs was ligated to VAHTS RNA Multiplex Oligos Set 1 for
Illumina (N323,Vazyme,China), and the ligated products were amplified, purified, quantified, and
stored at-80 °C before sequencing. The strand marked with dUTP (the 2nd cDNA strand) was not
amplified, allowing for strand-specific sequencing. For high-throughput sequencing, libraries were
prepared following the manufacturer’s instructions and applied to the DNBSEQ-T7 system for 150 nt
paired-end sequencing.
Flow cytometry
To detect cell apoptosis, we used the Annexin V-APC / 7-ADD cell apoptosis detection kit (40304 ES
60; Yeasen, Shanghai, China). Transfected cells were harvested in centrifuge tubes and treated as
required. The treated and control cells were incubated with 5 μLAnnexin V-APC for 15 min and 10 μL
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of PI reagent for 15 min in the dark. The level of apoptosis was determined by flow cytometry
(FACSCanto, BD, USA).
Statistical analysis
Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software, San Diego, CA,
USA). The Student’s t-test was used to assess the differences in continuous variables between the two
groups. Two-way ANOVA was used to study the effect of two categorical independent variables on a
continuous dependent variable. All statistical tests were two-tailed, and statistical significance was set
at P < 0.05.
Results
High OAS3 expression is associated with poor prognosis in patients with GC
To investigate the clinical significance of OAS3 in GC, we analyzed its expression in GC tissues and
evaluated its relationship with patient prognosis. First, analysis using the GEPIA online tool indicated
that OAS3 mRNA expression was significantly increased in most tumor tissues compared to that in
adjacent tissues, including GC (P < 0.001, Fig.1A). Concurrently, analysis of OAS3 expression in GC
tissues using TIMER 3.0 an online tool, yielded consistent results (P < 0.001, Fig.1B,Table 1).
Subsequently, transcriptome sequencing (hereafter referred to as GC RNA-Seq) of five paired GC and
adjacent non-cancerous tissues confirmed that OAS3 was significantly upregulated in GC tissues (P <
0.01) (Fig.1C). Analysis using the Kaplan-Meier Plotter online tool demonstrated that GC patients with
higher OAS3 expression had significantly shorter overall survival (OS), first progression (FP), and
post-progression survival (PPS) times than patients with low expression (Fig.1 D-F). Additionally,
KEGG enrichment analysis revealed that high OAS3 expression was associated with the NOD-like
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receptor (NLR) signaling pathway. Therefore, using GEPIA, we analyzed the correlation between
OAS3 and six genes related to the NLR signaling pathway (PLCB2 (PLCβ), CXCL8 (IL-8), BIRC3
(cIAP), NLRP7, TNFAIP3 (A20), and CARD16). Positive correlations were found for all genes, except
NLRP7 (Fig.1 G-L). In summary, these results indicate that OAS3 is significantly upregulated in GC
tissues, serves as an adverse prognostic factor, may be involved in the NLR signaling pathway in GC,
and may be significant for the development and progression of GC.
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Fig.1 OAS3 is overexpressed in GC and is associated with poor prognosis. (A)Expression of OAS3 mRNAacross various cancer
types; red indicates high expression, and green indicates low expression. (B)Expression level of OAS3 in GC tissues analyzed by
TIMER3.0. (C) Expression level of OAS3 in GC tumor tissues compared with matched adjacent non-tumor tissues based on GC
RNA-Seq data. (D-F) Kaplan–Meier survival curves for OS,PFS, and PPS of GC patients stratified by high and low OAS3
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mRNAexpression levels. (G-L)Correlation analysis between OAS3 and genes related to the NLR signaling pathway performed
using GEPIA. Asterisks indicate statistically significant differences (*P < 0.05; **P < 0.01; ***P < 0.001).
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Table.1 TheexpressionpatternofPPP2R1Aindifferenttumortissues
Typeofcancer
Sample OAS3
expression
pattern
Psignif Tumor(n) Normal(n)
BLCA(BladderUrothelial
Carcinoma) 406 19 Up ***
BRCA(Breastinvasive
carcinoma) 1086 113 Up ***
CESC
(Cervicalsquamouscell
carcinomaandendocervical
adenocarcinoma)
304 3 Up **
CHOL(Cholangiocarcinoma) 35 9 Up ***
COAD(Colon
adenocarcinoma) 456 41 Up ***
ESCA(Esophageal
carcinoma) 184 13 Up ***
HNSC(HeadandNeck
squamouscellcarcinoma) 520 44 Up ***
KICH(KidneyChromophobe) 66 25 Down ***
KIRC(Kidneyrenalclearcell
carcinoma) 533 72 Up ***
KIRP(Kidneyrenalpapillary
cellcarcinoma) 290 32 Up **
LIHC(Liverhepatocellular
carcinoma) 371 50 Up ***
LUAD(Lung
adenocarcinoma) 516 59 Up ***
LUSC(Lungsquamouscell
carcinoma) 501 51 Up NA
PAAD(Pancreatic
adenocarcinoma) 178 4 Up NA
PCPG(Pheochromocytoma
andParaganglioma) 179 3 Up *
PRAD(Prostate
adenocarcinoma) 497 52 Up NA
READ(Rectum
adenocarcinoma) 165 10 Up ***
STAD(Stomach
adenocarcinoma) 412 36 Up ***
THCA(Thyroidcarcinoma) 505 59 Up NA
UCEC(UterineCorpus
EndometrialCarcinoma) 545 23 Up ***
NA（NotAvailable）;*：p<0.05;**：p<0.01;***：p<0.001
Correlation of OAS3 With Immune-Infiltrating Cells in GC
Our GC RNA-Seq analysis identified 1,827 differentially expressed genes (DEGs), including 1,133
upregulated and 694 downregulated genes (Fig. 2 A,B). Gene Ontology(GO) functional enrichment
analysis of the DEGs revealed that the upregulated genes in biological processes (BP) were primarily
enriched in pathways related to immune response, cell migration, and cell adhesion (Fig. 2 C).
Conversely, the downregulated genes were mainly enriched in pathways such as the negative regulation
of growth and cellular response to zinc ions (Fig. 2 D). Consequently, we investigated the relationship
between OAS3 and GC immune cell infiltration using the TIMER online tool with the TIMER, ABIS,
EPIC, Xcell, CIBERSORT, ImmuCellAI, and CONSENSUS methods. The results showed that after
purity adjustment, OAS3 expression in GC was significantly positively correlated with seven types of
immune-infiltrating cells, including CD8 + T cells, CD4 + T cells, macrophages, Tregs, B cells,
dendritic cells (DCs), and NK cells, as well as with neutrophils (Fig.2 E, F). The strongest positive
correlations were observed with CD8 + T cells, DCs, and macrophages. Interestingly, among the CD8
+ Tcell subtypes, OAS3 was positively correlated with effector T cells and memory T cell subsets, but
negatively correlated with naïve T cells. Among the CD4 + T cell subtypes, most subtypes showed a
positive correlation, whereas T cell CD4⁺ central memory, T cell CD4⁺ memory resting, Th1, and Th2
cells exhibited negative correlations (Fig.S1 A-B). Furthermore, compared to GC patients with high
OAS3 expression and low macrophage levels, patients with concurrently elevated OAS3 and
macrophage levels had significantly shorter overall survival (OS), progression-free survival (PFS), and
disease-specific survival (DSS), indicating a poorer prognosis (Fig.2 G-I). In summary, these results
suggest that OAS3 overexpression may promote an immunosuppressive tumor microenvironment by
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enhancing interactions with tumor cells via macrophages, CD8 + T cells, DCs, and other
immune-infiltrating cells.
Fig.2 In GC, the expression of OAS3 is positively correlated with immune-infiltrating cells, and concurrent elevation of OAS3
and macrophages is associated with poorer prognosis. (A,B) Volcano plot and cluster heatmap of DEGs in GC RNA-Seq. (C,D)
Bubble charts of the top 10 upregulated and downregulated Biological Process terms from GO enrichment analysis of GC
RNA-Seq. (E,F) Using the TIMER tool, the correlation between OAS3 and CD8+T cells, CD4+ T cells, macrophages, Tregs, B
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cells, DCs, NK cells, and neutrophils in GC was analyzed. (G–I) In TIMER, the impact of OAS3 and macrophage expression on
OS, PFS, and DSS in GC patients was evaluated.
Silencing OAS3 significantly reduces the proliferation of GC cell lines
To elucidate the functional role of OAS3 in GC, the AGS cell line was used. The relative expression of
OAS3 at the mRNA level was determined by RT-qPCR. It was confirmed that when the internal
reference CT value was approximately 15, the expression level of the OAS3 gene was relatively high
(Table S3). The basal protein expression of OAS3 was examined by western blotting and a distinct
band was observed (Fig.3 A). Subsequently, stable OAS3-knockdown cell lines were established in an
AGS cell model by transfection with OAS3-targeting siRNA. The efficiency of the OAS3 knockdown
was verified using RT-qPCR and Western blotting. The results demonstrated a significant reduction in
both the OAS3 mRNA (P <0.0001, Fig.3 B) and protein levels (P <0.01, Fig.3 C,D). AGS cells
transfected with si_OAS3_1411, which showed the most effective knockdown (RT-qPCR, P <0.0001;
WB, P <0.001), were used for subsequent experiments. To assess the proliferative capacity of the
transfected AGS cell lines, a CCK-8 assay was performed. The results indicated that knockdown of
OAS3 in AGS cells significantly inhibited cell proliferation at 48h (P < 0.0001) and 72h (P < 0.01)
post-transfection (Fig.3 E-I).
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Fig.3 Knockdown of OAS3 expression significantly inhibited the proliferation of GC cell lines. (A) Western blot analysis was
performed to verify the endogenous protein expression of OAS3 in Hela and AGS cells. (B) The relative mRNA level of OAS3
was assessed by RT-qPCR following OAS3 knockdown in the AGS cell line. (C,D) The relative protein level of OAS3 was
evaluated by Western blot after OAS3 knockdown in the AGS cell line. (E-I) The relative proliferative capacity of AGS cells
treated with OAS3 knockdown was assessed by CCK-8 assay at 0 h, 24 h, 48 h, and 72 h. Data are presented as mean ± standard
deviation. Asterisks indicate statistically significant differences (*P< 0.05; **P< 0.01; ***P< 0.001; ****P< 0.0001). siNC:
control group transfected with non-targeting siRNA.
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Silencing OAS3 significantly reduces the invasion and migration and increases the apoptosis of
GCcell line
To evaluate the invasive and migratory capabilities of the transfected GC cell lines, a Transwell assay
was performed. The results demonstrated that, compared with the control group, knockdown of
OAS3-1411 significantly inhibited the migration (P < 0.01) and invasion (P < 0.01, Fig.4 A,B) of AGS
cells.
Flow cytometry was performed to assess the level of apoptosis in the transfected GC cell lines.
The results demonstrated that silencing of OAS3 significantly promoted apoptosis in AGS cells at 48h
post-transfection (P < 0.01, Fig.4 C,D).
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Fig.4 Knockdown of OAS3 expression inhibited the invasion and migration capabilities and promotes apoptosis of GC cells.
(A,B) Transwell assays were performed to determine the invasion and migration capabilities of AGS cells following OAS3
knockdown. Representative images are shown at 48 hours. Data are presented as mean ± standard deviation. Asterisks indicate
statistically significant differences. (C,D) Quadrant plots and statistical analysis of flow cytometry for the si-NC control group
and the OAS3 knockdown group. Data are presented as mean ± standard deviation. Asterisks indicate statistically significant
differences (*P< 0.05; **P< 0.01; ***P< 0.001). Apoptotic cells: All Annexin V-FITC-positive cells (Annexin V+/PI+; Annexin
V+/PI-).
Downregulation of OAS3 may lead to dysregulation of protein binding on the cell membrane,
impairing signal transduction, thereby affecting the Cytokine-cytokine receptor interaction
signaling pathway.
To elucidate the molecular mechanism by which OAS3 regulates GC, a Gene Co-expression Network
analysis tool was used to identify genes potentially interacting with OAS3. Among the genes
co-varying with OAS3, Interferon Alpha Inducible Protein 6 (IFI6) exhibited a strong positive
correlation with OAS3 expression (R=0.86, P < 0.01, Fig.5 A). Furthermore, analysis using the TIMER
2.0 online tool indicated that OAS3 mRNA expression was significantly increased in GC tissues (P <
0.001), and a significant positive correlation was observed between IFI6 and OAS3 in GC (P <0.001,
Fig.S1C,D). Concurrently, analysis of GEPIA also demonstrated high expression of IFI6 in GC
(Fig.5B). Subsequently, analysis of the GC RNA-Seq results confirmed that IFI6 expression was
significantly elevated in GC (P <0.001) (Fig.5C). However, subsequent western blot validation
revealed no significant difference in IFI6 protein levels following knockdown of OAS3 expression in
AGS cells (Fig.5D), suggesting that the impact of OAS3 on GC development may be independent of
IFI6.
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Subsequently, high-throughput transcriptome sequencing (siOAS3 RNA-Seq) was performed for
the siOAS3 and siNC groups. Under the filtering criteria of fold change (FC) ≥ 2 or ≤ 0.5 and false
discovery rate (FDR) < 0.05, 663 differentially expressed genes (DEGs) were identified, comprising
260 downregulated and 403 upregulated genes. Notably, IFI6 was not among these DEGs, providing
further validation of the aforementioned conclusion. Overlap analysis with previous GC RNA-Seq
results identified 60 common DEGs. Among these, 35 genes (including OAS3) were downregulated in
siOAS3 RNA-Seq and upregulated in GC RNA-Seq, and 10 genes showed the opposite pattern
(upregulated in siOAS3 RNA-Seq and downregulated in GC RNA-Seq) (Table S4). KEGG pathway
enrichment analysis revealed 48 significantly enriched pathways (P < 0.05; Fig.5E,F). Analysis of the
GC RNA-Seq data identified 91 significantly enriched KEGG pathways (P < 0.05, Fig.5G).
Intersection analysis of these pathways indicated that “Cytokine-cytokine receptor interaction” and
“Viral protein interaction with cytokine and cytokine receptor” were the most significantly affected by
OAS3 knockdown (Fig.5H,I). It was noted that the “NOD-like receptor signaling pathway,” directly
related to OAS3, was also present in the intersection (Table S5). Concurrently, Gene Ontology (GO)
enrichment analysis identified 47 significantly enriched Biological Process (BP) terms, 42 Cellular
Component (CC) terms, and 32 Molecular Function (MF) terms in the siOAS3 RNA-seq data, and 328
BP, 84 CC, and 81 MF terms in the GC RNA-Seq dataset (P< 0.05, Table S6). The intersection of these
enriched terms revealed 30 overlapping BP, 28 CC, and 20 MF terms (Fig.5J-M). The most
significantly enriched GO terms included “signal transduction” (GO:0007165, BP), “plasma
membrane” (GO:0005886, CC), and “protein binding” (GO:0005515, MF) (Fig.5N). It is worth
mentioning that among the intersecting BP terms, “innate immune response” ranked third in
significance and was the only BP term in the siOAS3 RNA-seq data in which OAS3 was directly
20
involved. OAS3 is also directly implicated in the “plasma membrane” and “protein-binding” pathways.
Interestingly, intersection analysis of DEGs enriched in the “innate immune response” BP term from
both the transcriptomes yielded four common DEGs. Among these, CD55 was the most significantly
affected by OAS3 knockdown and exhibited high consistency with OAS3 across BP, CC, and MF
analyses (Table S7). In summary, these data suggest that downregulation of OAS3 may lead to
dysregulation of protein binding to the plasma membrane, impair signal transduction or the innate
immune response, and consequently affect cytokine-cytokine receptor interactions.
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Fig.5 Downregulation of OAS3 may lead to dysregulation of protein binding on the plasma membrane, impair signal
transduction, and consequently affect the Cytokine-cytokine receptor interaction signaling pathway. (A) OAS3-interacting
proteins obtained based on a gene co-expression network. (B) Expression of IFI6 in GC as indicated by GEPIA. (C) Expression
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levels of IFI6 in GC tissues and adjacent non-tumor tissues based on GC RNA-Seq analysis. (D) Western blot validation of
relative IFI6 protein levels following OAS3 knockdown in AGS cells. (E, F) Bar graph and bubble plot showing the top 10
upregulated and downregulated KEGG pathways enriched based on siOAS3 RNA-Seq data after OAS3 knockdown. (G) Bar
graph showing the top 10 upregulated and downregulated KEGG pathways enriched based on GC RNA-Seq data. (H) Venn
diagram illustrating the overlap of enriched KEGG pathways between the two datasets. (I) The top two common enriched KEGG
pathways are “Cytokine-cytokine receptor interaction” (hsa04060) and “Viral protein interaction with cytokine and cytokine
receptor” (hsa04061). (J,K) Bar graph and bubble plot showing the top 10 upregulated and downregulated GO terms for BP, CC,
and MF categories based on DEGs from siOAS3 RNA-Seq data. (L) Bar graph showing the top 10 upregulated and
downregulated GO terms for BP, CC, and MF categories based on GC RNA-Seq data. (M) Venn diagrams illustrating the overlap
of enriched GO_BP, CC, and MF terms between the two datasets. (N) The most significantly overlapping GO_BP, CC, and MF
terms in the two datasets.
Discussion
This study systematically evaluated the expression characteristics and biological functions of the
RNA-binding protein OAS3 in GC along two main lines: tissue specimens and ex vivo functional
assays, and investigated its upstream and downstream genes and signaling pathways using RNA-seq
cross analysis. In our GC RNA-seq data from five cases, OAS3 was confirmed to be significantly
overexpressed in the tumor tissues. Moreover, this high OAS3 expression level is closely associated
with poor prognosis. Functionally, OAS3 knockdown significantly inhibited the proliferation, invasion,
and migration of GC cells, and induced apoptosis. These results suggest that OAS3 exhibits a typical
“pro-proliferation to pro-metastasis to anti-apoptosis” oncogenic phenotype during GC progression,
indicating its potential value as a prognostic biomarker and therapeutic target.
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RBPs are a class of molecules that play crucial roles in regulating gene expression. By
modulating RNA metabolism, participating in cell proliferation, differentiation, apoptosis, and
interactions within the tumor microenvironment, thereby influencing tumor progression[20–23]. They also
play key roles in the occurrence, metastasis, and drug resistance of gastrointestinal tumors[24]. Studies
have reported that RBPs in GC can directly bind to and stabilize mRNA, inhibit apoptosis[25]and
promote cell migration and liver metastasis[14]. RBPs can also act as oncogenic splicing factors in GC,
regulating alternative splicing (AS) and the differential expression of related genes[26], or
reprogramming lipid metabolism by forming ribonucleoprotein complexes to promote GC progression
or enhance cisplatin resistance[27]. The 2′-5′-oligoadenylate synthetase (OAS) family comprises
interferon-induced enzymes that are primarily encoded by the OAS1, OAS2, and OAS3 genes. OAS3
plays a central role in the classical OAS–RNase L pathway by inducing widespread RNA degradation
and apoptosis, exerting antiviral and antitumor effects[28,29]. Traditionally, this pathway was thought to
have a clear antiviral and antitumor orientation. However, OAS3 is the main OAS homolog responsible
for activating RNase L in human cells, and its deficiency fails to effectively activate RNase L in
various viral infections [30]. Notably, members of the OAS family also possess atypical RBP functions.
Munesh et al. reported that OAS1 selectively maintains the stability of IFNβ mRNA and sustains innate
antiviral protection[31]. OAS3 expression has been associated with prognosis and chemotherapy
outcomes in various cancers and is positively correlated with the infiltration of immunosuppressive
cells[17]. High expression of OAS genes can reflect the degree of immune cell infiltration in the tumor
microenvironment, thereby predicting better overall survival in bladder cancer (BLCA) [32]. It can also
act as a regulatory factor, inducing immune evasion in pancreatic cancer (PC) by promoting M2d
polarization to reduce CD8 + T cell cytotoxicity[19]. In acute myeloid leukemia (AML), upregulated
24
OAS3 promotes cell proliferation by regulating the JAK-STAT signaling pathway[33]. Most of these
reports focused on the perspective of immune infiltration; however, whether OAS3 can also function as
an RBP to influence tumor development remains unexplored. Our study demonstrated that OAS3 is
highly expressed in GC and positively correlated with immune-infiltrating cells. Combined with the
high enrichment of “protein binding” in our Gene Ontology (GO) results and the direct annotation of
OAS3 to the “protein binding” and “plasma membrane” terms, it is reasonable to speculate that OAS3
may also exert RBP-like functions in GC cells by binding to specific mRNAs or non-coding RNAs that
participate in post-transcriptional regulatory networks, thereby reprogramming signal transduction and
immune responses in GC.
Transcriptomics has indicated that OAS3 is primarily involved in cell membrane signal
transduction and innate immune responses. To further elucidate the molecular interaction network of
OAS3 in GC cells, we performed a cross-analysis of differentially expressed genes (DEGs) from
siOAS3 RNA-seq and GC RNA-seq and identified 60 overlapping DEGs. Among these, 35 genes
(including OAS3) were upregulated in GC tissues but downregulated in siOAS3 cells, and another 10
genes were downregulated in GC tissues but upregulated in siOAS3 cells. This “reverse change”
pattern suggests that these genes may be located in key downstream pathways mediated by OAS3,
holding a central position in tumorigenesis and OAS3 function. Intersection analysis of KEGG
pathway enrichment from both datasets revealed that “Cytokine-cytokine receptor interaction” and
“Viral protein interaction with cytokine and cytokine receptor” were the most significantly affected
common pathways upon OAS3 knockdown, suggesting that OAS3 downregulation may primarily
perturb the cytokine-receptor network, thereby altering intercellular signaling and the immune
microenvironment. Furthermore, we noted that the NOD-like receptor signaling pathway, to which
25
OAS3 was annotated, also appeared among the intersecting KEGG pathways. This pathway is a typical
pattern recognition receptor and innate immunity pathway, closely related to the OAS-RNase L system
and inflammasome activation[28–30]. The most highly enriched GO terms from the intersection analysis
were: Biological Process (BP), signal transduction involving various receptors and downstream
signaling cascades; Cellular Component (CC), plasma membrane, pointing to the cell membrane and
membrane-associated protein complexes; Molecular Function (MF), protein binding, suggesting
numerous protein-protein or protein-RNA binding events. It is worth emphasizing that among the
intersecting BP terms, “innate immune response” ranked only third in significance, but was the sole BP
term directly involving OAS3 in the siOAS3 RNA-seq data. In the CC term “plasma membrane” and
the MF term “protein binding,” OAS3 was also directly annotated. This result is consistent with our
speculation regarding the dual functions of OAS3 in “membrane-associated signal transduction and
innate immune responses.” Further cross-analysis of DEGs belonging to the “innate immune response”
term from both RNA-seq datasets yielded four common genes, among which CD55 showed the most
significant expression change and exhibited highly consistent annotations with OAS3 across BP, CC,
and MF terms. CD55 is a classical complement regulatory protein localized to the cell membrane that
plays a crucial role in complement-mediated cytotoxicity and innate immune responses. It has been
implicated in immune evasion and chemotherapy resistance in various tumors[34,35]. Our results suggest
that OAS3 downregulation significantly inhibits CD55 expression, potentially interfering with innate
immune responses and the cytokine-receptor signaling axis by altering complement regulation and
protein-binding patterns on the cell membrane. Considering OAS3 as an interferon-stimulated gene and
its potential RBP function, OAS3 downregulation may disrupt the balance of the “cell membrane
protein binding–signal transduction–innate immune response” chain, thereby reshaping the key
26
“cytokine-cytokine receptor interaction” pathway and providing transcriptomic evidence for its
pro-tumorigenic role in GC.
At the immune cell level, GC with upregulated OAS3 exhibited higher infiltration of CD8 + T
cells, CD4+ T cells, regulatory T cells (Tregs), B cells, dendritic cells (DCs), macrophages, and natural
killer (NK) cells, with CD8 + T cells, DCs, and macrophages being particularly prominent. This
indicates that GC with high OAS3 expression reside in an immunosuppressive “immune-inflammatory”
tumor microenvironment characterized by high immune cell infiltration and active IFN/inflammatory
signaling. This aligns with the previous KEGG/GO enrichment of terms such as “cytokine–cytokine
receptor interaction,” “innate immune response,” “plasma membrane,” and “protein binding”: GC with
high OAS3 expression may secrete/respond to large amounts of cytokines, attracting various immune
cells into the tumor. However, further analysis revealed that among CD8⁺ T cells, antigen-experienced
effector and memory T cells predominated, whereas naive T cells were reduced. Among CD4 + T cells,
classically favorable anti-tumor subsets such as Th1, Th2, and central/resting memory T cells were
negatively correlated with OAS3, resembling a population of T cells that are “chronically
antigen-stimulated, functionally remodeled, or even exhausted,” rather than a potent anti-tumor force.
Further stratification analysis revealed that when patients had concurrent high expression of OAS3 and
high macrophage infiltration, their disease duration was significantly shorter, with the poorest
prognosis. This indicates that the high expression of OAS3 and the high infiltration of macrophages
place the tumor in a state where many immune cells are present, but the overall immunity is inhibited.
This study has some limitations. First, the sample size for GC RNA-seq was limited, and further
validation of OAS3 expression patterns and prognostic value in larger tissue cohorts is required.
Second, this study was confined to bioinformatics analysis, in vivo experiments, and transcriptomics,
27
lacking in vivo experimental validation. Third, although we delineated the downstream pathway
spectrum of OAS3 through transcriptomics, evidence of direct RNA targets and protein interactions is
lacking. Future studies could use RIP-seq/CLIP-seq and proteomics to finely dissect its dual
RBP/enzymatic functions.
Conclusions
Our study incorporated OAS3 into the molecular profile of GC. By combining RNA-seq cross analysis
and functional experiments, we revealed that OAS3 plays a central role in cell membrane signal
transduction, innate immune response, and cytokine-receptor pathways. Furthermore, bioinformatic
and transcriptomic analyses demonstrated that in GC cases with high OAS3 expression, infiltrating
immune cells exhibit a state of “immune remodeling/exhaustion”. This study validates that OAS3 is
upregulated in GC and is associated with poor prognosis, and that OAS3 knockdown inhibits the
proliferation, invasion, and migration of GC cells while inducing apoptosis. This position of OAS3
represents a novel GC target with clinical translational potential. Subsequent in-depth research and
targeted intervention focusing on the OAS3-CD55-cytokine-receptor signaling axis are expected to
provide new entry points and strategies for precise treatment and immunotherapy of GC.
List of abbreviations
GC:Gastric cancer
OAS3 : 2′-5′oligoadenylate synthetase 3
RBP: RNA-binding protein
LUAD:lung adenocarcinoma
28
KIRP: kidney renal papillary cell carcinoma
LIHC : liver hepatocellular carcinoma
PC: pancreatic cancer
OS: overall survival
FP: first progression
PPS : post-progression survival
DEGs : differentially expressed genes
DCs : dendritic cells
PFS : progression-free survival
DSS : disease-specific survival
IFI6: Interferon Alpha Inducible Protein 6
FC : fold change
FDR : false discovery rate
GO:Gene Ontology
BP: Biological Process
CC: Cellular Component
MF: Molecular Function
29
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the Affiliated Tumor Hospital of Xinjiang
Medical University (Approval No. S-2024156).
Consent for publication
Not applicable
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author
upon reasonable request.
Competing Interests
The authors declare that they have no competing interests
Funding
This work was supported by the Central Guidance of Local Science and Technology Development
Special Fund Project (ZYYD2024CG06) and School-level Natural Science Youth Research Project of
Xinjiang Medical University (2024XYZR55).
Author contributions
All the authors contributed to the conception and design of the study. Material preparation, data
collection, and analysis were performed using WZ, LW,DS,PL,FY, and WW software. The first draft of
the manuscript was written by ZW, and YS provided research supervision. All authors have read and
approved the final manuscript.
30
Acknowledgements
Not applicable
Authors’ information
Zhixin Wan and Wenbin Zhang contributed equally to this study.
Department of gastrointestinal surgery of The Third Clinical Medical College of Xinjiang
Medical University, 830001, Urumqi, Xinjiang UygurAutonomous Region, China
Zhixin Wan
Department of gastrointestinal surgery of The Third Clinical Medical College of Xinjiang
Medical University, Center Director and Professor,830001, Urumqi, Xinjiang UygurAutonomous
Region, China
Wenbin Zhang
Department of gastrointestinal surgery of The Third Clinical Medical College of Xinjiang
Medical University, 830001, Urumqi, Xinjiang UygurAutonomous Region, China
Yin Shu
References
Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of
incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for
Clinicians, 2024, 74(3): 229-263.
López M J, Carbajal J, Alfaro A L, et al. Characteristics of gastric cancer around the world[J].
Critical Reviews in Oncology/Hematology, 2023, 181: 103841.
Thrift A P, Wenker T N, El-Serag H B. Global burden of gastric cancer: epidemiological trends,
risk factors, screening and prevention[J]. Nature Reviews. Clinical Oncology, 2023, 20(5):
338-349.
Machlowska J, Baj J, Sitarz M, et al. Gastric Cancer: Epidemiology, Risk Factors, Classification,
31
Genomic Characteristics and Treatment Strategies[J]. International Journal of Molecular
Sciences, 2020, 21(11): 4012.
[5]
[6]
[7]
[8]
[9]
Ilic M, Ilic I. Epidemiology of stomach cancer[J]. World Journal of Gastroenterology, 2022,
28(12): 1187-1203.
Smyth E C, Nilsson M, Grabsch H I, et al. Gastric cancer[J]. The Lancet, 2020, 396(10251):
635-648.
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of
gastric adenocarcinoma[J]. Nature, 2014, 513(7517): 202-209.
Mou P, Ge Q hua, Sheng R, et al. Research progress on the immune microenvironment and
immunotherapy in gastric cancer[J]. Frontiers in Immunology, 2023, 14: 1291117.
Lei Z N, Teng Q X, Tian Q, et al. Signaling pathways and therapeutic interventions in gastric
cancer[J]. Signal Transduction and Targeted Therapy, 2022, 7: 358.
[10] Cheng G, Wusiman L, Song D, et al. Silencing PPP2R1A inhibits the progression of gastric
cancer cells[J]. Journal of Cancer Research and Clinical Oncology, 2025, 151(4): 142.
[11] Perez-Perri J I, Noerenberg M, Kamel W, et al. Global analysis of RNA-binding protein
dynamics by comparative and enhanced RNA interactome capture[J]. Nature Protocols, 2021,
16(1): 27-60.
[12] Xiao R, Chen J Y, Liang Z, et al. Pervasive Chromatin-RNA Binding Protein Interactions Enable
RNA-Based Regulation of Transcription[J]. Cell, 2019, 178(1): 107-121.e18.
[13] Chen C, Zheng Q, Pan S, et al. The RNA-Binding Protein NELFE Promotes Gastric Cancer
Growth and Metastasis Through E2F2[J]. Frontiers in Oncology, 2021, 11: 677111.
[14] Wang X, Liang C, Wang S, et al. RNA Binding Protein PTBP1 Promotes the Metastasis of
Gastric Cancer by Stabilizing PGK1 mRNA[J]. Cells, 2024, 13(2): 140.
[15] Wang N, Qiao H, Hao J, et al. RNA-binding protein ENO1 promotes the tumor progression of
gastric cancer by binding to and regulating gastric cancer-related genes[J]. Journal of
Gastrointestinal Oncology, 2023, 14(2): 585-598.
[16] Cusic R, Burke J M. OAS3 and RNase L integrate into higher-order antiviral condensates[A].
2023.
[17] Li X yu, Hou L, Zhang L yu, et al. OAS3 is a Co-Immune Biomarker Associated With Tumour
Microenvironment, Disease Staging, Prognosis, and Treatment Response in Multiple Cancer
Types[J]. Frontiers in Cell and Developmental Biology, 2022, 10: 815480.
[18] Zhang Y, Yu C. Prognostic characterization of OAS1/OAS2/OAS3/OASL in breast cancer[J].
BMCCancer, 2020, 20(1): 575.
[19] Zhang S, Xu X, Zhang K, et al. Targeting OAS3 for reversing M2d infiltration and restoring
anti-tumor immunity in pancreatic cancer[J]. Cancer Immunology, Immunotherapy, 2024, 74(1):
37.
[20] Gu Z, Zhang S, Hu X, et al. Bibliometric analysis of RNA-binding proteins in osteosarcoma:
unraveling research trends and hotspots[J]. Frontiers in Immunology, 2025, 16: 1577261.
[21] Zhang C, Chen Y, Zhu S, et al. Targeting CLK1/SRSF7 axis-dependent alternative splicing
sensitizes pancreatic ductal adenocarcinoma to chemotherapy and immunotherapy[J]. Drug
Resistance Updates, 2025, 83: 101292.
[22] Yang S, Zeng J, Wang Y, et al. Single-cell and bulk transcriptome profiling reveals RNA-binding
protein regulatory programs in cervical cancer[J]. Scientific Reports, 2025, 15(1): 29538.
[23] An J, Sun W, Ti C, et al. circHMGCS1 Promotes the Progression of NSCLC by Regulating the
32
HuR/SFPQ/TNF Pathway[J]. The FASEB Journal, 2025, 39(17): e71002.
[24] Shi Y, Zhou Z, Liu C, et al. Post-transcriptional regulation by HuR in colorectal cancer: impacts
on tumor progression and therapeutic strategies[J]. Frontiers in Immunology, 2025, 16: 1658526.
[25] Peng H, Zhang L, Li F, et al. RBM8A promotes gastric cancer progression by binding with
UPF3B to induce BBC3 mRNA degradation[J]. International Journal of Molecular Medicine,
2025, 56(3): 1-19.
[26] X C, H W, A Y, et al. KPNA2 promotes the progression of gastric cancer by regulating the
alternative splicing of related genes[J]. Scientific reports, 2024, 14(1).
[27] Zhao P, Yuan F, Xu L, et al. HKDC1 reprograms lipid metabolism to enhance gastric cancer
metastasis and cisplatin resistance via forming a ribonucleoprotein complex[J]. Cancer Letters,
2023, 569: 216305.
[28] Chakrabarti A, Banerjee S, Franchi L, et al. RNase L activates the NLRP3 inflammasome during
viral infections[J]. Cell Host & Microbe, 2015, 17(4): 466-477.
[29] Ibsen M S, Gad H H, Thavachelvam K, et al. The 2’-5’-oligoadenylate synthetase 3 enzyme
potently synthesizes the 2’-5’-oligoadenylates required for RNase L activation[J]. Journal of
Virology, 2014, 88(24): 14222-14231.
[30] Li Y, Banerjee S, Wang Y, et al. Activation of RNase L is dependent on OAS3 expression during
infection with diverse human viruses[J]. Proceedings of the National Academy of Sciences, 2016,
113(8): 2241-2246.
[31] Harioudh M K, Perez J, Chong Z, et al. Oligoadenylate synthetase 1 displays dual antiviral
mechanisms in driving translational shutdown and protecting interferon production[J]. Immunity,
2024, 57(3): 446-461.e7.
[32] Gao L, Ren R, Shen J, et al. Values of OAS gene family in the expression signature, immune cell
infiltration and prognosis of human bladder cancer[J]. BMC Cancer, 2022, 22(1): 1016.
[33] Zhang X, Zhong L, Zhai L, et al. IRF2-driven upregulation of OAS3 promotes AML cell
proliferation by modulating the JAK-STAT signaling pathway[J]. Biochemical and Biophysical
Research Communications, 2025, 772: 152064.
[34] Jurianz K, Ziegler S, Garcia-Schüler H, et al. Complement resistance of tumor cells: basal and
induced mechanisms[J]. Molecular Immunology, 1999, 36(13-14): 929-939.
[35] Li L, Spendlove I, Morgan J, et al. CD55 is over-expressed in the tumour environment[J]. British
Journal of Cancer, 2001, 84(1): 80-86.
33