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Ding et al. Human Genomics (2024) 18:77
https://doi.org/10.1186/s40246-024-00631-7 Human Genomics
†Yixin Ding and Yangyang Lu contributed equally to this work.
*Correspondence:
Wensheng Qiu
wsqiuqdfy@qdu.edu.cn
Weiwei Qi
qwwdz@qdu.edu.com
1Department of Oncology, The Aliated Hospital of Qingdao University,
Qingdao, China
2Department of Medical Oncology, Department of Cancer Center, Peking
Union Medical College Hospital, Chinese Academy of Medical Sciences,
Beijing, China
3Department of Urology, Qingdao Municipal Hospital, Qingdao University,
Qingdao, China
Abstract
Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and
tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC).
By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate
gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis
was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with
clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental
validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted
of 66 TRIMs, of which 52 were dierentially expressed and 43 were dierentially prognostic. Signicant survival
dierences existed between the gene clusters obtained by consensus clustering analysis. Using 4 dierentially
expressed genes identied by multivariate Cox regression and LASSO regression, a risk scoring system was
developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell inltration, and
drug resistance. Transcriptomic data and clinical sample tissue microarrays conrmed that TRIM31 was highly
expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony
formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay
revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to
gastric carcinogenesis. This study investigated the whole functional and expression prole and a risk score system
based on the TRIM family in GC. Further investigation centered around TRIM31 oers insight into the underlying
mechanisms of action exhibited by other members of its family in the context of GC.
Keywords TRIM family, TRIM31, Gastric cancer, Risk score, Prognostic biomarkers
An investigation of the molecular
characterization of the tripartite motif (TRIM)
family and primary validation of TRIM31
in gastric cancer
YixinDing1,2†, YangyangLu1†, JingGuo1, ShumingChen1, XiaoxiHan1, ShiboWang1, MengqiZhang1, RuiWang1,
JialinSong1, KongjiaWang3, WenshengQiu1* and WeiweiQi1*
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Page 2 of 18
Ding et al. Human Genomics (2024) 18:77
Introduction
e prevalence and fatality rates of gastric cancer (GC)
have become significant global concern in the realm of
human health, demanding attention and action. With
the increasing depth of exploration into the molecu-
lar process of GC, a growing number of molecular tar-
gets and biomarkers have been identified. Nevertheless,
there remains a scarcity of targets possessing substantial
pharmaceutical value, and the issue of drug resistance
persists. Hence, further investigation into chemicals that
hold prognostic and carcinogenic relevance can contrib-
ute to a more comprehensive comprehension of GC.
Gene families are established through a shared ances-
tral origin and demonstrate significant structural and
functional resemblances, leading to the generation of
related protein products [1]. Numerous gene families
have been confirmed to be associated with the process of
carcinogenesis [2, 3]. e tripartite motif (TRIM) family
has more than 80 members and is primarily distinguished
by the interesting new gene (RING) domain, which serves
as an E3-ubiquitin ligase [4]. e TRIM family is known
for its active involvement in various biological processes,
including ubiquitination, regulation of immunological
response, and cancer, owing to its distinctive domain [5].
Multiple studies have provided evidence indicating that
the expression levels of TRIM family genes are associ-
ated with negative clinical outcomes in various types of
cancer, including acute myeloid leukemia (AML), hepa-
tocellular carcinoma (HCC), colorectal cancer (CRC),
pancreatic cancer, breast cancer, lung cancer, and GC [6–
12]. Nevertheless, there is a notable scarcity of extensive
research investigating the combined manifestation of the
TRIM family and integrating risk evaluation within the
framework of GC.
e primary aim of our study was to examine the
expression patterns and prognostic characteristics of
TRIM family members in GC. Furthermore, gene clus-
ters were obtained through the utilization of consensus
clustering analysis, with particular attention given to the
expression patterns exhibited by members of the same
family. In addition, a risk score pertaining to TRIM was
created in order to assess discrepancies in characteris-
tics such as immune infiltration, medication sensitiv-
ity, and prognosis between groups classified as high-risk
and low-risk. Moreover, the selection of TRIM31, a
member of this gene family that demonstrates prognos-
tic significance, was made to undertake comprehensive
investigation using bioinformatic analysis and in vitro
experiments.
Results
Expression and survival analyses of TRIMs
e merged RNA-Seq transcriptome data was defined
as merged-matrix, which concluded 63 TRIMs. e
expression level of TRIMs were displayed between STAD
and normal samples from TCGA (Fig. 1A). e prog-
nostic network diagram of the univariate Cox regression
analysis of TRIMs was showed in Fig.1B. ere were 52
DEMs expressed differentially in STAD merged-matrix.
e expression levels of 44 DEMs were significantly cor-
related with the OS according to the K-M analysis (Sup-
plementary Figs.1–2).
Gene alteration prole, TF-gene-miRNA and PPI network
of TRIMs
e alteration frequency of 7 TRIMs were above 2%,
including TRIM9, TRIM51, TRIM71, TRIM26, TRIM46,
TRIM37, and TRIM42. Missense mutation accounted for
the majority of gene alteration (Fig.2A). e increased
copy number was discovered in 37 TRIMs, while the
decreased copy number was discovered in 28 TRIMs
(Fig.2B). e PPI network of proteins coded by TRIMs
was showed in Fig. 2C-D. Figure 2E and F displayed
the TF-gene and gene-miRNA network of TRIMs,
respectively.
Consensus clustering analysis of TRIMs
e optimal K value was determined to be K = 2 accord-
ing to the consensus clustering analysis (Fig. 3A, Sup-
plementary Fig. 3A-G, Supplementary Fig. 5A). STAD
samples were divided into TRIM cluster A and TRIM
cluster B (Fig.3B). PCA analysis showed favorable dis-
crimination between the two clusters (Fig. 3C). K-M
analysis indicated better survival benefit of TRIM clus-
ter B than cluster A (p = 0.021) (Fig. 3D). GSVA analyses
showed glycosaminoglycan biosynthesis chondroitin sul-
fate, neuroactive ligand receptor interaction pathways
were more enriched in cluster A. While proteasome,
aminoacyl tRNA biosynthesis, RNA degradation, spliceo-
some, base excision repair were more enriched in clus-
ter B (Fig.3E). ssGSEA analysis showed that a variety of
immune cells were more enriched in TRIM cluster A,
including activated B cells, mature B cells, CD56 bright
natural killer (NK) cells, MDSC, macrophage, mast cell,
natural killer T cells, NK cells, plasmacytoid dendritic
cells (DCs), T follicular helper cells (TFH), and 1 cells.
While, activated CD4 T cells, CD56 dim NK cells, neu-
trophil, and 17 cells were more enriched in TRIM clus-
ter B (Fig.3F).
818 DEGs were obtained from the differential expres-
sion analysis between TRIM cluster A and B. GO and
KEGG enrichment analysis showed DEGs were most
enriched in components and structure of extracellular
matrix (Fig.3G-H). Gene cluster A and B were obtained
by consensus clustering analysis based on DEGs (Fig.3I,
Supplementary Fig. 3A-G, Supplementary Fig. 5B).
Gene cluster A and B exhibited more pronounced prog-
nostic disparities in comparison to TRIM cluster A
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Ding et al. Human Genomics (2024) 18:77
Fig. 1 mRNA expression prole and prognostic network of TRIMs. (A) The expression level of TRIM family CI-CXI groups between gastric STAD and nor-
mal tissues. MID1: TRIM18. MID2: TIRM1. PML: TRIM19. (B) The prognostic network diagram was drawn according to the results of Univariate Cox regres-
sion analysis between TRIM family members and OS of STAD. HR > 1 was dened as risk factors, and HR < 1 was favorable factors. ns, p ≥ 0.05; *p < 0.05;
**p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
Fig. 2 (See legend on next page.)
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Page 5 of 18
Ding et al. Human Genomics (2024) 18:77
and B (p < 0.001) (Fig. 3J). 38 TRIMs were differentially
expressed between the two gene clusters (Supplementary
Fig.5C).
Prognostic model construction and scRNA-seq analysis
based on hub DEGs
7 DEGs were screened out by the LASSO regression,
including RGS4, MYB, EDNRA, SLC27A2, TAGLN,
HTRA1, and KRT7 (Supplementary Fig. 5D). 4 hub
DEGs including RGS4, MYB, SLC27A2, and KRT7 satis-
fied the criteria of the multivariate Cox regression with
p value < 0.05 and were selected as the model constitu-
ent genes. STAD samples from the merged-matrix were
divided into a train set and a test set at a 1:1 ratio, and
the train set was used to constructed the Cox regres-
sion model and nomogram (Fig. 4A). e calibration
curve was close to the diagonal and the AUC of 1-, 3-,
and 5-years ROC curve were 0.625, 0622, and 0.646 in
all samples (Fig.4B-C). e ROC curve with AUC of the
predicted model was also obtained in train set and test
set at 1, 3, and 5-year (Supplementary Fig.5E-F).
e scRNA-seq data of our study was obtained from
the GSM5101015 diffuse-type GC sample with 2234 cell
samples. After unsupervised clustering, cell annotation
and visualization of clusters, we identified 7 clusters as 5
kinds of cell subgroups: Cytotoxic cell, Endothelial cell, B
cell, Fibroblast and Epithelial cell (Fig.4D). RGS was veri-
fied expressed mainly in fibroblast cell subgroup (Fig.4E).
High risk group displayed unfavorable survival outcome
and impressive immune cell inltration
e formula of risk score was displayed as: risk
score =(0.206021*RGS4) + (-0.09742*MYB) +
(-0.24672*SLC27A2) + (0.107758*KRT7). ALL STAD
samples were divided in to low- or high-risk group based
on the median of risk score (Fig. 5A, Supplementary
Fig.6A-B). K-M analysis showed better survival in low-
risk group (p < 0.001) (Fig .5B, Supplementary Fig.6C-D).
36 genes in TRIMs were differentially expressed between
the two groups, and TRIM cluster A and gene cluster A
had higher risk scores (p < 0.001) (Fig. 5C, Supplemen-
tary Fig. 6E). Sankey diagram showed the distribution
of STAD samples between TRIM clusters, gene clusters,
risk groups and clinical outcomes (Fig.5D).
e stromal score and ESTIMATE score were higher
in the high-risk group (Fig. 5E). Immune cell infiltra-
tion analysis showed the infiltration of resting mast
cells, memory B cells, M2, monocytes, resting memory
CD4 + T cells and Tregs were positively related to risk
scores, while activated memory CD4 + T cells, TFHs, M0,
M1, resting NK cells, and plasma cells were negatively
related to risk scores. In particular, resting mast cells,
activated memory CD4 + T cells, and TFHs were closely
related to risk scores (|Spearman correlation coefficient|
>0.2) (Fig.5F, Supplementary Fig.5F).
High risk group was connected with MSS/MSI-L status and
higher IC50 of drugs
Patients belong to the low-risk group had the tendency
to present MSI-H status (Fig.6A-B). Significantly statis-
tical difference of IC50 of multiple drugs were displayed
between high and low risk group. Higher IC50 of drugs
(chemotherapy drugs: 5-Fluorouracil, cisplatin, cyclo-
phosphamide, docetaxel, oxaliplatin, gemcitabine, iri-
notecan, paclitaxel, and so on; targeted drugs: afatinib,
dabrafenib, erlotinib, gefitinib, lapatinib, sorafenib, and
so on) was found in high group (Fig.6C).
TRIM31 was up-expressed on gastric tissues and indi-
cated unfavorable clinical prognosis.
We found that the TRIM31 was overexpressed in
STAD compared with the normal and paracancerous tis-
sues (Fig.7A). Patients with distance metastasis displayed
higher TRIM31 expression (Supplementary Fig.7A). e
ROC curve indicated the favorable identification abil-
ity in GC of TRIM31 (Fig.7B). In STAD samples, the OS
of TRIM31 high expression group was better than that
of TRIM31 low expression group (Fig.7C). e N stage,
age, primary therapy outcome and the expression level of
TRIM31 were proved to be the independent prognostic
risk factor of the multivariate Cox regression analysis,
and were included to construct the prognostic model and
the nomogram (Fig.7D-E).
e IHC score of STAD samples were significantly
higher than paracancerous samples in tissue microarray
chips (Table1). Clinical characteristics of STAD patients
in tissue microarray chips was shown in (Supplemen-
tary Tabel 1). TRIM31-low group was verified with lon-
ger survival time than TRIM31-high group based on the
(See gure on previous page.)
Fig. 2 Gene mutation prole and interaction networks of TRIMs. (A) Visualization of the somatic mutation prole of TRIMs from 433 samples of TCGA-
STAD. Genes are ordered by their mutation frequencies. The mutation frequency and number of samples for each gene are shown on the right. The bot-
tom side shows information about the nucleic acid mutation sites and the type of mutation. (B) The CNV frequency of TRIMs. The values represented by
the red circles indicated the gain frequency of the gene, while the green circles indicated the loss frequency. (C) The PPI network of TRIMs used Cytoscape
software. The ordering of “Degree” is quantized using the size and color of the circle. Larger circles and darker colours represent stronger interactions. (D)
The network of functional and physical protein associations of TRIMs by the STRING database with 0.400 interaction score. (E) The TF-gene interaction
network constructed by the ENCODE database with the peak intensity signal < 500 and the predicted regulatory potential score < 1. Red circles represent
TRIMs, blue squares represent transcription factors. The size of the red circle was positively related to the number of interacted TFs. (F) The gene-miRNA
interaction network by the miRTarBase database. Red circles represent TRIMs, blue squares represent miRNAs. The size of the red circle was positively
related to the number of interacted miRNAs.
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Ding et al. Human Genomics (2024) 18:77
Fig. 3 (See legend on next page.)
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Page 7 of 18
Ding et al. Human Genomics (2024) 18:77
ICH score in STAD samples, the mOS was 39m (95%CI
35-NA) and 31 m (95%CI 26–42) (P = 0.0471), respec-
tively (Fig.7F). IHC score of TRIM31, age, T stage and N
stage were verified to be correlated with the overall sur-
vival of STAD patients in univariate Cox regression anal-
ysis and were submitted to multivariate analysis (Table2).
Deep research on TRIM31 based on bioinformatics analysis
7 mi RNAs and 25 TFs were verified to have interaction
with TRIM31 (Supplementary Fig. 7B). DEGs between
the TRIM31-high and TRIM31-low group were selected
with the threshold mentioned before. 4424 positively
and 1478 negatively co-expressed genes of TRIM31 were
selected and the top 300 co-expressed genes were used
to construct the PPI network (Supplementary Fig. 7C).
We identified the top 9 enrichment items of BP, CC,
MF, and KEGG pathways (Supplementary Fig.7D). Cell
cycle, oxidative stress and cell stress, and tumorigenesis
related pathways were verified to be strongly connected
with TRIM31 by GSEA analysis (Fig.7G-I, Supplemen-
tary Fig.7E). Especially, lower expression of TRIM31 was
related with the activation of FOXO mediated transcrip-
tion of oxidative stress. Moreover, immune cells includ-
ing aDC, mast cells, NK cells, pDC, T helper, and Treg
were positively related to TRIM31, while the stomal
score was negatively related to TRIM31 (Supplementary
Fig.7F-G). Finally, we evaluated the relationship of the
sensibility of GC common chemotherapy drugs and the
expression of TRIM31. IC50 of 5-Fluorouracil and pacli-
taxel were negatively related to TRIM31, while cisplatin
and docetaxel were positively related to TRIM31 which
indicated that the expression of TRIM31 may serve as a
biomarker of sensibility of chemotherapy in GC patients
(Fig.7J-M).
Knockdown of TRIM31 inhibited the growth of GC cells
We know that TRIM31 is highly expressed in GC tissue
based on prior immunohistochemistry data. We further
look into the biological function of TRIM31 in GC via in
vitro research. First, we compared the levels of TRIM31
protein expression in GES-1, AGS, MKN7, SGC7901,
and NCI-N87 cell lines. e findings revealed that the
protein expression level of TRIM31 in AGS cells was sig-
nificantly higher than in the GES-1 cells (Fig.8A). As a
result, we chose AGS cells as the experimental subjects in
the following investigations.
We are going to inject the lentivirus into AGS cells
in order to create a stably transfected cell line with
decreased TRIM31 protein. Western blot was utilized
to test the efficacy of lentivirus infection, and it was
discovered that the decrease in TRIM31 expression in
TRIM31-sh3 AGS cells was the most significant (Fig.8B).
Following that, we assessed the proliferative potential
of TRIM31-sh3 AGS cells and discovered that TRIM31
knockdown resulted in a decrease in cell proliferative
ability (Fig.8C). e similar result was observed when we
utilized EDU staining to measure cell proliferation ability
(Fig.8E). It is worth mentioning that the reduction in cell
viability caused by TRIM31 knockdown is not consider-
able (Fig.8D). Additionally, we assessed the TRIM31-sh3
AGS cells’ capacity for migration and discovered that this
capacity declined after TRIM31 knockdown (Fig. 8F).
e rise in ROS levels in cells following TRIM31 knock-
down, a sign of increased oxidative stress, is exciting
(Fig.8G). e data from JC-1 staining further revealed
that TRIM31 knockdown impacted the mitochondrial
activity of cells (Fig.8H). Furthermore, western blot anal-
ysis revealed a decrease in CyclinD1 and CDK2 protein
expression in TRIM31 knockdown cells (Fig.8I).
Discussion
e TRIM family proteins were characterized as the
N-terminal RING domain, B-box domain, and a CC
domain, of which the dimerization of RING domain was
crucial to the activation of E3 ubiquitin ligases [13]. Com-
prising a large protein family, the members are divided in
to C-I to C-XI based on the various C-terminus of TRIM
protein [5, 14]. us lead to the diverse and even dia-
metrically opposite cell function in cell proliferation and
mitosis, immune response, tumorigenesis or harmoni-
zation of certain ubiquitination-related signal pathways
[15–17]. Studies have verified that abnormal expression
of TRIM family members were existed in multiple can-
cers [6, 17–19], and effected the tumor cells by promot-
ing epithelial mesenchymal transition (EMT) and aerobic
glycolysis [20]. e TRIM family functioned in tumori-
genesis based on their other non-classical domains.
TRIM14 and TRIM21 regulated the nuclear factor-κB
(NF-κB) and phosphoinositide 3-kinase (PI3K)/protein
(See gure on previous page.)
Fig. 3 The consensus clustering analysis and functional analysis. (A) When the value of k (representing the number of clusters) is set to 2, the consensus
matrix is generated to describe the outcomes of the clustering. (B) Heatmap of the expression of TRIMs in TRIM cluster A and B with clinical characteristics
(N stage, T stage, gender and age). (C) PCA of TRIM cluster A and B of GC patients. Visualization by the PCA analysis, mapping from high dimensions to
two dimensions. (D) K-M analysis between TRIM cluster A and B. (E) The dierent enriched pathways between TRIM cluster A and B by the GSVA analysis.
Enriched pathways were shown on the right. (F) Immune inltration score of immune cells in TRIM cluster A and B by the ssGSEA analysis. The horizontal
coordinates represented the dierent types of immune cells and the vertical coordinates represented the corresponding immune inltration scores. GO
(G) and KEGG (H) enrichment analyses conducted in the DEGs. The size of the circle represented the degree of enrichment. BP: Biological Process. MF: Mo-
lecular Function. CC: Cellular Component. (I) Heatmap of the expression of TRIMs in gene cluster A and B after the second consensus clustering analysis
with clinical characteristics (N stage, T stage, gender and age). (J) K-M analysis between gene cluster A and B. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
kinase B (AKT) pathways by the PRYSPRY domain [21,
22]. TRIM44 was verified to be participated in the resis-
tance of cisplatin in lung adenocarcinoma via deubiquiti-
nating the K48-linked polyubiquitin chain with the ZnF
UBP domain [23].
e unsupervised consensus clustering analysis pro-
vided with a more effective, accurate and visible method
to classify disease phenotypes [24]. After doing the con-
sensus clustering analysis, we identified differentially
expressed genes (DEGs) from the two gene clusters,
namely TRIM clusters A and B. e process involved the
examination of all mRNA-encoding genes acquired from
the TCGA and GEO gastric cancer databases. e pur-
pose was to investigate the genes that are linked to dif-
ferent gene clusters. Additionally, we aimed to enhance
the model using LASSO regression, specifically the risk
score system, which was seen as an enhanced abstraction
of the various expression profile of the entire TRIM fam-
ily. e utilization of consensus clustering and LASSO
analysis has successfully discovered hub genes implicated
Fig. 4 The nomogram based on the risk score and scRNA analysis of hub genes. (A) The survival rate at 1, 3, and 5-year of a sample from TCGA-STAD were
0.729, 0.387 and 0.255, respectively, according to the nomogram based on gender, age, risk score, T stage and N stage. (B) The ROC of the model and
AUC at 1, 3, and 5-year. (C) Calibration curve of the model at 1, 3, and 5-year. (D) The t-SNE algorithm was used for dimensionality reduction and 5 cell
subgroups were identied. (E) The expression of hub genes in 5 cell subgroups. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
Fig. 5 Survival analysis and immune inltration analysis between high-risk and low-risk groups. (A) The risk score, survival time and expression level of
hub genes in high-risk and low-risk groups in all samples. (B) K-M analysis between high-risk and low-risk groups. (C) The risk score of TRIM cluster A was
higher than TRIM cluster B. (D) The Sankey diagram displayed the distribution of the samples after twice consensus clustering analysis and risk score con-
struction, as well as survival outcomes. (E) Stromal score, immune score and ESTIMATE score in high-risk and low-risk groups. (F) The correlation analysis in
CD4 + memory T cells, mast cells and Tfh with risk score. R referred as the correlation coecient. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
in carcinogenesis or cancer inhibition [25], which indi-
rectly supported the significance of the analysis for the
entire TRIM family. RGS proteins induced the regulation
of vimentin and E-cadherin, and involved in the process
of tumor metastasis [26, 27]. Especially, the scRNA-seq
study revealed a considerable upregulation of RGS4 in
the fibroblast population, hence playing a crucial role
in tumour invasion and the modulation of the extra-
tumoral stroma.
e remarkable function of TRIM family in tumor
immune escape by multiple signaling pathways (includ-
ing the PI3K/AKT signaling pathway, NF-κB signaling
pathway and Wnt signaling pathway) was also be verified
in various studies [12, 28–30]. Furthermore, the toler-
ance and suppression of anti-tumor immune response
via abnormal expression of immune checkpoint such
as PD-1/PD-L1 in tumor immune microenvironment
(TME) largely contributes the tumor immune escape
[31].TRIM16 influenced the expression of PD-L1 by reg-
ulating the JAK/STAT signaling pathway in non-small
cell lung cancer, and TRIM28 was related to the resis-
tance of cytotoxic T lymphocyte-associated antigen-4
(CTLA-4) blockade by the ubiquitination of AMP-acti-
vated protein kinase in melanoma [32, 33]. Our study
found that the TRIM cluster A that with a worse survival
outcome was related with higher infiltration of MDSCs
and Tregs. Risk score was positive related with the infil-
tration of M2 and Tregs, while negatively related with the
activated CD4 + memory cells. In TME, regulatory T cells
(Tregs) induces non-antigen specific suppressive immune
response and myeloid-derived suppressor cells (MDSCs)
are impaired in maturation and negatively regulate the
immune response [34].Furthermore, M2 macrophages
have been found to be linked with the restriction of
immunological response, the promotion of angiogenesis,
and the facilitation of pro-tumorigenic activity [35]. e
Fig. 6 Exploration of MSI status and drug sensitivity between high-risk and low-risk groups. (A) The relationship between MSI status and risk score. The
risk score was with signicant statistical dierence between MSS and MSI-H, MSI-L and MSI-H. (B) The percent weight of dierent MSI status in high-risk
and low-risk group. (C) The IC50 of 9 kinds of drugs in high-risk and low-risk group. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
Fig. 7 Single-gene bioinformatics analyses of TRIM31. (A) The expression level of TRIM31 between STAD and normal tissue. (B) The ROC curve with AUC
of the accuracy of TRIM31 for predicting outcomes in STAD. (C) K-M analysis between TRIM31-high and TRIM31-low groups. (D) The 1-, 3-, and 5-year
nomogram was constructed based on independent risk factors of OS after Cox regression. (E) Calibration curve of the model at 1, 3, and 5-year. (F) K-M
analysis between high TRIM31 IHC score and low score groups. (G-I) GSEA analysis on DEGs between TRIM31-high and TRIM31-low groups. (J-M) The
IC50 of paclitaxel, cisplatin, docetaxel and 5-uorouracil in TRIM31-high (G1) and TRIM31-low (G2) groups. ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
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Ding et al. Human Genomics (2024) 18:77
expression of TRIM family in macrophages was verified
to influence the interaction between macrophages and
tumor cells, thus decrease the monitory effect on cancer
cells [36, 37].
In our study, TRIM31 exhibited significant upregula-
tion in gastric cancer tissues, and this elevated expression
was correlated with a poorer prognosis. Furthermore,
TRIM31 exhibited differential expression in gene clus-
ters A and B. As a member of E3 ubiquitin ligase fam-
ily, TRIM31 was characterized with RING finger domain
and was found to be engaged in inflammations, cell cycle
and oncogenesis [38]. e suppression of miR-876-3p/
TRIM31 axis was related to the impeded AML cell
growth, indicated the role of TRIM31 in tumor cell cycle
[39]. e role of TRIM31 in regulation of tumorigenesis
was related to various cell signaling pathways in different
cancers. High-expressed TRIM31 was found in gallblad-
der cancer, pancreatic cancer, and colorectal cancer [40,
41] while low-expressed in breast cancer [42]. e over-
expression of TRIM31 was demonstrated as a tumor sup-
pressor that inhibited the proliferation of lung cancer and
ovarian cancer cells [43, 44]. However, TRIM31 exhibits
contradictory functions in the progression of cancer. In
gallbladder cancer and glioma, TRIM31 played a car-
cinogenic role by activating PI3K-Akt pathway and the
resistance of chemical agents [45, 46]. In hepatocellular
carcinoma, TRIM31 was related to the over activation
of AMPK pathway by suppressing P53, which was the
tumor suppressor protein [47].
Cell stress (including oxidative stress and inflammation
responses) play a crucial role in oncogenesis by regulat-
ing Wnt, NF-κB and TGF-β related signaling pathways
[48]. Multiple cytokines induced the activation of NF-κB
pathway, thus contributed to the enhance of inflamma-
tion response, EMT and tumorigenesis. Ge et al. found
that the function of Mulberrin against hepatic fibro-
sis and oxidative stress was depended on the expres-
sion of TRIM31 and the suppression of NF-κB pathway
and NOD-like receptor protein 3 (NLRP3) inflamma-
some [49]. In pancreatic cancer and colorectal cancer,
TRIM31 played a role in the activation of the NF-κB and
the downstream genes [41, 50]. Moreover, downregu-
lated TRIM31 elevated the level of reactive oxygen spe-
cies (ROS) and strengthened the apoptosis in colorectal
cancer cells [51]. Pathway enrichment analyses verified
high expression of TRIM31 was involved in GC net-
works, related to cell cycle and DNA replication. e
activation of cellular response to hypoxia and chemi-
cal stress may be reliant on high expression of TRIM31,
therefore suggesting its indispensability. While, suppres-
sion of TRIM31 expression may trigger oxidative stress
in tumor cells, leading to apoptosis. e results of out
in vitro experiments have validated the significant con-
clusions derived from the pathway enrichment analysis.
Table 1 Dierential expression of TRIM31 in cancer and
paracancerous tissues
Group n TRIM31
expression
χ2 p value
High Low
Cancer tissues 97 32 65 12.47 0.000
Paracancerous tissues 83 9 74
Table 2 Univariate and multivariate analyses of the factors
correlated with Overall survival of STAD patients
Characteristics Univariate analysis Multivariate
analysis
Hazard ratio
(95% CI)
P value Hazard ratio
(95% CI)
P
value
IHC score of
TRIM31
low Reference Reference
high 1.642
(1.003–2.689)
0.049 0.947
(0.555–1.614)
0.840
Sex
Male Reference
Female 1.070
(0.617–1.857)
0.809
Age 1.030
(1.004–1.057)
0.022 1.023
(0.996–1.051)
0.102
T stage
T1 Reference Reference
T2 1.441
(0.373–5.574)
0.596 1.242
(0.318–4.843)
0.755
T3 3.515
(1.076–11.486)
0.037 2.371
(0.702–8.003)
0.164
T4 9.291
(2.754–31.351)
< 0.001 5.546
(1.537–20.009)
0.009
N stage
N0 Reference Reference
N1 0.680
(0.266–1.737)
0.420 0.834
(0.323–2.152)
0.708
N2 1.547
(0.788–3.040)
0.205 1.361
(0.674–2.749)
0.390
N3 2.793
(1.492–5.228)
0.001 2.097
(1.061–4.145)
0.033
Pathological
grade
Well
dierentiated
Reference
Moderately
dierentiated
1.468
(0.440–4.894)
0.532
Poorly
dierentiated
2.237
(0.691–7.247)
0.179
Hp
positive Reference
negative 0.989
(0.571–1.714)
0.970
unknown 1.088
(0.583–2.028)
0.791
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 18
Ding et al. Human Genomics (2024) 18:77
e knockdown of TRIM31 reduced the proliferation of
GC tumor cells and negatively regulated the cell cycle
displayed by the decrease of CylclinD1 and CDK2. e
decrease of mitochondrial membrane potential was
indicated the early stage of cell apoptosis. is tendency
was found in TRIM31-knockdown GC cells by using the
detection of JC-1. Moreover, the elevated level of ROS
was found in TRIM31-knockdown GC cells, suggested
oxidative stress may be occurring with the decreasing
expression level of TRIM31.
It was found that the sensitivity of cancer therapy was
influenced by the expression level of TRIM31. Upregu-
lated TRIM31 suppressed the sensitivity of daunoru-
bicin and promoted the proliferation of AML cell lines
by regulating the Wnt/β-catenin pathway [52], and
the knockdown of TRIM31 caused DNA damage and
radiosensitized colorectal cancer cell lines [51]. In our
study, the IC50 of cisplatin and docetaxel were higher
in TRIM31-high group, indicated high expression of
TRIM31 may contributed to the resistance of cisplatin
Fig. 8 Functional validation of TRIM31 in vitro experiments. (A) Western blot revealed TRIM31 expression levels in GES-1, AGS, MKN7, SGC7901, NCI-N87
cell lines (n = 3). (B) TRIM31 protein expression was evaluated by western blot in AGS cells silenced by TRIM31-sh1, TRIM31-sh2, and TRIM31-sh3 (n = 3).
(C) AGS cell lines cloning after lentivirus treatment (shControl, TRIM31-sh3). The absorbance of the solution generated by dissolving the cell population
in glacial acetic acid represents the number of cell clones (n = 4). (D) Cell viability of AGS cell lines treated with lentivirus (shControl, TRIM31-sh3) was
determined (n = 3). (E) EDU staining was used to detect the potential of AGS cells to proliferate. Scale bar: 100μm. (F) Determination of migration ability
of AGS cell lines treated with lentivirus (shControl, TRIM31-sh3) (n = 3). Scale bar: 100μm. (G) Evaluate the oxidative stress level of AGS cell lines treated
with lentivirus (shControl, TRIM31-sh3) by DCFH-DA staining (n = 3). Scale bar: 100μm. (H) Evaluate the mitochondrial function of AGS cell lines treated
with lentivirus (shControl, TRIM31-sh3) by JC-1 staining. Scale bar: 100μm. (I) Western blotting was used to assess CyclinD1 and CDK2 levels in AGS cell
lines treated with lentivirus (shControl, TRIM31-sh3) (n = 3). ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 18
Ding et al. Human Genomics (2024) 18:77
and docetaxel in GC tumor cells. While the outcomes
were opposite in 5-Fu and paclitaxel.
However, limitations were still existed in our study.
Firstly, TRIM31 IHC score was submitted to the multi-
variate Cox regression and a negative result was obtained.
is may due to the small samples and a short follow-up
visit. Second, specific signaling pathway and molecular
mechanism of TRIM31 in cell cycle and stress need to be
explored. Moreover, animal experiments were needed to
investigate the dynamic role of TRIM31 during the dif-
ferent process of GC tumorigenesis and development in
vivo.
In conclusion, our study investigated the whole func-
tional and expression profile and a risk score system
based on the TRIM family in GC. Particularly, TRIM31,
a member of TRIM family with the predictive value on
survival outcome, was chosen for further analysis. e
close connection of TRIM31 and tumorigenesis was dis-
played through the role of TRIM31 in cell cycle and oxi-
dative stress. Further investigation is required to conduct
comprehensive pathway analyses in order to elucidate
the intricate molecular interaction networks linked to
TRIM31.
Methods and materials
Data download and processing
RNA-seq transcriptome profiling data and clinicopatho-
logical information of GC come from TCGA-STAD (e
Cancer Genome Atlas database) and GSE84437 (Gene
Expression Omnibus database). e RNA-seq in Frag-
ments Per Kilobase per Million (FPKM) was transformed
into Transcripts Per Million (TPM) format and ensemble
id was transformed into gene symbol for further analysis.
75 TRIM family members were screened out from GEN-
ECARDS database. RNA-Seq profiling data from TCGA-
STAD and GSE84437 were merged for calibration and
batch correction. e common TRIM family members
(TRIMs) were intersected.
Expression, survival analyses, and gene alteration of TRIMs
e median expression level of TRIMs was calculated for
univariate Cox regression analysis between the high and
low expression groups. e prognostic network diagram
was drawn according to the results of univariate Cox
regression analysis. HR > 1 was defined as risk factors,
and HR < 1 was favorable factors.
Differential expression analysis of TRIMs was per-
formed using “limma” package in TCGA TPM data.
e threshold for defining differential expression is set
as |log2 (Fold change) |>0.5 and p-adjust value < 0.05.
TRIMs of which was expressed differentially which were
defined as differentially expressed members (DEMs). e
median expression level of DEMs was calculated for K-M
survival analysis between the high and low expression
groups. Survival curves were drawn for DEMs with p
value < 0.05 in K-M survival analysis.
Simple nucleotide variation (SNV) data was down-
loaded from TCGA database for gene alteration fre-
quency of TRIMs using R “maftools” package. e
gene-level copy number data of TCGA-STAD was down-
loaded from the XENA database for analyzing the copy
number alteration frequency.
Construction of transcription factor (TF)-gene-miRNA and
protein-to-protein (PPI) network on TRIMs
e PPI network was based on the construction of the
interaction relationship of proteins coded by TRIMs
using STRING database with medium confidence (0.400).
Cytoscape software (v3.8.2) was used for visualization
ranked by the “Degree”. e ENCODE database was cho-
sen for the construction of TF-gene interaction network,
with the peak intensity signal < 500 and the predicted reg-
ulatory potential score < 1. Additionally, the gene-miRNA
interaction network was validated from the miRTarBase
database.
Consensus clustering analysis and risk score system of
TRIMs
Consensus clustering analysis was performed based on
the expression of TRIMs using R “ConsensusClusterPlus”
package. Expression data of TRIMs were obtained from
the merged-matrix from TCGA-STAD and GSE84437.
e K value with the best stability and clustering effect
was selected as the grouping criterion. e Kaplan-
Meier (K-M) analysis, single-sample gene set enrichment
analysis (ssGSEA) and GSVA analysis were performed
between different TRIM clusters. Different expression
genes (DEGs) between different TRIM clusters were
screened out with the threshold of |Log2 [Fold Change
(FC)]| >1 and p value < 0.05. Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG)
were performed among DEGs.
In order to further investigate the characteristics of
TRIMs and to accurately predict prognosis in GC, we
aimed to established the risk score system. First, uni-
variate Cox regression analysis on DEGs was performed.
Subsequently, DEGs that exhibited significant differ-
ences in prognosis were selected for the second con-
sensus clustering analysis to obtained the gene clusters.
e K-M analysis were performed between different
gene clusters. STAD patients obtained from the merged-
matrix were randomly divided in to a train set and a test
set. e least absolute shrinkage and selection operator
(LASSO) regression was used to perform the variable
selection and regularization and the multivariate Cox
regression analysis was used for model construction. e
risk score was calculated by the following formula: Risk
Score = ∑(normalized gene expression * Cox coefficients).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 18
Ding et al. Human Genomics (2024) 18:77
Clinicopathological variates and the risk score were
included into Cox regression analysis and variates with
the p value < 0.05 were used to construct the nomogram.
e receiver operating characteristic (ROC) curve and
calibration analysis were used to evaluated the predicted
ability and accuracy of the model.
Deep research of TRIM31 with function and prognostic
signicance
Signaling pathway and cell function enrichment analysis
were performed for better understand of the potential
effect of TRIM31 on STAD based on the co-expressed
genes of TRIM31 and DEGs between TRIM31-high and
TRIM31-low groups. Furthermore, the expression level
of TRIM31 was regarded as a variate for multivariate Cox
regression analysis.
Immune infiltration analysis.
e CIBERSORT analysis was used to explore the
correlation between the model constituent genes and
immune cells. e indicator of the tumor immune
microenvironment (TME), including the stromal score,
immune score, and tumor purity were generated with the
ESTIMATE algorithm.
Microsatellite instability and drug sensitive analysis
Microsatellite instability (MSI) status (including micro-
satellite instability stable (MSS), microsatellite instability
-low (MSI-L), and microsatellite instability – high (MSI-
H)) of STAD samples were obtained from the TCGA
and were performed the related analysis with the risk
score. e response to chemotherapy was predicted for
each sample based on the Genomics of Drug Sensitivity
in Cancer (GDSC) database. e prediction process was
implemented by the ‘pRRophetic’ package, in which the
half-maximal inhibitory concentration (IC50) of the sam-
ples was estimated by ridge regression, and all parame-
ters were set at default values.
scRNA-seq transcriptome analysis
scRNA-seq data GSM5101015 of GC was downloaded
from GSE167297 series. e expression matrix was con-
verted into “Seurat” object and was filtered to reserve
cells with the gene content between 200 and 2500. e
“PercentageFeatureSet” function was used to calculate
the percentage of mitochondrial genes and visualize
gene characteristics and sequencing depth. e data was
standardized and the top 200 genes with high variation
coefficient were extracted. PCA principal component
analysis was performed to reduce the dimension of the
data, and PC with p-value < 0.05 was selected for further
analysis. T-distributed stochastic neighbor embedding
(t-SNE) algorithm was used to unsupervised clustering
and visualization of clusters. CellMarker 2.0 database was
used for cell subgroups’ annotation with the retrieval of
marker genes of different PCs. Cell trajectory analysis
and cell trajectory difference were performed using the
“monocle” R package. Finally, the cell trajectory analysis
and cell trajectory difference analysis were performed
using the ‘monocle’ package.
Cell culture and reagents
GES-1, AGS, MKN7, SGC7901, NCI-N87 were pur-
chased from the Cell Bank of Type Culture Collection of
the Chinese Academy of Sciences (Shanghai, China) and
were all cultured in RPMI 1640 medium (10% fetal bovine
serum (FBS), 1% penicillin/ streptomycin) and stably cul-
tured in an incubator (37℃, 5% carbon dioxide).
e following substances or antibodies were used:
3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bro-
mide (MTT; Aladdin: M158055), Crystal violet (Alad-
din: C110703), 2′,7′-dichlorodihydrofluorescein diacetate
(H2DCFDA; Sigma-Aldrich: D6883), Mitochondrial
Membrane Potential Assay Kit (Elabscience Biotechnol-
ogy Co.,Ltd: E-CK-A301), BeyoClick ™ EdU-488 Cell Pro-
liferation Detection Kit (Beyotime: C0071S), HRP Goat
Anti Rabbit IgG (H + L) (Abclonal: AS014), anti-cyclin D1
(Abclonal: A19038), anti-CDK2 (Abclonal: A0094) and
anti-GAPDH (Cell Signaling Technology: #5174).
Establishment of the stable transfected cell line
We bought lentivirus (vector: GV112) (TRIM31-
RNAi121410-1 target sequence: GCTCTCAG-
GATACGAAGSCAT; TRIM31-RNAi121411-1 target
sequence: G C C A C A G T T G A A C G A T C T C A A; TRIM31-
RNAi121412-1 target sequence: C G T G A A T C C A A G G A
C C A C A A A) from Jikai Gene (Shanghai, China) with the
goal to create a stable cell line that can lower TRIM31
expression.
Before infecting with lentivirus, place AGS cells
(1.2*105/well) in a 6-well plate and incubate in a culture
incubator for 24 h. Calculate the required volume of
lentivirus per well cell using a MOI (Multiple Infection
Index) of 10 and add lentivirus infection enhancer to the
culture medium to increase lentivirus infection effec-
tiveness. After 48h of infection, cells were digested with
trypsin to facilitate cell subculture. Puromycin was added
to the cell culture media at a concentration of 1µg/mL
to eliminate any uninfected AGS cells. Puromycin must
constantly treat cells for one week before collecting cells
to detect the expression level of TRM31 protein within
the cells and select the appropriate knockdown.
Cell viability determination
Inoculate 1.3*104/well AGS cells per well in 24-well
plates. Stop the cultivation on 1, 3 and 5 days, respec-
tively. Add 500 µL/well MTT (0.5mg/mL) and incubate
for 3h at 37°C in the 24-well plates. Add 500 µL DMSO
to each well after removing the culture medium to
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 16 of 18
Ding et al. Human Genomics (2024) 18:77
completely dissolve the formaldehyde. Draw a cell growth
curve by measuring the solution’s absorbance at 490nm
with a full-function microplate detector (BioTek, USA).
EdU (5-ethynyl-2’ -deoxyuridine) assay
Inoculate AGS cells (3.3*104/well) for 24–36h of steady
cultivation in the 24-well plates and incubate the EdU
working solution for 3 h at 37 ℃. 4% paraformalde-
hyde was added and fixed for 15min before incubating
the transparent solution for 15min. After rinsing with
PBS, add the Click reaction solution and incubate in
the dark for 30min before staining with Hoechst 33,342
for 15 min. Using an inverted fluorescent microscope
(Nikon, Japan) for imaging and photography.
Migration and colony formation assay
Inoculate AGS cells (3.3*104/well) into 24-well plate and
incubate them stably in an incubator. When the cell con-
vergence hits 90%, use the gun head to make a wound on
the cell’s surface. PBS was used to clean the cells in each
well and to remove any suspended cells. After that, culti-
vate the cells for 48h in fresh culture media to examine
cell migration.
Inoculate AGS cells (1000/well) into 6-well plate and
incubate them for 12 days in a stable incubator. Change
the culture media every 3 days. Fix overnight with 4%
paraformaldehyde. In each well, incubate 1mL 0.1% crys-
tal violet solution for 2h before rinsing and drying. Pho-
tograph cell clones for examination. Dissolve cell colonies
with 30% glacial acetic acid and measure the absorbance
of the solution on a full-function microplate detector
(BioTek, USA) and perform analysis.
ROS assay and measurement of mitochondrial membrane
potential
AGS cells (3.3*104/well) were seeded in a 24-well plate
for stable cultivation for 36h. We prepared 10 µmol/L
H2DCFDA using RPMI 1640 culture medium without
FBS. Subsequently, the culture medium was removed
and freshly prepared DCFH-DA was added to a 24-well
plate and incubated with the cells for 30min. Fluores-
cence inverted microscope captured green fluorescence
intensity.
JC-1 is often used to detect changes in mitochondrial
membrane potential within cells. JC-1 emits strong red
fluorescence in normal mitochondria, while green fluo-
rescence is enhanced in damaged mitochondria. JC-1 dye
working solution was added to the cell culture plate and
then rinsed with buffer. Subsequently, the green fluores-
cence and red fluorescence intensities were measured
using a fluorescence inverted microscope.
Western blot
Cells should be lysed using Western/IP lysis buffer (Beyo-
time: P0013) at 4°C for 60min. Centrifuge afterwards for
25min at 12,000rpm and 4 ° C. Gather the supernatant,
measure the protein content, and then boil the sample
at 100°C for 10min to denaturize the protein. Proteins
are separated using the Omni Easy TM Gel before being
transferred to polyvinylidene difuoride (PVDF) mem-
branes. e PVDF membrane was first incubated for
2 h at room temperature with a sealing solution, then
overnight at 4 °C with anti-TRIM31, anti-CDK2, anti-
cyclinD1, and anti-GAPDH, and finally for 2h at room
temperature with HRP Goat Anti Rabbit IgG (H + L). Pro-
tein expression is discovered using an Electrochemical
Luminescence (ECL) detection kit.
Tissue microarray chips and immunohistochemistry (IHC)
staining
Tissue microarray chips consisted 97 STAD patients’
samples and 83 paracancerous stomach tissue samples
were purchased from Shanghai Qutdo Biotech Company
(Shanghai, China). Tissue microarray chips was incu-
bated with monoclonal rabbit anti-human TRIM31 (dilu-
tion 1:1500, Proteintech, Wuhan, China) after formalin
fixation. IHC score = staining intensity score × percentage
of positive staining. K-M analysis was performed between
TRIM31-high and TRIM31-low groups. Age, sex, tumor
size, pathological grade, T stage, N stage, M stage and
TRIM31 expression were included in the univariate and
multivariate Cox regression analysis. e study protocol
was approved by the Shanghai Qutdo Biotech Company
Ethics Committee (NO. YB M-05-02).
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40246-024-00631-7.
Supplementary Material 1
Supplementary Material 2
Supplementary Material 3
Supplementary Material 4
Supplementary Material 5
Supplementary Material 6
Supplementary Material 7
Supplementary Material 8
Supplementary Material 9
Author contributions
We would like to express gratitude to all authors for their contributions:
YD provided the idea and data analysis of the article; YL implemented the
cellular part of the experiments. JG completed the data analysis of the tissue
microarrays.SC, XH, and SW provided partial technical support.MZ, RW, JS and
KW were responsible for the logic and accuracy of the manuscript. WSQ and
WWQ supervised the whole subject and provided funding.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 17 of 18
Ding et al. Human Genomics (2024) 18:77
Funding
Out study was supported by the following funds: Science and Technology
Development Plan Project of Shandong Province (Grant No. 202003030451);
The Youth Scientic Research Fund of the Aliated Hospital of Qingdao
University (Grant No. QDFYQN202101007) and Beijing Science and
Technology Innovation Medical Development Foundation (Grant No.
KC2021-JX-0186-145); Qingdao Key Clinical Specialty Elite Discipline.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Institutional Review Board Statement
This study was performed in line with the principles of the Declaration of
Helsinki. Approval was granted by the Ethics Committee of Shanghai Outdo
Biotech Company (No. YB M-05-02).
Competing interests
The authors declare no competing interests.
Received: 27 November 2023 / Accepted: 28 May 2024
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