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BCL9 is a risk factor of neck lymph nodes
metastasis and correlated with immune cell
inltration in papillary thyroid carcinoma
Rui Zhang
Wuhan No.1 Hospital
Zhengwei Gui
Tongji Hospital aliated with Tongji Medical College of Huazhong University of Science and
Technology
Jianguo Zhao
Wuhan No.1 Hospital
Di Zhu
Fudan University
Lu Zhao ( luzhao268@outlook.com )
Tongji Hospital aliated with Tongji Medical College of Huazhong University of Science and
Technology
Research Article
Keywords: BCL9, papillary thyroid cancer, lymph node metastasis, tumor immune microenvironment,
tumor-inltrating immune cells
Posted Date: July 18th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-3160301/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
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Abstract
Purpose
BCL9 contributed to tumor progression and metastasis in various tumors, whereas, the role of BCL9 in
papillary thyroid cancer (PTC) hasn’t been investigated.
Methods
We acquired PTC gene expression data from The Cancer Genome Atlas (TCGA) databases. 59 PTC
tissues were applied to validate the clinical signicance of BCL9. Cell experiments were applied to
investigate the role of BCL9. Bioinformatics analysis were employed to investigate the biological
functions of BCL9.
Results
We found that BCL9 was higher expressed (
P
0.05) and an independent risk factor for lymph node
metastasis (
OR
= 3.770,
P
= 0.025), as well as associated with poorer progression free survival (PFS) (
P
=
0.049) in PTC. BCL9 knockdown inhibited proliferation and invasion of PTC cells. BCL9 was positively
associated with the key genes of Wnt/β-catenin and MAPK pathway by Co-expression analysis. GO, KEGG
and GSEA analysis showed BCL9 might participated in PPAR, cAMP, and focal adhesion pathway.
CIBERSORT analysis found BCL9 was negatively associated with CD8 + T cells and NK cells inltration
and positively with PD-L1 expression.
Conclusion
Therefore, BCL9 was associated with lymph node metastasis and shorter PFS of PTC, due to promotion
of PTC cell proliferation and invasion, activation of Wnt/β-catenin and MAPK pathway, inhibition of CD8
+ T and NK cells inltration, and promotion of PD-L1 expression.
1. Introduction
Thyroid cancer (TC) is the endocrine malignant tumor with the highest incidence rate. 85% of all thyroid
cancer subtypes is papillary thyroid cancer (PTC), with a growing incidence rate every year, especially in
developed countries[1]. Due to the widely applied ultrasonography in the early-diagnosis of TC, most
patients with PTC have a relatively satisfactory clinical prognosis followed the standard surgery therapy
and thyroid stimulating hormone (TSH) inhibition or radioactive iodine[2]. However, nearly 50% PTC
patients there is still a presence of an approximately 30% recurrence rate and 8.6% mortality for a three-
decade period[3], and either thyroid ultrasonography or FNAC sometimes provides indeterminate reports
which cause diculties in interpretation and clinical management[4]. So, it is extraordinary signicance
to uncover promising prognostic markers for PTC patients to early screening, timely treatment, effective
monitoring and prolong survival time.
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Wnt/β-catenin pathway is involved in many key aspects of cancer development[5], including in thyroid
carcinoma[6, 7]. The progression and metastatic activity of PTC has been veried to be correlated with
the downregulation of E-cadherin and the cytoplasmic accumulation of β-catenin[8]. Furthermore, nuclear
β-catenin can induce expression of B-cell lymphoma 9 (BCL9) and B-cell lymphoma 9-like (BCL9L), which
inducing tumor proliferation and survival[9].
BCL9 protein can connect ygopus and β-catenin[10], and is normally absent expressed[11]. The
overexpression of BCL9 contributes to the tumor proliferation, recurrence, progression, and metastasis in
colorectal cancer, multiple myeloma, hepatocellular carcinoma and breast cancer[12–14]. Whereas, the
value of BCL9 and the molecular mechanism of action of BCL9 in PTC has not been studied. In this
study, we explored the expression, prognostic value, and mechanism of BCL9 in PTC.
2. Materials and methods
2.1. Dataset
The gene expression data of 512 tumor samples (including 499 PTC samples) and 59 normal samples
were downloaded from the The Cancer Genome Atlas (TCGA) database. The UCSC Xena project
(http://Xena.ucsc.edu/) (download in July 2022). A total of 499 PTC samples and 59 normal samples
were enrolled in our study.
2.2. HPA
We studied BCL9 protein expression in PTC using The Human Protein Atlas. The Human Protein Atlas
(HPA) (https://www.proteinatlas.org/) focus on a particular aspect of the genome-wide analysis of the
human proteins.
2.3. PTC tissues and adjacent tissues
The tissues containing 59 PTC tissues and paired normal thyroid tissues were collected. BCL9 mRNA
expression of 59 PTC and normal thyroid tissues were tested by qRT-PCR. The protein expression of BCL9
in 12 PTC and normal thyroid tissues were tested by immunohistochemistry. The study was agreed with
the ethics committee (No. 2020S181) and all patients agreed to use their tissues, which complied with the
Declaration of Helsinki.
2.4. qRT-PCR
Total RNA of 59 PTC and normal thyroid tissues was extracted by TRIzol reagent (Invitrogen). The mRNA
of BCL9 was then tested in triplicate by SYBR Green qPCR Mix (Toyobo, Shanghai, China). Primer
sequences of BCL9 were as follow: Forward: 5’ - ACACACCACACTCGATGACC - 3’, Reverse: 5’ -
AGCTTCTGCAGCTTTATTGGC - 3’. Primer sequences of internal control-GAPDH were as follow: Forward:
5’ - GAGAAGGCTGGGGCTCATTT - 3’, Reverse: 5’ - TAAGCAGTTGGTGGTGCAGG - 3’. Relative mRNA
expression was worked out using the 2−△△CT method.
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2.5. Immunohistochemistry (IHC)
BCL9 protein expression of 12 PTC and normal thyroid tissues were tested by immunohistochemistry.
BCL9 protein antibody (anti-BCL9) (Product PA5-93229) was obtained from ThermoFisher Scientic,
China. Anti-BCL9 protein antibody diluted 1:100 was used to immunohistochemistry. The IHC results
blinded to clinical information were independently assessed by two pathologists. The percent score of
positive cells was assessed as follow: 0 (0-5%), 1 (6–25%), 2 (26–50%), 3 (51–75%), 4 (75%). The
staining intensity was assessed as follows: 0 for no color, 1 for yellow, 2 for light brown, and 3 for dark
brown. The outcome of nal staining score was dened as the multiplication of percentage and intensity
scores: 0 (negative), I (1–4), II (5–8), and III (9–12). For a further statistical analysis, we dened a low
protein expression group for score of 0 or I, while scores of II or III were severed as a high protein
expression group.
2.6. Cell Culture and Treatment
PTC-1, K1and TPC1 humanpapillary thyroid cancer cell lines were bought from Shanghai Institute of Cell
Biology (Shanghai, China). All cell lines were grown in a 5% CO2 ThermoFisher incubator. Using STR to
identify and compare all purchased cell lines to reliable databases.
Lipo3000 transfection reagent (Invitrogen, USA) was mixed with siRNA to transfect PTC cells. The
sequences of the siBCL9 were as follows: siBCL9-1 sense: GCCAGGUUGAAACUAUCGU(dT) (dT), siBCL9-2
sense: GGACAUCCCUCUUGGUACA(dT)(dT).
2.7. CCK8 Assay
Cells from each experimental group in the logarithmic growth phase and in good growth conditions were
digested and resuspended in full culture media. Cell proliferation was measured using the Cell Counting
Kit-8 (Invitrogen, USA) according to the manufacturer's instructions at 24 h, 48 h, 72 h, and 96 h. The
optical density values at 450 nm were measured using an enzyme marker (Molecular Devices, Rockford,
IL, USA).
2.8. Colony-Formation Assay
3,000 papillary thyroid cancer cells were seeded into six-well plates and cultured for 14 days. Crystal
violet (Beyotime, China) was used to dye the cell colonies after they had been xed for 10 minutes in 4%
polyacetal. The cell colonies were photographed and counted.
2.9. Transwell Assay
2×104 papillary thyroid cancer cells are seeded in the upper chamber of a transwell chamber in a 24-well
plate (Corning, USA). After 24 hours of incubation at 37℃, the cells were wiped from the top surface of
the chamber. Cells on the bottom surface of the chambers were xed with 4% paraformaldehyde for 10
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minutes before being stained with crystal violet for 10 minutes (Beyotime, China). The number of
migrating cells was counted and photographed.
2.10. Scratch Assay:
Papillary thyroid cancer cells in the log phase of growth were placed in 24-well plates with IBIDI two-well
culture inserts and incubated for 24 hours. Forceps were used to carefully remove the culture implants
from the pristine table. Each well received 1 mL of low-serum media, and the rate of cell migration was
observed under a light microscope at 0 and 24 hours after the inserts were removed.
2.11. Co-expressed genes analysis and Functional Enrichment Analyses
The Pearson correlation coecient (r) was applied to nd BCL9 co-expressed genes and the screening
threshold was
P
0.001 and |r|0.6 by R software. We also estimated the correlation between BCL9 and
the key gene of Wnt/β-catenin and MAPK pathway by using the “correlation module” of The Tumor
Immune Estimation Resource (TIMER) database[15]. Differentially expressed genes (DEGs) between low
and high BCL9 groups by the median expression were screened by using “limma” and “DESeq2” package
in R software on the basis of the thresholds of |log2FoldChange|1 and adjusted
P
-Value (padj)0.05.
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were
performed to analyze the function of BCL9 through “clusterProler” package. “clusterProler” package
(4.4.4) was used for GSEA to elucidate the potential regulatory mechanisms through the analysis of the
functional differences based on the low and high BCL9 groups, which identied by adjusted
P
value0.05.
2.12. Correlation between BCL9 and tumor immune microenvironment
The relationship between tumor cells and tumor immune
microenvironment (TIME) is well recognized as
a key clinical feature in multiple malignant tumors. With the CIBERSORT L22 as the reference, we
analyzed the mRNA expression matrix using CIBERSORT R script acquired from the CIBERSORT website
(http://cibersort.standard.edu/). By using Monte Carlo sampling, we calculated an empirical P-value for
the deconvolution of each case. To obtain the scores of multiple immunocytes, gene expression matrix
was analyzed via “estimate” package and the correlation heatmaps between immune checkpoint genes
and BCL9 were visualized via “corrplot” package.
2.13. Statistical analysis
The difference of BCL9 in PTC tissues and normal thyroid tissues from TCGA databases were calculated
by the Wilcoxon rank-sum test. Survival analysis was performed by the log-rank test. The difference of
PTC tissues between the low and high BCL9 expression groups was calculated by the Spearman’s
analysis. The risk factors of neck lymph node metastasis were calculated by logistic regression analysis.
Data were processed through R (V.4.1.3), SPSS version 25.0, and GraphPad Prism (V.9.0) software.
P
value0.05 was considered statistically signicant, *
P
0.05, **
P
0.01, ***
P
0.001.
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3. Results
3.1. The expression and clinical signicance of BCL9 in PTC
To investigate the clinical signicance of BCL9 in PTC, we analyzed the relationship between its
expression levels and clinical value. First, we investigated the expression of BCL9 in PTC, the mRNA
expression level of BCL9 was overexpression signicantly in PTC (Fig. 1A and Fig. 1B). To investigate the
prognostic value of BCL9 in PTC, the PTC samples from the TCGA database were divided into low BCL9
(n = 250) and high BCL9 (n = 249), on the base of the median of BCL9 expression level. KM survival
analysis showed that the PFS of PTC group with high BCL9 was shorter (
P
= 0.049) compared with PTC
group with low BCL9 (Fig. 1C). To determine the clinical signicance of BCL9 in PTC, the differences of
clinical data between the low and high BCL9 groups were investigated by Spearman’s analysis and the
high BCL9 group correlated to lymph node metastasis (
P
0.001) (Fig. 1D).
3.2. Validation of BCL9 expression in the HPA database and in PTC tissues by qRT-PCR and IHC
To go deeply into BCL9 expression in PTC, we investigate the BCL9 expression based on the results from
the HPA database, BCL9 exhibited a higher expression in PTC tissues than in normal tissues (Fig. 2A). For
investigating into the tissues level, we tested BCL9 mRNA expression of 59 paired PTC and adjacent
tissues by qRT-PCR and BCL9 protein expression of 12 paired PTC and adjacent tissues by IHC, we found
that the mRNA (
P
= 0.001) and protein expression of BCL9 (
P
= 0.039) was signicantly overexpressed in
PTC than in paired normal thyroid tissues (Fig. 2B and Fig. 2C).
3.3. BCL9 correlated with neck lymph nodes metastasis in PTC
To investigate the relationship between BCL9 expression and PTC clinicopathological characteristics, we
divided 59 PTC patients into low and high BCL9 mRNA expression groups based on the median
expression. High BCL9 level was associated with a more advanced N stage (
P
= 0.024) (Table 1). Neck
lymph node metastasis might be associated with high BCL9 expression.
Table 1. The patients’ clinicopathological characteristics analyzed based on median of BCL9 mRNA.
Page 7/20
Characteristics Low (n=30) High (n=29) Chi-square
P
Value
Ageyears 0.004 0.948
55 24 23
≥55 6 6
Sex 0.203 0.706
female 27 25
male 3 4
T stage 0.036 0.850
≤1cm 10 9
1cm 20 20
N stage 5.107 0.024*
N0 17 8
N1 13 21
BRAFV600E 0.894 0.344
Negative 7 10
Positive 23 19
Abbreviations: *
P
0.05
3.4. BCL9 was a risk factor of neck lymph nodes metastasis in PTC
In order to investigate the risk factors of neck lymph nodes metastasis, we performed a logistic
regression analysis. According to the status of neck lymph nodes metastasis, the group with lymph
nodes metastasis had larger tumor size (
P
= 0.026) and higher BCL9 expression (
P
= 0.024) (Table 2).
What’s more, tumor size (
OR
= 3.940,
P
= 0.027) and BCL9 (
OR
= 3.770,
P
= 0.025) positively correlated
with neck lymph nodes metastasis in PTC patients (Table 3).
Table 2. Risk factors of lymph nodes metastasis in papillary thyroid carcinoma
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Characteristics Lymph node metastasis (n=34) Non-metastasis (n=25) Chi-square
P
Value
Ageyears 3.641 0.1
55 30 17
≥55 4 8
Sex 0.710 0.443
female 31 21
male 3 4
T stage 4.958 0.026*
≤1cm 7 12
1cm 27 13
BRAFV600E 1.092 0.386
Negative 8 9
Positive 26 16
BCL9 5.107 0.024*
Low 13 17
High 21 8
Abbreviations: *
P
0.05
Table 3. Logistic regression analysis for the risk factor of neck lymph node metastasis
Variables B SE Walds P OR 95% CI
T (1cm vs≤1cm) 1.371 0.622 4.864 0.027*3.940 1.165-13.325
BCL9 (High vs Low) 1.327 0.590 5.052 0.025*3.770 1.185-11.990
Abbreviations: OR, Odd ratio, CI, Condence interval, *
P
0.05
3.5 BCL9 is overexpressed in various PTC cell lines and promotes PTC cell proliferation and invasion
Cell experiments revealed that BCL9 expression was signicantly higher in TPC-1, K1, and KTC1 cells
than in thyroid cells (Fig. 3A). We chose TPC-1 and K1 cell lines with the most obvious BCL9
overexpression and knocked it out, then veried the knockdown eciency (Fig. 3B and 3C). CCK8 (Fig. 3D
and 3E) and colony assay (Fig. 4A and 4B) results showed that BCL9 knockdown signicantly inhibited
Page 9/20
TPC-1 and K1 cell proliferation. Furthermore, the transwell assay (Fig. 4C and 4D) and scratch assay (Fig.
4E-G) demonstrated that BCL9 knockdown signicantly inhibited the invasive ability of PTC cells.
3.5. The biological functions of BCL9 in PTC
We performed the co-expression analysis based on the data of the TCGA cohort to elucidate genes that
interact closely with BCL9. It has been shown that the top 5 genes negative correlated with BCL9 were
SNTA1, CHCHD10, PRADC1, VEGFB, and GCAT, and the top 5 genes positive correlated with BCL9 were
KDM5B, NRIP1, MAP3K1, NAV2, and PIAS3 (Fig. 5A).
Subsequently, 1066 DEGs including 471 up-regulated and 595 down-regulated were identied statistically
signicantly (|log fold change (logFC)| 1,
P
0.05). The top 50 up-regulated DEGs and top 50 down-
regulated DEGs were illustrated by the heatmap (Fig. 5B).
To go deeply into the function of 1066 DEGs between high and low expression of BCL9 in PTC, GO and
KEGG functional enrichment analysis were performed. The top 10 association with biological process
(BP) included detoxication of copper ion, stress response to copper ion, cellular response to zinc ion,
stress response to metal ion, detoxication of inorganic compound, cellular zinc ion homeostasis, zinc
ion homeostasis, cellular response to copper ion, response to copper ion, and response to zinc ion.
Cellular components (CC) included ion channel complex, transmembrane transporter complex, transporter
complex, postsynaptic membrane, synaptic membrane, cation channel complex, juxtaparanode region of
axon, potassium channel complex, voltage-gated potassium channel complex, and chloride channel
complex. Molecular function (MF) included sulfur compound binding, glycosaminoglycan binding,
heparin binding, passive transmembrane transporter activity, channel activity, receptor ligand activity,
signaling receptor activator activity, chloride transmembrane transporter activity, growth factor activity,
and ion channel activity (Fig. 5C). The top 10 KEGG pathways included Neuroactive ligand-receptor
interaction, Mineral absorption, Nicotine addiction, PPAR signaling pathway, Thyroid hormone synthesis,
Gastric acid secretion, cAMP signaling pathway, Pancreatic secretion, Cocaine addiction, and Proximal
tubule bicarbonate reclamation (Fig. 5D).
GSEA analysis was performed to acquire a greater depth of understanding of the pathways involved.
Signicant differences (FDR 0.05, adjusted
P
0.05) were observed in the enrichment of MSigDB
Collection (c2.cp.kegg.v7.4.symbols.gmt and c5.go.v7.4.symbols.gmt) of these pathways- focal
adhesion, tight junction, olfactory transduction, oxidative phosphorylation, and parkinsons disease (Fig.
5E). These results suggested that BCL9 participates in focal adhesion and tight junction of PTC.
3.7. BCL9 was related with Wnt/β-catenin and MAPK pathway
We investigate the relationship between BCL9 and Wnt/β-catenin and MAPK pathway in PTC based on
the “correlation module” of the TIMER database. As shown in Fig. 6, BCL9 was signicantly positively
associated with the key genes of Wnt/β-catenin pathway-Wnt2 (
r
= 0.208,
P
= 2.32E-06), 2B (
r
= 0.392,
P
=
4.25E-20), 3 (
r
= 0.532,
P
= 1.82E-38), 5A (
r
= 0.538,
P
= 1.3E-39), 7A (
r
= 0.158,
P
= 3.52E-04), 7B (
r
= 0.2,
P
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= 5.34E-06), 8A (
r
= 0.195,
P
= 8.91E-06), 8B (
r
= 0.226,
P
= 2.71E-07), 9A (
r
= 0.273,
P
= 3.56E-10), 9B (
r
=
0.316,
P
= 2.73E-13), 10A (
r
= 0.474,
P
= 1.7E-30), 16 (
r
= 0.169,
P
= 1.28E-04) and CTNNB1 (
r
= 0.681,
P
=
1.34E-70), and positively correlated with the key molecule of MAPK pathway (
r
= 0.547,
P
= 4.28E-41).
3.8. The correlation between BCL9 and tumor immune microenvironment
Tumor-inltrating immune cells (TICs) were widely existed in the TIME (tumor immune microenvironment)
of PTC and affect pathological processes, PTC samples in the TCGA database were subdivided into high
and low BCL9 sets on the base of BCL9 median expression level. StromalScore and ESTIMATEScore of
high-BCL9 group were generally higher when compared with those of low BCL9 groups evaluated by the
ESTIMATE algorithm (Fig. 7A). Next, we investigated the relationship between BCL9 expression and
various TIC subtypes, the differences in the proportion of 22 types of TICs in tumor tissues for the high-
and low-BCL9 expression groups were shown in violin plots. We discovered that CD4 memory resting T
cells, M0 macrophages, and mast cells resting were positively associated with BCL9 expression, and CD8
T cells, activated Dendritic cells, Neutrophils, and activated NK cells were negatively associated with
BCL9 expression, suggesting that BCL9 affecting PTC prognosis by inuencing the immune status of the
TIME (Fig. 7B). In addition, BCL9 was positively associated with mRNA of immune checkpoint genes,
such as CD274 (PD-L1 code gene) (r = 0.118, P = 0.008), CD276 (r = 0.504, P = 8.20E-34), CD44 (r =
0.486, P = 3.57E-31), TNFRSF8 (r = 0.410, P = 8.23E-22), TNFSF15 (r = 0.333, P = 1.68E-14), CD40 (r =
0.311, P = 9.10E-13) and negatively associated with the transcription levels of immune checkpoint genes,
such as IDO2 (r = -0.219, P = 7.21E-07), TNFRSF4 (r = -0.133, P = 0.003), IDO1 (r = -0.130, P = 0.003),
CD27 (r = -0.100, P = 0.025), TNFRSF14 (r = -0.99, P = 0.027) (Fig. 7C). But BCL9 expression was not
correlated with tumor mutation burden (TMB) in PTC (Fig. 7D). In conclusion, BCL9 was associated with
inhibition of cytotoxic immune cells (CD8+ T and NK cells) inltration, and promotion of anti-cancer
immune PD-L1 expression.
4. Discussion
PTC is a low-grade malignant tumor with relatively satisfactory prognosis, while accounts for the vast
majority of thyroid malignancies. Neck lymph node metastasis and recurrence are still unavoidable. The
frequency of neck lymph node metastasis (LNM) is 30%-90% in PTC[16]. In addition, LNM acts as an
important factor for recurrence[17]. The revised American Thyroid Association management guidelines
suggests that neck LNM is considered a risk factor of prognosis of PTC patients[18]. The criterion for
high risk in post-operative risk assessment is ≥ 5 lymph node metastases[19], and the ratio of the
metastatic lymph node count to the total lymph node yield (MLNR) has also been reported as a valuable
prognostic indicator in PTC[20, 21]. It remains a challenge to accurately identify the patients with higher
risk of recurrence and metastasis. In this study, we found that BCL9 was overexpressed (Fig. 1, Fig. 2 and
Fig.3A) and positively correlated with neck lymph nodes metastasis (
OR
= 3.770,
P
= 0.025) in PTC
patients (Table. 3).
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Some researcher supported PTC has constitutive activation of the MAPK pathway[22], in our study, we
veried BCL9 was related to MAPK1 (
r
= 0.547,
P
0.001) (Fig. 6) and MAP3K1 (Fig. 5A). While the
activation of the MAPK and PI3K/Akt pathways caused by BRAFV600E mutation, RAS mutation or RET
mutation is the major pathogenic mechanism in thyroid cancer[23], however, the canonical Wnt signaling
pathway plays an irreplaceable role as well[24]. Recent researchers found that BRAFV600E, RAS and RET
mutation can stimulate the Wnt pathway to promote tumorigenesis[25]. Signaling by Wnt proteins
activates β-catenin-independent or β-catenin-dependent pathway. When proteins of the family-
Wnt1/3/3a/7A/10B bound to a frizzled receptor and the LDL-Receptor-related protein coreceptor, Wnt/β-
catenin pathway could be activated. The study have found that BCL9/9L promoted cell proliferation and
metastasis though the activating β-catenin[26]. In order to investigate whether the mechanism existed in
PTC, we analyzed the correlations between BCL9 and Wnt/β-catenin by using the “correlation” module of
the TIMER database and veried BCL9 was positively related with Wnt2, 2B, 3, 5A, 7A, 7B, 8A, 8B, 9A, 9B,
10A, 16 and CTNNB1 (Fig. 6).
Immune cell inltration has been reported to relate to prognosis in patients with PTC[27], and the
antitumor effect of autoimmunity may be another important mechanism[28]. The Wnt/β-catenin pathway
has also been identied as an important oncogenic regulator of immune evasion[29, 30]. Up-expressed
immune checkpoint molecules in the tumor immune microenvironment plays a critical role in antitumor
immunity evasion and cancer progression[31]. In our study, we found that overexpression of BCL9 was
related to the proportion of StromalScore in the TIME in PTC (Fig. 7A) and T cells CD8 and NK cells
activated which acted as anti-tumor immune cells were negatively correlated with BCL9 expression (Fig.
7B). CD8+ T cell abundance was negatively correlated with BCL9 expression in f triple-negative breast
cancers[32]. Currently, immune checkpoint inhibitors, including anti-cytotoxic T-lymphocyte-associated
protein 4 (anti-CTLA4)[33], anti-PD-1[34], and anti-Programmed death-ligand 1 (PD-L1)[35] were agreed to
the cancer treatment. BCL9 was identied to be positively related with PD-L1 gene (CD724) expression
(Fig 7C). Because BCL9 activated Wnt/β-catenin pathway and MAPK, inhibited tumor killing immune cell
(CD8 T and NK cells) inltration, and promoted the expression of anti-tumor immune PD-L1, BCL9 further
promoted lymph node metastasis and the poor PFS of PTC (Fig. 1C).
5. Conclusions
By investigating the relationship between BCL9 and PTC, our study found that BCL9 expression is
correlated with the poor PFS and several anti-tumor inltrating immune cells in PTC, which highlight the
prognostic and predictive value of BCL9 in PTC. Whereas, there were several limitations to this study. We
did not verify the expression of BCL9 in MTC and NTC due to the limitation of the sample tissues and
performed experiment to evaluate the relationship between BCL9 and β-catenin.
Declarations
Funding
Page 12/20
This work was supported by the scientic research project of Wuhan Municipal Health Commission
(WX20B10), Special funds for scientic and technological development of Hubei Province (2022BGE233)
and Health Commission of Hubei Province scientic research project (WJ2021M001).
Availability of data and materials
The datasets can be obtained via the two databases (TCGA and HPA) or the rst authors on reasonable
request.
Authors' contributions
RZ and LZ conducted investigation, developed methodology, performed software analysis, wrote the
original draft, and edited the manuscript. ZG and DZ conducted the experiments and analyzed the data.
JZ revised the manuscript critically for intellectual content. All authors read and approved the nal
version of manuscript.
Ethics approval and consent to participate
The study was approved with the ethics committee of Tongji Hospital aliated with Tongji Medical
College of Huazhong University of Science and Technology (approval No. 2020S181). Informed consent
was provided by all patients.
Patient consent for publication
All patients agreed to use their tissues and articles for publication.
Declaration of Conict of Interests
The authors declare that the research was conducted in the absence of any commercial or nancial
relationships that could be construed as a potential conict of interest.
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Figures
Figure 1
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Analysis of the BCL9 expression based on the TCGA database.
(A) BCL9 was increased signicantly in PTC. (B) BCL9 was increased signicantly in paired PTC. (C)
BCL9 was associated with poorer PFS of PTC patients. (D) BCL9 was associated with clinical
characteristics of PTC patients and high BCL9 group signicantly correlated to lymph node metastasis.
Figure 2
The BCL9 expressed highly in PTC.
(A) The protein of BCL9 expressed highly in PTC by the HPA database. (B) The mRNA of BCL9 expressed
highly in PTC by qRT-PCR. (C) The protein of BCL9 expressed highly in PTC by IHC.
Figure 3
BCL9 is overexpressed in PTC cell lines.
(A) BCL9 expression was signicantly higher in TPC-1, K1, and KTC1 cells than in thyroid cells by qRT-
PCR. (B and C) The expression of BCL9was downregulated in TPC-1 (B) and K1 (C). (D and E)
Page 17/20
Knockdown of BCL9 signicantly inhibited TPC-1 (D) and K1 (E) cell proliferation by CCK8 assay.
Figure 4
Knockdown of BCL9 signicantly inhibited proliferation and invasion of PTC cells.
(A and B) Colony assay showed that BCL9 knockdown signicantly inhibited TPC-1 and K1 cell
proliferation. (C and D) Transwell assay showed that BCL9 knockdown signicantly inhibited the invasion
of TPC-1 (C) and K1 (D) cells. (E, F and G) Scratch assay showed that BCL9 knockdown signicantly
inhibited the invasion of TPC-1 (E) and K1 (F) cells.
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Figure 5
Functional annotation of BCL9.
(A) Co-expression genes based on BCL9 expression. (B) Differentially expressed genes (DEGs) between
high and low BCL9 expression groups. (C) GO enrichment analyses of DEGs. (D) KEGG enrichment
analysis of DEGs. (E) GSEA analysis to investigate the potential regulatory mechanisms.
Page 19/20
Figure 6
BCL9 is positively correlated with the activation states of Wnt/β-catenin and MAPK pathway.
(A-MWnt ligands - Wnt2, 2B, 3, 4, 5A, 7B, 8A, 8B, 9A, 9B, 10A, 16. (N) CTNNB1, β-catenin gene. (O) MAPK1
Page 20/20
Figure 7
The relationship among tumor immune microenvironment, tumor-inltrating immune cells, immune
checkpoint genes, and BCL9 expression.
(A) The correlation between BCL9 expression and TIME in PTC. (B) The correlation between BCL9
expression and various types of tumor-inltrating immune cells in PTC. (C) The heatmaps of correlation
between BCL9 expression and immune checkpoint genes. (D) The relationship between BCL9 expression
and tumor mutation burden (TMB) in PTC.
