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Cartilage oligomeric matrix protein acts as a molecular biomarker in multiple cancer types
Bingjie Guo1 . Yajing Wang2 . Wenyu Liu3 . Sailong Zhang4(D
Received: 14 August 2022 / Accepted: 26 September 2022 / Published online: 18 October 2022 @ The Author(s), under exclusive licence to Federación de Sociedades Españolas de Oncología (FESEO) 2022
Abstract
Purpose The main function of cartilage oligomeric matrix protein (COMP) is to maintain the synthesis and stability of the extracellular matrix by interacting with collagen. At present, there are relatively few studies on the role of this protein in tumors. This study aimed to explore the relationship between COMP and pan-cancer, and analyzed its diagnostic and prognostic value.
Methods The Cancer Genome Atlas database, the Genotype-Tissue Expression database and the Cancer Cell Line Encyclo- pedia database was used for gene expression analysis. The receiver operating characteristic curve was used to assess the diag- nostic value of COMP in pan-cancer. Kaplan-Meier plots were used to assess the relationship between COMP expression and prognosis of cancers. R software v4.1.1 was used for statistical analysis, and the ggplot2 package was used for visualization. Results COMP was significantly overexpressed in 15 human cancers and showed significantly difference in 12 molecular subtypes and 16 immune subtypes. In addition, the expression of COMP is associated with tumor immune evasion. The ROC curve showed that the expression of COMP could predict the occurrence of 16 kinds of tumors with relative accuracy, including adrenocortical carcinoma (ACC) (AUC = 0.737), breast invasive carcinoma (BRCA) (AUC = 0.896), colon adeno- carcinoma (COAD) (AUC = 0.760), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD) (AUC = 0.775), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (AUC = 0.875), kidney renal papillary cell carcinoma (KIRP) (AUC = 0.773), kidney chromophobe (KICH) (AUC = 0.809), ovarian serous cystadenocarcinoma (OV) (AUC = 0.906), prostate adenocarcinoma (PRAD) (AUC = 0.721), pancreatic adenocarcinoma (PAAD) (AUC = 0.944), rectum adenocarcinoma (READ) (AUC = 0.792), skin cutaneous melanoma (SKCM) (AUC = 0.746), stomach adenocar- cinoma (STAD) (AUC = 0.711), testicular germ cell tumors (TGCT) (AUC = 0.823), thymoma (THYM) (AUC = 0.777) and uterine carcinosarcoma (UCS) (AUC = 0.769). Furthermore, COMP expression was correlated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) in ACC (OS, HR = 4.95, DSS, HR = 5.55, PFI, HR = 2.79), BLCA (OS, HR = 1.59, DSS, HR = 1.72, PFI, HR = 1.36), KIRC (OS, HR = 1.36, DSS, HR = 1.94, PFI, HR = 1.57) and COADREAD (OS, HR = 1.46, DSS, HR = 1.98, PFI, HR = 1.43). We selected previously unreported blad- der urothelial carcinoma (BLCA) for further study and found that COMP could be an independent risk factor for OS, DSS and PFI. Moreover, we found differentially expressed genes of COMP in BLCA and obtained top 10 hub genes, including LGR4, LGR5, RSPO2, RSPO1, RSPO3, RNF43,ZNRF3, FYN, LYN and SYK. Finally, we verified the function of COMP at the cellular level by using J82 and T24 cells and found that knockdown of COMP could significantly inhibit migration and invasion. This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis.
Conclusion This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encom- passing tumor microenvironment, disease stage and prognosis.
Keywords Cartilage oligomeric matrix protein (COMP) · Molecular biomarker · Pan-cancer · Multiple omics integrative analysis · Tumor microenvironment
Bingjie Guo and Yajing Wang have equally contributed to the article.
Introduction
Tumor tissue is a complex assembly of tumor cells and their surrounding tumor microenvironment (TME) [1]. TME is an ecological niche that stimulates the progression of can- cer, which contains various cells, such as pathochemical entities, extracellular matrices, normal stromal fibroblasts, and immune cells [2]. An increasing number of reports have shown that there are interactions between tumor cells, stromal cells, and immune cells in the TME, which could influence anti-tumor immunity and immunotherapy [3, 4]. Immunosuppressive cells in the TME can inhibit the func- tion of cytotoxic T cells, allowing tumor immune evasion and reducing therapeutic efficacy [5, 6]. Unfortunately, it is very complicated to simultaneously study various cellular constituents and their molecular mechanisms in the TME by the traditional molecular biology experiments, especially for the complex and diverse immune phase cells in the TME [7, 8]. However, the bioinformatics approach has proven to be one of the most effective strategies to address this challenge. In addition, bioinformatics can also exert its advantages in research fields, such as tumor pathogenesis, mutation cap- ture, and biomarker detection.
COMP, one of the thrombospondin family, consists of five matricellular calcium-binding proteins that regulate growth factors, cytokines, and responses to injury [9]. The protein is composed of five identical subunits, which are linked by disulfide bonds to form a protein with 524 kDa. COMP can bind to collagen with high affinity through its C-terminal domain [10, 11]. Elevated COMP expression is associated with various diseases, such as systemic sclerosis, vascular atherosclerosis, and rheumatoid arthritis [12-14]. A recent report suggested that COMP could be used as a novel biomarker to assess the risk of liver cirrhosis and hepatocel- lular carcinoma [15]. In addition, it has been reported that COMP can also be a potential biomarker in chronic hepatitis C and liver fibrosis process [16]. Studies have shown that COMP mRNA expression may be highly correlated with tumorigenesis, including the occurrence and progression of colon, breast, and lung cancer [17-20]. However, there is currently insufficient scientific evidence to indicate whether COMP can be a marker of cancer and its role in the immune microenvironment.
Therefore, in this study, we investigated the expression and prognostic significance of COMP in multiple cancer types and examined the potential role of COMP in OS, DSS, and PFI. In addition, we investigated the effect of COMP on TME in different tumors. Moreover, we further detected COMP-related co-expressed genes and differen- tially expressed genes in the BLCA.
Materials and methods
COMP expression analysis
The tumor tissue data for this study were obtained from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases, with a total of 15,776 samples for 33 tumor types. The tumor cell line data in this study were obtained from the Cancer Cell Line Encyclo- pedia (CCLE) database [21]. The above data are used for statistical analysis using R Software V4.1.1.
The relationship between COMP expression and tumor immunity
The relationship between the expression of COMP and the molecular subtypes and immune subtypes in different tumor types was analyzed using TISIDB database [22]. In addition, this database was used to analyze the correlation between COMP expression and immunomodulator.
This study explored the relationship between COMP expression and immune cell infiltration in different tumor cells using TIMER2 database. In addition, we used the TIMER2 database to explore the correlation between COMP expression and infiltration of 4 immunosuppressive cells (cancer-associated fibroblasts, M2 subtype of tumor-asso- ciated macrophages, myeloid-derived suppressor cells, and regulatory T) in different tumor types.
COMP mutation landscape in different tumors
We used the cBioPortal database to explore the COMP mutation landscape as well as the copy-number alteration [23, 24].
COMP-binding proteins’ interaction network
A total of 50 COMP-binding proteins were obtained using the STRING network, with the parameters’ set to minimum required interaction score [“medium confidence (0.400)”]. The protein-protein interaction (PPI) was visualized using Cytoscape (version 3.7.1).
Gene ontology and Kyoto encyclopedia of genes and genomes analyses
The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the enrichment of 50 COMP-binding proteins. “ClusterProfiler” package in R was used for analysis [25, 26].
Values of COMP expression in predicting tumorigenesis
We used receiver-operating characteristic (ROC) curves to evaluate the accuracy of COMP in predicting the occurrence of different tumor types. AUC (0.5-0.7) means low accu- racy, AUC (0.7-0.9) means a certain accuracy, and AUC (>0.9) means a high accuracy.
Survival analysis in different tumor types
The Kaplan-Meier plots were used to assess the relation- ship between COMP expression and survival prognosis (OS, DSS and PFI) in different tumor types by COX regression analysis. The visualization was performed using “surminer” in the R package.
Relationship between COMP expression and clinical characteristics in BLCA
In BLCA, the relationship between COMP expression and different clinical characteristics was presented using box- plot. The downloaded fragments per kilobase per million data were converted into transcripts per million reads and analyzed after log2 conversion. The Wilcoxon rank sum test was used to detect two groups of data.
Univariate and multivariate cox regression analyses in BLCA
In BLCA, the expression of COMP and its clinical char- acteristics were analyzed by univariate and multivariate COX regression to further determine the prognostic value of COMP in OS, DSS, and PFI.
Searching for COMP co-expressed genes in BLCA
In BLCA, we explored 50 co-expressed genes that were posi- tively and negatively correlated with COMP expression. The volcano map was plotted in ggplot2 package with a threshold of |log2 fold-change|>1.0 and adjusted p value<0.05. In addition, we construct PPI network of different expression genes using a threshold of |log2 fold-change|>2.0. The MCC algorithm of “CytoHubba” in Cytoscape was used to analyze the central gene.
GSEA identifies COMP-related signaling pathways
To elucidate the potential effects of selected genes in the BLCA, the high- and low-expression groups were analyzed by GSEA using the MSigDB collection (h.all.v7.2.symbols. gmt).
Cell culture
The human HCC cell lines J82, T24 were purchased from Cell bank of Chinese Academy of Sciences. The J82 and T24 cells were cultured in high DMEM (Hyclone, USA) and MEM (Hyclone, USA) respectively, which supplemented with 1% streptomycin and penicillin and 10% FBS (Gibco, USA). Cells were cultured in a humidified atmosphere of 5% CO2 at 37 °℃.
Wound-healing assay and Transwell assays
For wound-healing assay, J82 and T24 cells were digested into a suspension at the density of 1× 105 cells/well, and spread evenly on 12-well plates. The cells were draw a thin line at the bottom by using a 10 uL pipette tip gently. The wounds at 0 h and 24 h were recorded under the micro- scope (Leica, Germany). Relative wound closure (%)= [Area (24 h)-Area (0 h)] / Area (0 h).
The migration assay was performed by Transwell cham- bers (Corning, USA) on 24-well cell culture plates. The upper and lower culture chambers were separated by 8-um pore diameter. J82 and T24 cells in a density of 1× 104/ well were seeded in the upper chamber. For the invasion assays, the Matrigel (BD Biosciences, USA) was diluted with serum-free medium and added 100 µL/well to the upper chamber sand. J82 and T24 cells were stained with 0.1% crystal violet for 20 min, and were observed under the microscope (Leica, Germany).
Western blot analysis
Whole-cell lysates were prepared for the Western blot analy- sis of expression of COMP (Abcam, ab231977, UK) and B-actin (CST, 4970, USA). The concentration of protein was determined by BCA kit (Thermo Scientific, A53225, USA). Protein samples were isolated using 10% SDS-PAGE gel and then transferred to polyvinylidene difluoride mem- branes. The secondary antibodies (CST, 7074, USA) were incubated, and the membranes were washed and visualized using enhanced chemiluminescence kit (Thermo Scientific, USA) and Gel Imaging System (Syngene, USA).
Knockdown of COMP in cells
Small interfering RNA specific to COMP (si-COMP) (5’- AGAAACUUGAGCUGUUGAUGCC-3’, 5’-GGCUAU CAAGACAGCUCAAGUUUCU-3’) and the scramble siRNA (control) were purchased from Shanghai Bio-Link Company (Shanghai, China). J82 and T24 cells were spread on 6-well plates and then transfected with 100 nM siRNA using Lipofectamine 2000 (Invitrogen, Eugene, OR, USA).
Results
COMP expression in different tumors
In this study, we first investigated the expression of COMP in normal tissues. COMP showed relatively high level in adipose tissue, blood vessel, breast, cervix uteri, lung, nerve, prostate, salivary, skin, testis, thyroid, and vagina. On the contrast, especially, the expression of COMP in blood vessels was the highest in all tissues. However, COMP showed relatively low level in adrenal gland, blood, bone marrow, brain, fallopian tube, liver, ovary, pituitary, and spleen (Fig. 1a). CCLE database results showed that COMP was expressed in all cell lines. As the figure shown, embryonal cell lines showed relative high expression of COMP and bile duct showed relative low expression of COMP (Fig. 1b). The TCGA database showed that COMP is significantly overexpressed in 10 cancer types, such as BLCA, colon adenocarcinoma (COAD), and kidney renal clear cell carcinoma (KIRC) (Fig. 1c). In addition, results from the GTEX database showed that COMP was sig- nificantly increased in 15 cancer types, such as BLCA, COAD, and KIRC (Fig. 1d).
Relationship between COMP expression and immune or molecular subtypes in different tumors
As the results from the TISIDB database shown, COMP expression was correlated with the immune subtypes of 16 kinds of tumors, including BLCA (Fig. 2a), breast invasive carcinoma (BRCA) (Fig. 2b), cervical squamous cell carci- noma and endocervical adenocarcinoma (CESC) (Fig. 2c), head and neck squamous cell carcinoma (HNSC) (Fig. 2d), kidney chromophobe (KICH) (Fig. 2e), kidney renal pap- illary cell carcinoma (KIRP) (Fig. 2f), liver hepatocel- lular carcinoma (LIHC) (Fig. 2g), lung adenocarcinoma (LUAD) (Fig. 2h), lung squamous cell carcinoma (LUSC) (Fig. 2i), mesothelioma (MESO) (Fig. 2j), ovarian serous cystadenocarcinoma (OV) (Fig. 2k), prostate adenocar- cinoma (PRAD) (Fig. 2l), sarcoma (SARC) (Fig. 2m), skin cutaneous melanoma (SKCM) (Fig. 2n), testicular germ cell tumors (TGCT) (Fig. 2o), and thyroid carcinoma (THCA) (Fig. 2p).
Moreover, we also explored the relationship between COMP expression and molecular subtypes, and found that COMP was correlated with 12 cancer types such as BRCA, COAD, and HNSC. For BRCA, COMP was identified to express less in basal (Fig. 3a). For COAD, COMP was expressed the highest in CIN (Fig. 3b). For HNSC, mesenchymal showed a higher COMP level than
other groups (Fig. 3c). COMP was expressed more in c2c-CIMP than any other groups in KIRP (Fig. 3d). For LIHC, COMP showed a higher expression in icluster1 than all the other groups (Fig. 3e). For LUSC, COMP was expressed more in secretory than anyone else (Fig. 3f). For OV, no groups expressed more COMP than mesen- chymal (Fig. 3g). No groups expressed COMP so much as kinase signaling in pheochromocytoma and paragan- glioma (PCPG) (Fig. 3h). For PRAD, COMP expressed ever so much in ETV4 (Fig. 3i). For stomach adenocar- cinoma (STAD), COMP was expressed the highest in GS (Fig. 3j). For SKCM, COMP was expressed the highest in hotspot mutants (Fig. 3k). For uterine corpus endometrial carcinoma (UCEC), COMP was expressed the highest in CN-HIGH (Fig. 3l).
Conspicuously, the COMP expression showed a signifi- cantly correlation with the immune stimulators (Supplemen- tary Fig. S1) and immune inhibitors (Supplementary Fig. S2) in the most tumors except for GBM and LGG. Further- more, we also revealed the association of chemokines with COMP, among which most chemokines could be regulated by COMP except CCL16, CCL24, and CCL25 (Supplemen- tary Fig. S3). Furthermore, COMP exerted a vital role in modulating most receptors in the most tumors except for GBM and LGG (Supplementary Fig. S4).
COMP mutation landscape in different tumors
A total of 10,967 samples from 32 studies in TCGA were used to explore COMP mutations using the cBioPortal data- base. The results showed that the COMP mutation frequency was the highest in OV and UCEC, both exceeding 5%. In SKCM, the mutation frequency of COMP was more than 4%, which is also at a relative high level (Fig. 4a). In terms of mutation counts, COMP levels were higher in SKCM, LUSC, and BLCA than other tumor types (Fig. 4b). In addi- tion, the mutation site of COMP showed that a total of 119 sites were mutated in COMP, which had a total of 757 amino acids. Among them, R672Q and R740S sites showed higher mutation frequency than any other sites (Fig. 4c).
COMP is associated with immunoevasive TME via different mechanisms
For 39 tumor types in TCGA database, we investigated the association between COMP and 6 common types of immune cells infiltration (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells). The results showed that, except for ACC, DLBC, KICH, UCS, and UVM, the more expression of COMP, the more immune cells infiltrated in other TME. Furthermore, in HNSC, LIHC, LUSC, and PAAD, up-regulated COMP was positively associated with infiltration of all six types of immune cells.
a
15
The expression of COMP Log2 (TPM+1)
0
10
o
00 D
IN GD
000
D
0
5
P
0
300 000
0
000 0
090
%
0
0
A
o
4
D
-
0
0
O
M
0
0
®
Adipose Tissue
Adrenal Gland
Bladder
Blood
Blood Vessel
Bone Marrow
Brain
Breast
Cervix Uteri
Colon
Esophagus
Fallopian Tube
Heart
Kidney
Liver
Lung
Muscle
Nerve
Ovary
Pancreas
Pituitary
Prostate
Salivary Gland
Skin
Small Intestine
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
b
1.75
·
The expression of COMP
1.50
·
0
·
0
0
0
0
1.25
50
000 0
CEDIDO
00
o
0
00
0
-000 0
0
0
0
8
8
0
o
?
1
8
1.00
O
9
0.75
o
8
O
o
00
6
0
0300
8
o
0
₾
0
0
0
0
0
0
0.50
Bile Duct
Bladder
Bone
Brain
Breast Cervical
o
Colon/Colorectal
Embryonal
Endometrial/Uterine
Engineered
Esophageal
Eye
Fibroblast
Gallbladder
Gastric
Head and Neck
Kidney
Leukemia
Liposarcoma
Liver
Lung
Lymphoma
Myeloma
Neuroblastoma
Non-ous
Ovarian
Pancreatic
Prostate
Rhabdoid
Sarcoma
Skin
Teratoma
Thyroid
c
10
ns
ns
Normal
The expression of COMP Log2 (TPM+1)
4
8
Tumor
**
**
**
6
**
ns
ns
ns
4
2
0
T
BLCA
BRCA
CHOL
COAD
ESCA
HNSC
KICH
KIRC
KIRP
LIHC
T
T
LUAD
LUSC
PAAD
PRAD
READ
STAD
THCA
UCEC
0
12
₩
ns
ÅR
ns
4%
*
ns
.
Normal
The expression of COMP Log2 (TPM+1)
10
.. ..
E
Tumor
.
.
.
.
. …
·
¿
Co
…
₹
.
.
.
·
6
..
…
…
-
.. …
.
…
.
4
. .
…
…
·
-
.
2
·
-
A
..
·
0
S
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
BLCA :: COMP_exp
a
Pv=2.75e-08
b
BRCA :: COMP_exp Pv=1.06e-15
CESC :: COMP_exp Pv=3.17e-02
n=C1 369,C2 390,C3 191,C4 92,C6 40
c
HNSC : COMP_exp
n=C1 173,C2 164,C3 21,C4 36,C6 3
n=C1 77,C2 217,C4 6
d
Pv=4.6e-03
n=C1 128,C2 379,C3 2,C4 2,C6 3
Expression (log2CPM)
20
Expression (log2CPM)
Expression (log2CPM)
10
10
Expression (log2CPM)
10
10
5
5
5
0
0
0
0
-10
-5
-5
-5
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
-10
C1
C2
C4
Subtype
C3
Subtype
Subtype
C1
C2
C4
C6
Subtype
KICH :: COMP_exp
e
Pv=4.9e-02 n=C1 2,C3 38,C4 12,C5 13
f
KIRP :: COMP_exp
Pv=6.02e-04
g
LIHC :: COMP_exp
Pv=3.91e-11
n=C1 22,C2 45,C3 135,C4 159,C6 1
h
LUAD : COMP_exp
n=C1 3,C2 4,C3 202,C4 66,C5 2,C6 2
Pv=6.8e-05
n=C1 83,C2 147,C3 179,C4 20,C6 28
Expression (log2CPM)
Expression (log2CPM)
20
Expression (log2CPM)
10
Expression (log2CPM)
10
5
10
5
5
0
₿
0
₿
0-
8
-5
:
0
-5
-10
-5
-10
-20
-10
C1
C3
C4
C5
Subtype
C1
C2
C3
C4
C5
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
Subtype
Subtype
Subtype
LUSC :: COMP_exp
Pv=1.47e-03
MESO :: COMP_exp Pv=5.09e-03
k
OV :: COMP_exp Pv=3.52e-03
PRAD :: COMP_exp
n=C1 275,C2 182,C3 8,C4 7,C6 14
n=C1 32,C2 21,C3 8,C4 11,C6 11
n=C1 46,C2 159,C3 3,C4 61
Pv=6.99e-05
n=C1 35,C2 18,C3 307,C4 45
Expression (log2CPM)
10
Expression (log2CPM)
10
Expression (log2CPM)
Expression (log2CPM)
10
10
5
8
5
5
0
5
0
0
-5
0
-5
-5
-10
-10
-5
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
-10
C1
C2
C3
C4
C1
C2
C3
C4
Subtype
Subtype
Subtype
Subtype
m
SARC :: COMP_exp Pv=4.6e-04 n=C1 64,C2 38,C3 42,C4 59,C6 20
n
SKCM :: COMP_exp Pv=1.48e-02 n=C1 41,C2 27,C3 14,C4 19,C6 2
o
TGCT :: COMP_exp Pv=3.19e-02 n=C1 42,C2 104,C3 2,C4 1
p
THCA: COMP_exp
Pv=7.17e-03
n=C1 2,C2 13,C3 459,C4 22,C6 3
Expression (log2CPM)
20
Expression (log2CPM)
10
Expression (log2CPM)
10
Expression (log2CPM)
10
10
5
5
5
₿
0
0
0
0
-5
-5
-10
-10
-5
-
-10
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C1
C2
C3
C4
C6
Subtype
Subtype
Subtype
Subtype
COMP showed an excellent correlation with immune cells infiltration in LUSC (r=0.23 ~ 0.48) and PAAD (r=0.17 ~ 0.43), and a good correlation in HNSC (r=0.14 ~ 0.35) and LIHC (r=0.25 ~ 0.37). Furthermore, we found the strong- est correlation between COMP expression and neutrophil infiltration in CHOL (r=0.55) (Fig. 5a).
In addition, we investigated the association between COMP expression and infiltration of common immuno- suppressive cells that promote T-cell exclusion (CAF,
Macrophage M2, MDSC, and Tregs). The results showed that the expression of COMP was positively correlated with the invasion of CAF, but negatively correlated with the invasion of macrophage M2 in most tumors except GBM, PCPG, and THYM. Notably, in KIRC, LIHC, MESO, and UCES, the expression of COMP expression had positive correlation with tumor infiltration of MSDC, while in BLCA, BRCA, COAD, HNSC, HNSC-HPV-, LUSC, PCPG showed the opposite. In addition, for Tregs,
BRCA :: COMP_exp
COAD :: COMP_exp
a
Pv=2.91e-16 n=Basal 172,
b
Pv=3.24e-02 n=CIN 226, GS 49.
HNSC :: COMP_exp Pv=2.05e-14
KIRP :: COMP_exp Pv=2.46e-06 n=C1 95,
C
Her2 73.
n=Atypical 67, Basal 87, Classical 48, Mesenchymal 74
d
LumA 508,
HM-SNV 6.
C2a 35, C2b 22.
LumB 191.
15
Normal 137
HM-indel 60
C2c-CIMP 9
Expression (log2CPM)
15
Expression (log2CPM)
Expression (log2CPM)
10
Expression (log2CPM)
10
10
:
10
5
5
5
:
:
5
0
0
0
0
-5
-5
-5
-5
-10
-10
-10
-15
Basal
Her2
LumA
LumB
Normal
Atypical
Basal
Classical
Mesenchymal
CIN
GS
HM-SNV
HM-indel
C1
C2a
C2b
C2c-CIMP
Subtype
Subtype
Subtype
Subtype
e
LIHC :: COMP_exp
Pv=4.79e-06
f
LUSC :: COMP_exp
Pv=1.53e-06
n=basal 42, classical 63, primitive 26, secretory 39
g
OV :: COMP_exp
Pv=1.02e-16
n=Differentiated 66, Immunoreactive 78, Mesenchymal 71. Proliferative 78
h
PCPG : COMP_exp
n=iCluster:1 64,
Pv=1.93e-08
iCluster:2 55,
iCluster:3 63
Expression (log2CPM)
10
Expression (log2CPM)
10
Expression (log2CPM)
n=Corticaladmixture 22, Kinasesignaling 68, Pseudohypoxia 61. Wnt-altered 22
15
Expression (log2CPM)
10
5
10
5
5
5
0
8
!
0
0
0
:
-5
-5
-5
-5
:
-10
-10
-10
iCluster:1
iCluster:2
iCluster:3
basal
classical
primitive
secretory
Differentiated
Immunoreactive
Mesenchymal
Proliferative
Corticaladmixture
Kinasesignaling
Pseudohypoxia
Wnt-altered
Subtype
Subtype
Subtype
Subtype
i
PRAD :: COMP_exp
STAD :: COMP_exp Pv=4.4e-03
k
UCEC :: COMP_exp
Pv=3.7e-03
SKCM :: COMP_exp Pv=8.02e-03
Pv=5.61e-04
n=1-ERG 152,
n=CIN 223, EBV 30, GS 50.
Expression (log2CPM)
n=BRAF_Hotspot_Mutants 150, NF1_Any_Mutants 27. RAS_Hotspot_Mutants 92. Triple_WT 46
n=CN_HIGH 160, CN_LOW 144, MSI 124, POLE 79
2-ETV1 28.
3-ETV4 14.
4-FLI1 4,
5-SPOP 37.
HM-SNV 7, HM-indel 73
6-FOXA1 9.
15
Expression (log2CPM)
15
7-IDH1 3,
Expression (log2CPM)
Expression (log2CPM)
8-other 86
10
10
5
10
10
5
0
:
-5
5
5
0
8
8
C
G
0
6
-10
0
_Hotspot_Mutants
NF1_Any_Mutants
RAS_Hotspot_Mutants
Triple_WT
0
-5
-5
-5
-10
-10
1-ERG
2-ETV1
3-ETV4
4-FLI1
5-SPOP
Sub. 6-FOXA1
7-IDH1
8-other
CIN
EBV
GS
HM-SNV
HM-indel
CN_HIGH
CN_LOW
MSI
POLE
Subtype
Subtype
Subtype
Subtype
COMP expression was positively correlated in PCPG and THCA, and negatively correlated in SKCM and SKCM- metastasis (Fig. 5b).
We next explored the impact of COMP on immune check- point blockade. Moreover, we also compared with other bio- markers to explore the accuracy of COMP in predicting OS. The results showed that in 25 cohort studies, COMP could function in 9 groups (AUC>0.5). The predictive ability of COMP is comparable to that of T. Clonality, but lower than that of TIDE, MSI. score, CD274 CD8, IFNG, and Merck18. This indicates that COMP is at a moderate level for OS pre- diction accuracy (Fig. 5c).
GO and KEGG analyses of COMP-binding proteins
We screened 50 COMP-related binding proteins using STRING, visualized them with Cytoscape, and performed GO and KEGG enrichment analysis. As the results shown, GO enrichment mainly contained extracellular structure organization, plasma membrane receptor complex, and extracellular matrix structural constituent (Fig. 6c). KEGG analysis showed that the pathway related to ECM-receptor interaction, PI3K-Akt signaling pathway, Human papillo- mavirus infection, and Focal adhesion (Fig. 6d).
Clinical and Translational Oncology (2023) 25:535-554
Mutation
Structural Variant
Amplification
Deep Deletion
☒ Multiple Alterations
☒
☒
☒
☒
TGCT (TCGA, PanCancer Atlas)
Amplification
PCPG (TCGA, PanCancer Atlas)
KIRP (TCGA, PanCancer Atlas)
Shallow Deletion
KIRC (TCGA, PanCancer Atlas)
KICH (TCGA, PanCancer Atlas)
THCA (TCGA, PanCancer Atlas) LAML (TCGA, PanCancer Atlas) ☐
☒ Gain
☐ Diploid
Deep Deletion
Structural Variant Splice (VUS)
☐ GBM (TCGA, PanCancer Atlas)
Truncating (VUS)
HNSC (TCGA, PanCancer Atlas)
☒
Inframe (VUS)
PRAD (TCGA, PanCancer Atlas)
☐
Missense (VUS)
THYM (TCGA, PanCancer Atlas)
☐
PAAD (TCGA, PanCancer Atlas) LGG (TCGA, PanCancer Atlas)
☒
Not mutated
☒ LUAD (TCGA, PanCancer Atlas)
☒
UVM (TCGA, PanCancer Atlas) BRCA (TCGA, PanCancer Atlas) STAD (TCGA, PanCancer Atlas) LUSC (TCGA, PanCancer Atlas)
☐
DLBC (TCGA, PanCancer Atlas)
BLCA (TCGA, PanCancer Atlas)
Melanoma
ACC (TCGA, PanCancer Atlas)
Non-Small Cell Lung Cancer Bladder Urothelial Carcinoma
6%
MESO (TCGA, PanCancer Atlas)
Esophagogastric Adenocarcinoma
542
5%
SARC (TCGA, PanCancer Atlas) COAD (TCGA, PanCancer Atlas)
Mature B-Cell Neoplasms
Colorectal Adenocarcinoma
a
Alteration Frequency
4%
CESC(TCGA, PanCancer Atlas)
000098880099000006
Head and Neck Squamous Cell Carcinoma
3%
ESCA (TCGA, PanCancer Atlas)
Esophageal Squamous Cell Carcinoma
Cervical Squamous Cell Carcinoma
757aa
Fig. 4 COMP mutation landscape in different tumors. a The mutation frequency of COMP in different TCGA studies. b The counts of mutations
2%
CHOL (TCGA, PanCancer Atlas)
LIHICI (TCGA, PanCancer Atlas)
Undifferentiated Stomach Adenocarcinoma
☒
Hepatocellular Carcinoma
1%
UCS (TCGA, PanCancer Atlas)
Cervical Adenocarcinoma
Structural variant data
SKCM (TCGA, PanCancer Atlas)
Ovarian Epithelial Tumor
Mutation data CNA data
SV/Fusion (1)
UCEC (TCGA, PanCancer Atlas)
Endometrial Carcinoma
Ov (TCGA, PanCancer Atlas)
Renal Non-Clear Cell Carcinoma
TSP_C
Renal Clear Cell Carcinoma
Glioblastoma
☒
Sarcoma Invasive Breast Carcinoma
Splice (8)
600
Cholangiocarcinoma
Pancreatic Adenocarcinoma
Pleural Mesothelioma
☒
Diffuse Glioma
TSP_3
Adrenocortical Carcinoma
14
Prostate Adenocarcinoma
Inframe (1)
TSP_3
b
Mutation Count (log2(value + 1))
12
Fibrolamellar Carcinoma
Thymic Epithelial Tumor Leukemia
TSP_3
10
☒
TS
Non-Seminomatous Germ Cell Tumor
400
8
Seminoma
TSP_3
Ocular Melanoma
6
Well-Differentiated Thyroid Cancer
Truncating
Pheochromocytoma
TSP_3
4
Miscellaneous Neuroepithelial Tumor
2
Encapsulated Glioma
☒
0
Missense (102)
EGF_CA
200
☒
EGF_CA
in different tumors. c The mutation sites of COMP
COMP
C
# COMP Mutations
5
0
0
a
ACC
b
ACC
C
Random
BLCA
BLCA
COMP
BRCA
BRCA
BRCA-Basal
BRCA-Basal
BRCA-Her2
BRCA-Her2
BRCA-Luminal
BRCA-LumA
TIDE
CESC
BRCA-LumB
CHOL
CESC
Zhao2019_PD1_Glioblastoma_Pre
COAD
CHOL
Pos=8,Neg=7
MSI.Score
847
Zhao2019_PD1_Glioblastoma_Post
DLBC
COAD
Pos=6,Nog=3
VanAllen2015_CTLA4_Melanoma
ESCA
DLBC
Pos=19,Neg=23
Uppaluri2020_PD1_HNSC_Pre
GBM
ESCA
P value
Pos=8,Neg=15
P value
GBM
Uppaluri2020_PD1_HNSC_Post
HNSC
1.00
1.00
147
Pos=9,Neg=13
HNSC-HPVneg
HNSC
6.45
Ruppin2021_PD1_NSCLC
0.75
HNSC-HPV-
0.75
TMB
Pos=7,Neg=15
HNSC-HPVpos
Riaz2017_PD1_Melanoma_Ipi.Prog
KICH
HNSC-HPV+
0.50
0.50
Pos=4,Neg=22
KIRC
KICH
Riaz2017_PD1_Melanoma_Ipi.Naive
Pos=6,Neg=19
0.25
KIRC
0.25
CD274
Prat2017_PD1_NSCLC-HNSC-Melanoma Pos=21,Neg=12
KIRP
0.00
KIRP
0.00
Nathanson2017_CTLA4_Melanoma_Pre
LGG
LGG
Pos=4,Neg=5
LIHC
Correlation
LIHC
Correlation
Nathanson2017_CTLA4_Melanoma_Post
Pos=4,Nog=11
LUAD
1.0
1.0
Miao2018_JCB_Kidney_Clear
LUAD
Pos=20,Neg=13
LUSC
0.5
LUSC
0.5
CD8
McDermott2018_PDL 1_Kidney_Clear
Pos=20,Neg=61
MESO
0.0
MESO
0.0
Mariathasan2018_PDL1_Bladder_mUC
OV
Pos=68,Neg=230
-0.5
OV
-0.5
Liu2019_PD1_Melanoma_Ipi.Prog
PAAD
PAAD
Pos=16,Neg=31
-1.0
Liu2019_PD1_Melanoma_Ipi.Naive
PCPG
-1.0
PCPG
IFNG
.
Pos=33,Neg=41
PRAD
PRAD
Lauss2017_ACT_Melanoma
Pos=10,Neg=15
READ
READ
Kim2018_PD1_Gastric
Pos=12,Neg=33
SARC
SARC
T.Clonality
Hugo2016_PD1_Melanoma
SKCM
SKCM
Pos=14,Neg=12
Hee2020_PD1_NSCLC_Oncomine
SKCM-Metastasis
SKCM-Metastasis
Pos=9,Nog=12
Gide2019_PD1_Melanoma
SKCM-Primary
SKCM-Primary
Pos=19,Neg=22
Gide2019_PD1+CTLA4_Melanoma
STAD
STAD
B.Clonality
Pos=21,Neg=11
-
TGCT
TGCT
Chen2016_PD1_Melanoma_Nanostring
Pos=6,Neg=9
THCA
THCA
Chen2016_CTLA4_Melanoma_Nanostring
Pos=5,Neg=11
THYM
THYM
Braun2020_PD1_Kidney_Clear
UCEC
Pos=201,Neg=94
UCEC
UCS
UCS
UVM
UVM
Purity
B Cell
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
CAF
Macrophage M2
Merck18
MDSC
Tregs
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
AUC
Diagnostic value analysis of COMP in pan-cancer
The ROC curve was used to evaluate the predictive diag- nostic value of COMP. As the results shown, COMP had a certain accuracy (AUC>0.7) in predicting 16 cancer types, including ACC, BRCA, COAD, COADREAD, DLBC, KIRP, KICH, OV, PRAD, PAAD, READ, SKCM, STAD, TGCT, THYM, and UCS. Among them, COMP showed the highest accuracy (AUC>0.9) in predicting OV and PAAD (Fig. 7).
Prognostic performance of COMP in pan-cancer
Conspicuously, the expression level of COMP was mark- edly associated with the OS, DSS, and PFI of ACC, BLCA, KIRC, and COADREAD. Cox regression results showed
that poor prognosis was correlated with higher COMP expression in ACC, OS (HR 4.95) (Fig. 8a), DSS (HR 5.55) (Fig. 8b), and PFI (HR 2.79) (Fig. 8c). For BLCA, KIRC, and COADREAD, the same conclusion could be obtained that the upregulation of COMP predicted poor progno- sis. For BLCA, OS (HR 1.59) (Fig. 8d), DSS (HR 1.72) (Fig. 8e), and PFI (HR 1.36) (Fig. 8f). For KIRC, OS (HR 1.36) (Fig. 8g), DSS (HR 1.94) (Fig. 8h), and PFI (HR 1.57) (Fig. 8i). For COADREAD, OS (HR 1.46) (Fig. 8j), DSS (HR 1.98) (Fig. 8k), and PFI (HR 1.43) (Fig. 8l).
Furthermore, we concluded that elevated expression of COMP was correlated with a worse prognosis in most clini- cal subgroups of BCLA. For OS, age < = 70, gender (Male), N stage (N0), race (White), radiation therapy (No), smoker (Yes), lymphovascular invasion (Yes), primary therapy out- come (CR), and histologic grade (High Grade) (Fig. 9a); For
a
ITGA4
PTK2
DCN ACAN
ITGA9
b
extracellular structure organization extracellular matrix organization integrin-mediated signaling pathway
SOX9
MMP13
ITGB3
ITGA3
CD47
integrin complex
protein complex involved in cell adhesion
CILP
ITGA2
plasma membrane receptor complex
extracellular matrix structural constituent
ITGB4
COL9A1
FN1
ITGB5
collagen binding
ITGA2B
integrin binding
MMP3
ECM-receptor interaction
Focal adhesion
ITGB8
ITGAV
PI3K-Akt signaling pathway
Counts
BGN
COL9A2
COL9A3
CD36
13
FMOD
COMP
SDC1
28
ITGB7
ITGA5
44
ITGA1
COL2A1
COL1A1
VWF
SDC4
ANGPT1
ITGB6
ITGB1
ADAMTS14
MATN4
ITGA7
ITGA11
PRG4
MATN1
CHAD
ASPN
ITGA10
ADAMTS12
ITGA6
ADAMTS7
ITGA8
MATN3
C
d
extracellular structure organization
ECM-receptor interaction
extracellular matrix organization
of
PI3K-Akt signaling pathway
cell-substrate adhesion
p.adjust
Human papillomavirus infection
p.adjust
integrin-mediated signaling pathway
1.312720e-14
5e-09
9.845401e-15
Focal adhesion
4e-09
3e-09
plasma membrane receptor complex
6.563601e-15
2e-09
3.281800e-15
1e-09
protein complex involved in cell adhesion
Regulation of actin cytoskeleton
8
5.400890e-63
integrin complex
Counts
Dilated cardiomyopathy
Counts
10
☐
9
focal adhesion
☐ 27
Hypertrophic cardiomyopathy
☐ 21
☐
☐ 33
44
extracellular matrix structural constituent
Arrhythmogenic right ventricular cardiomyopathy
integrin binding
collagen binding
MA
Proteoglycans in cancer
Hematopoietic cell lineage
extracellular matrix binding
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
GeneRatio
GeneRatio
DSS, age< =70, gender (Male), N stage (N0), race (White), radiation therapy (No), smoker (Yes), lymphovascular inva- sion (Yes), histologic grade (High Grade), and BMI < = 25 (Fig. 9b); For PFI, age < = 70, gender (Male), race (White), radiation therapy (No), smoker (Yes), lymphovascular inva- sion (Yes), BMI ⇐ 25, subtype (Papillary), and pathologic stage (Stage III) (Fig. 9c).
Effects of COMP in different clinical characteristics of BLCA
We investigated the relationship between the expression of COMP and the different clinical features of BLCA. The results showed that the expression of COMP was
significantly correlated with pathologic stage, T stage, N stage, radiation therapy, race, histologic grade, and sub- type of BLCA (Table 1). In addition, we found that COMP was up-regulated in patients with age > 70 (Fig. 10a), path- ologic stage III/IV (Fig. 10b), T stage III/IV (Fig. 10c), and N stage II/III (Fig. 10d), while it was down-regulated in radiation therapy (Yes) (Fig. 10e), primary therapy outcome (CR) (Fig. 10f), subtype (Papillary) (Fig. 10g), lymphovascular invasion (No) (Fig. 10h), and race (Asian) (Fig. 10i), respectively.
a
ACC
b
BRCA
C
COAD
d
COADREAD
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
COMP
COMP
COMP
COMP
AUC: 0.737
0.2
AUC: 0.896
0.2
AUC: 0.760
0.2
AUC: 0.775
0.0
CI: 0.657-0.816
0.0
CI: 0.877-0.914
0.0
CI: 0.722-0.799
Cl: 0.741-0.809
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
e
DLBC
f
KIRP
g
KICH
h
OV
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
COMP
0.2
COMP
0.2
COMP
COMP
AUC: 0.875
AUC: 0.773
AUC: 0.809
0.2
AUC: 0.906
0.0
CI: 0.807-0.943
0.0
CI: 0.716-0.829
0.0
CI: 0.732-0.886
0.0
CI: 0.871-0.940
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
i
PRAD
j
PAAD
k
1.0
READ
I
SKCM
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
COMP
0.2
COMP
0.2
COMP
COMP
AUC: 0.721
AUC: 0.944
AUC: 0.792
0.2
AUC: 0.746
0.0
Cl: 0.677-0.764
0.0
CI: 0.914-0.974
0.0
Cl: 0.730-0.855
0.0
CI: 0.718-0.774
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
m
STAD
n
TGCT
o
1.0
THYM
p
UCS
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
COMP
AUC: 0.711
0.2
COMP
AUC: 0.823
0.2
COMP
AUC: 0.777
0.2
COMP
AUC: 0.769
0.0
Cl: 0.670-0.751
0.0
CI: 0.772-0.873
0.0
CI: 0.722-0.832
0.0
CI: 0.689-0.849
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
Univariate and multivariate cox regression analyses in BLCA
We used univariate and multivariate Cox regression analy- sis in BLCA to explore the correlation between COMP expression and clinical characteristics. As the results
shown, in OS, age, primary therapy outcome, pathologic stage, and COMP expression were prognostic risk fac- tors (Table 2), while in DSS, pathologic stage, primary, therapy outcome, and COMP were prognostic risk factors (Supplementary Table S1), and in PFI, pathologic stage,
a
ACC
b
ACC
C
ACC
1.0
COMP
1.0
COMP
1.0
4
COMP
Survival probability
Low
Survival probability
Low
Low
0.8
High
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
#
0.4
0.4
0.4
0.2
Overall Survival
Disease Specific Survival
HR = 4.95 (2.00-12.23)
0.2
0.2
Progress Free Interval
HR = 5.55 (2.08-14.76)
HR = 2.79 (1.45-5.37)
0.0
P = 0.001
0.0
P = 0.001
0.0
P = 0.002
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
Low
39
16
2
Low
38
15
7
2
Low
39
13
6
2
High
40
12
0
High
39
12
0
High
40
6
0
0)
d
BLCA
e
BLCA
f
BLCA
1.0
COMP
1.0
-
COMP
1.0
-
COMP
Survival probability
Low
Survival probability
Low
0.8
High
Survival probability
Low
0.8
High
0.8
High
0.6
0.6
0.6
0.4
0.4
H
0.4
+
0.2
Overall Survival
++
0.2
Disease Specific Survival
HR = 1.59 (1.18-2.14)
HR = 1.72 (1.20-2.48)
0.2
Progress Free Interval
HR = 1.36 (1.01-1.83)
0.0
P = 0.002
0.0
P = 0.004
0.0
P = 0.04
0
40
80
120
160
0
40
80
120
160
0
40
80
120
160
Time (months)
Time (months)
Time (months)
Low
207
42
9
2
1
Low
199
42
9
2
1
Low
207
35
8
2
1
High
206
39
14
4
2
High
200
39
14
4
2
High
207
32
11
3
1
g
KIRC
h
KIRC
İ
KIRC
1.0
COMP
1.0
COMP
1.0
J
COMP
Survival probability
Low
Survival probability
Low
0.8
High
0.8
High
Survival probability
Low
0.8
High
0.6
0.6
0.6
0.4
0.4
0.4
H
0.2
Overall Survival HR = 1.36 (1.01-1.84)
0.2
Disease Specific Survival
HR = 1.94 (1.31-2.88)
0.2
Progress Free Interval HR = 1.57 (1.14-2.15)
0.0
P = 0.043
0.0
P = 0.001
0.0
P = 0.005
0
50
100
150
0
50
100
150
0
50
100
Time (months)
Time (months)
Time (months)
Low
269
106
18
1
Low
262
104
18
1
Low
268
90
13
0
High
270
101
22
0
High
266
99
22
0
High
269
83
15
0
COADREAD
k
COADREAD
I
COADREAD
1.0
COMP
1.0
COMP
1.0
COMP
Survival probability
Low
Survival probability
Low
Survival probability
Low
0.8
High
0.8
++ High
0.8
High
0.6
0.6
0.6
+
0.4
+
0.4
0.4
+
0.2
Overall Survival HR = 1.46 (1.03-2.08)
0.2
Disease Specific Survival HR = 1.98 (1.23-3.16)
0.2
Progress Free Interval
HR = 1.43 (1.05-1.94)
0.0
P = 0.033
0.0
P = 0.005
0.0
P = 0.024
0
50
100
150
0
50
100
150
0
50
100
150
Time (months)
Time (months)
Time (months)
Low
321
43
9
0
Low
306
38
9
0
Low
321
35
8
0
High
322
32
7
1
High
315
31
7
1
High
322
24
6
1
Springer
Fig. 9 Relationship of COMP expression with the a OS, b DSS, and c PFI in different clinical subgroups of BLCA
a
| Characteristics | HR (95% CI) | OS | P value |
|---|---|---|---|
| Age ( <= 70) | 1.73(1.13-2.67) | 0.013 | |
| Gender (Male) | 1.60(1.12-2.28) | 0.01 | |
| N stage (NO) | 2.35(1.20-4.58) | 0.012 | |
| Race (White) | 1.67(1.21-2.31) | 0.002 | |
| Radiation therapy (No) | 1.73(1.26-2.39) | 0.001 | |
| Smorker (Yes) | 2.05(1.44-2.92) | <0.001 | |
| Lymphovascular invasion (Yes) | 1.78(1.14-2.79) | 0.011 | |
| Primary therapy outcome (CR) | 2.18(1.22-3.90) | 0.008 | |
| Histologic grade (High Grade) | 1.51(1.12-2.03) | 0.007 |
0
1
2
3
4
b
| Characteristics | HR (95% CI) | DSS | P value |
|---|---|---|---|
| Age ( <= 70) | 1.84(1.13-3.00) | 0.014 | |
| Gender (Male) | 1.85(1.19-2.87) | 0.006 | |
| N stage (NO) | 2.59(1.28-5.25) | 0.008 | |
| Race (White) | 1.77(1.19-2.64) | 0.005 | |
| Radiation therapy (No) | 2.05(1.39-3.03) | <0.001 | |
| Smorker (Yes) | 2.19(1.42-3.35) | <0.001 | |
| Lymphovascular invasion (Yes) | 2.00(1.16-3.45) | 0.013 | |
| Histologic grade (High Grade) | 1.61(1.12-2.31) | 0.011 | |
| BMI ( <= 25) | 2.29(1.14-4.61) | 0.02 |
0
1
2
3
4
5
C
| Characteristics | HR (95% CI) | PFI | P value |
|---|---|---|---|
| Age( <= 70) | 1.61(1.09-2.36) | 0.016 | |
| Gender (Male) | 1.69(1.19-2.41) | 0.003 | |
| Race (White) | 1.49(1.07-2.06) | 0.018 | |
| Radiation therapy (No) | 1.52(1.11-2.06) | 0.008 | |
| Smorker (Yes) | 1.60(1.13-2.26) | 0.008 | |
| Lymphovascular invasion (Yes) | 1.91(1.19-3.05) | 0.007 | |
| BMI ( <= 25) | 1.98(1.11-3.52) | 0.02 | |
| Subtype (Papillary) | 2.86(1.54-5.32) | 0.001 | |
| Pathologic stage (Stage III) | 0.47(0.25-0.91) | 0.024 |
0
1
2
3
4
5
6
| Characteristic | Low expres- sion of COMP | High expres- p sion of COMP | |
|---|---|---|---|
| n | 207 | 207 | |
| T stage, n (%) | <0.001 | ||
| T1 | 5 (1.3%) | 0 (0%) | |
| T2 | 77 (20.3%) | 42 (11.1%) | |
| T3 | 73 (19.2%) | 123 (32.4%) | |
| T4 | 24 (6.3%) | 36 (9.5%) | |
| N stage, n (%) | 0.004 | ||
| N0 | 129 (34.9%) | 110 (29.7%) | |
| N1 | 20 (5.4%) | 26 (7%) | |
| N2 | 24 (6.5%) | 53 (14.3%) | |
| N3 | 4 (1.1%) | 4 (1.1%) | |
| Pathologic stage, n (%) | <0.001 | ||
| Stage I | 4 (1%) | 0 (0%) | |
| Stage II | 91 (22.1%) | 39 (9.5%) | |
| Stage III | 59 (14.3%) | 83 (20.1%) | |
| Stage IV | 51 (12.4%) | 85 (20.6%) | |
| Radiation therapy, n (%) | 0.028 | ||
| No | 180 (46.4%) | 187 (48.2%) | |
| Yes | 16 (4.1%) | 5 (1.3%) | |
| Race, n (%) | <0.001 | ||
| Asian | 34 (8.6%) | 10 (2.5%) | |
| Black or African American | 6 (1.5%) | 17 (4.3%) | |
| White | 153 (38.5%) | 177 (44.6%) | |
| Histologic grade, n (%) | <0.001 | ||
| High grade | 186 (45.3%) | 204 (49.6%) | |
| Low grade | 19 (4.6%) | 2 (0.5%) | |
| Subtype, n (%) | <0.001 | ||
| Non-papillary | 112 (27.4%) | 163 (39.9%) | |
| Papillary | 94 (23%) | 40 (9.8%) | |
primary therapy outcome, and COMP were prognostic risk factors (Supplementary Table S2).
DEGs between different COMP expression groups and GSEA analysis in BLCA
We performed differential gene analysis in COMP high- and low-expression groups, and there were 3013 DEGs in total, including 2257 up-regulated genes and 756 down- regulated genes (Fig. 11a). Among them, we obtained 491 DEGs, including 440 up-regulated genes and 51 down-reg- ulated genes with the threshold values of |log2 fold-change (FC)|>2.0 and adjusted p value <0.05.
To clarify the potential impact of the expression levels of the selected genes in BLCA, GSEA analysis was performed with the high expression and low-expression group using the MSigDB collection (h.all.v7.2.symbols.gmt). There are 22
data sets satisfying FDR (qvalue) <0.25 and p.adjust <0.05. GSEA revealed that several pathways, such as those related to epithelial mesenchymal transition, allograft rejection, myogenesis, and inflammatory response, were enriched in the high expression group (Fig. 11b-e). These findings sug- gest potential roles for COMP-related genes in the progres- sion, tumor microenvironment, and immune responses of BLCA.
Furthermore, among 491 DEGs, we obtained top 10 hub genes, including LGR4, LGR5, RSPO2, RSPO1, RSPO3, RNF43, ZNRF3, FYN, LYN, and SYK (Fig. 11f). Among them, LGR-related genes, RSPO-related genes, RNF43, and ZNRF3 were all involved in the regulation of WNT pathway, indicating that COMP might affect tumor microenvironment through WNT, which needs to be verified by subsequent experiments.
COMP knockdown inhibited migration and invasion in BLCA cell lines
We used 2 BLCA cell lines, J28 and T24, to further ver- ify the inhibitory effect of COMP. As shown in results, in both J82 and T24 cell lines, the protein level of COMP was significantly decreased after RNA interference (Fig. 12a). Furthermore, COMP knockdown was able to significantly inhibit the migration and invasion of J82 and T24 (Fig. 12b). Moreover, Transwell assays showed that COMP knockdown could significantly reduce the wound healing of J82 and T24 (Fig. 12c).
Discussion
COMP is a glycoprotein expressed in various tissues, and is involved in the assembly and stabilization of the extracel- lular matrix [27]. Currently, high expression of COMP can be seen in a variety of diseases, such as multiple epiphyseal dysplasia and arthritis [28-30]. It has been reported that in patients with arthritis, COMP can be used as a diagnos- tic marker and correlate with disease severity [31]. COMP was also found to be up-regulated in tumors, and correlated with tumor volume increase, metastasis, and rate of cancer recurrence. These tumors include esophageal adenocarci- noma [32], gastric cancer [33], breast cancer [34], papillary thyroid carcinoma [35], colon cancer [17], lung adenocarci- noma [19], and hepatocellular cancer [36]. Taken together, COMP may be a promising candidate target or novel bio- marker for tumor.
Although an association between COMP and cancer has been reported, no studies have shown whether COMP can be a specific biomarker for the diagnosis of cancer. Based on this, we analyzed the relationship between COMP and pan-cancer through CCLE database, TCGA database, and
a
b
15
ns
C
15
ns
12
The expression of COMP Log2 (TPM+1)
10
The expression of COMP Log2 (TPM+1)
The expression of COMP Log2 (TPM+1)
8
10
10
6
4
5
5
2
0
0
0
⇐ 70
>70
Stage I&Stage IIStage III
Stage IV
T1&T2
T3
T4
Age
Pathologic stage
T stage
d
15
ns
e
**
f
10
ns
12
The expression of COMP Log2 (TPM+1)
The expression of COMP Log2 (TPM+1)
10
The expression of COMP Log2 (TPM+1)
8
10
8
6
6
5
4
4
2
2
0
0
0
NO
N1
N2&N3
No
Yes
CR
PD&SD&PR
N stage
Radiation therapy
Primary therapy outcome
g
İ
h
12
**
15
ns
12
**
The expression of COMP Log2 (TPM+1)
10
The expression of COMP Log2 (TPM+1)
10
The expression of COMP Log2 (TPM+1)
8
8
10
6
6
4
4
5
2
2
0
0
0
Non-Papillary
Papillary
No
Yes
Asian
White
Subtype
Lymphovascular invasion
Black or African American Race
GTEx database, and found that COMP was overexpressed in various human cancers. Further analysis revealed that COMP may play an important role in the development and prognosis of various tumors, and may serve as a spe- cific biomarker for cancer. In addition, prognostic analysis showed a significant correlation between the expression of COMP and different molecular subtypes of 12 cancers and different immune subtypes of 16 cancers. Recent studies have shown that COMP could affect the prognosis of liver
fibrosis and hepatocellular carcinoma, and COMP may be a promising therapeutic target and may be a promise thera- peutic target [36]. Moreover, it was reported that the serum levels of COMP may be a potential novel biomarker for the evaluation of the prognosis in breast cancer [34]. Fur- thermore, COMP could lead to liver fibrosis via regulating collagen-I deposition [37]. Our study not only confirms the previous studies, but also expands the function of COMP
| Characteristics | Total (N) | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | ||
| Age ( <= 70 vs.> 70) | 413 | 1.421 (1.063-1.901) | 0.018 | 1.670 (1.121-2.489) | 0.012 |
| Primary therapy outcome (CR vs. PD&SD&PR) | 357 | 5.224 (3.710-7.354) | <0.001 | 4.304 (2.739-6.764) | <0.001 |
| Pathologic stage (Stage I & Stage II vs. Stage IV) | 269 | 3.036 (2.050-4.498) | <0.001 | 1.968 (1.266-3.060) | 0.003 |
a
b HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
C
HALLMARK_ALLOGRAFT_REJECTION
0.8
NES = 2.584
NES = 2.326
0.6
p.adj = 0.004
0.6
p.adj = 0.004
FDR = 0.002
FDR = 0.002
100-
0.4
0.4
-Log,0(P.adj)
Enrichment Score
Enrichment Score
0.2
0.2
0.0
0.0
50
Ranked list metric
Ranked list metric
6
6
NONA9
4
0
-2
-2
-4
-Log2(Fold Change)
0
4
10000
20000
30000
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
d
HALLMARK_MYOGENESIS
e
HALLMARK_INFLAMMATORY_RESPONSE
f
NES =2.309
0.6
NES = 2.034
LGRS
RSPO3
Enrichment Score
p.adj = 0.004
Enrichment Score
0.6
FDR = 0.002
p.adj = 0.004
0.4
0.4
FDR = 0.002
RSPO2
ZNRF3
0.2
0.2
FYN
SYK
0.0
0.0
Ranked list metric
6
Ranked list metric
LGR4
LYN
RNF43
A
6
20NAO
0
-2
-2
RSPO1
10000
20000
30000
Rank in Ordered Dataset
10000
20000
30000
Rank in Ordered Dataset
in other tumor and provides ideas for the future work for searching tumor biomarkers.
Another important finding of this study is that the expres- sion of COMP is highly correlated with TME. COMP expression was positively correlated with the degree of immune cell infiltration in most tumors, especially in HNSC, LIHC, LUSC, and PAAD. Furthermore, we found that upregulation of COMP was positively correlated with CAF in most tumors. CAF, one of the abundant cell species in TME, could reshape the TME through collagen deposi- tion and matrix metalloproteinase secretion [38, 39]. The
available evidence suggested that CAF could interact with infiltrating immune cells to form immunoevasive TME, which in turn promotes tumorigenesis and development [40, 41]. In addition, among the hub genes we found, LGR- related genes and RSPO-related genes encoded receptors for R-spondins and were involved in the canonical WNT signaling pathway. Meanwhile, RNF43 and ZNRF3 played anti-regulatory roles in WNT, which functioned in tumor stem cells and tumor microenvironment [42]. Furthermore, SYK was involved in coupling activated immunoreceptors to downstream signaling events [43]. Therefore, we supposed
a
Relative expression (COMP)
1.5
Relative expression (COMP)
1.5
Control
si-COMP
Control
si-COMP
COMP
1.0
COMP
1.0
J82
0.5
T24
0.5
ß-Actin
ß-Actin
0.0
Control si-COMP
0.0
Control si-COMP
b
J82
800
T24
1000
Migration cell number
Migration cell number
Control
si-COMP
600
Control
si-COMP
800
600
Migration
400
400
200
200
0
Control si-COMP
0
Control si-COMP
800
1000
Control
si-COMP
Invasion cell number
Control
si-COMP
600
Invasion cell number
800
600
Invasion
400
400
200
200
0
Control si-COMP
0
Control si-COMP
C
J82
T24
Control
si-COMP
Control
si-COMP
Relative wound closure (%) (ratio to 0 H)
Relative wound closure (%) (ratio to 0 H)
0 H
1.5
1.5
1.0
1.0
0.5
0.5
24 H
0.0
0.0
Control si-COMP
Control si-COMP
that COMP might mediate similar immune functions in cancer. It might be an immune co-stimulatory molecule, which can further promote tumor proliferation, metastasis, or drug resistance by mediating immunosuppression to form immunoevasive TME. Further work is needed to determine whether COMP performs these functions and its potential molecular mechanisms.
At present, there are many reports on the effect of COMP on tumors, but few on its mechanism. For instance, COMP interacted with TAGLN in colorectal cancer to promote malignant progression [44]. COMP contributed to the severity of the breast cancer by increasing invasiveness and switching metabolism [18]. COMP regulated the interac- tion between Notch3 and Jagged1 in breast cancer [45]. COMP collaborated with CD36 and subsequently played
an essential role in MEK/ERK and PI3K/AKT-mediated HCC progression [17, 46]. To provide a basis for future research on the mechanism of COMP, we conducted GSEA analysis in BLCA. The results showed that there may be the following mechanisms: mesenchymal transition, allograft rejection, myogenesis, and inflammatory response. Among them, mesenchymal transition had also been reported to be a mechanism in COMP, which also corroborates our analy- sis [47]. There were also some reports on the promoting effect of EMT on BLCA [48, 49]. Whether COMP specifi- cally affected BLCA through EMT need to be verified by further research, including the mechanisms of other related pathways.
In this study, we evaluated the diagnostic and prognostic value of COMP using ROC curve and KM survival curve.
The results showed that COMP had certain value for the prognosis of 16 kinds of cancer. In addition, COMP was significantly associated with OS, DSS, and PFI in ACC, BLCA, KIRC, and COADREAD. The results suggested that COMP may be a biomarker or therapeutic target for tumor diagnosis and treatment. To further investigate the possible mechanism of COMP, we performed the GO and KEGG pathway enrichment of 50 COMP-binding pro- teins. The results suggested that COMP may play a role in tumorigenesis through extracellular matrix interaction and PI3K-AKT pathway. It should be emphasized that COMP is critical not only for extracellular matrix, but also for signal transduction both inside and outside the cell.
Since no relevant reports on the effect of COMP on BLCA have been reported so far, we chose BLCA for further research and found that COMP had important implications for BLCA. High expression of COMP was significantly associated with worse OS, DSS, and PFI in various clinical subgroups of BLCA. Univariate and multivariate Cox regression analyses identified pathologi- cal stage, primary therapy outcome, and COMP expres- sion as independent risk factors for OS, DSS, and PFI in BLCA. In addition, GSEA analysis of the co-expressed genes of COMP showed that it functioned in the progres- sion, tumor microenvironment, and immune response of BLCA. Finally, we further verified the inhibitory effect of COMP in BLCA cell lines. The results showed that COMP knockdown could inhibit migration and invasion in BLCA cell lines, which is consistent with our previous analysis results.
Conclusions
COMP may serve as an important target in pan-cancer diagnosis and prognosis as well as provide a theoretical basis for a comprehensive understanding of tumorigenesis and development, especially in the aspect of immune eva- sion of TME.
Supplementary Information The online version contains supplemen- tary material available at https://doi.org/10.1007/s12094-022-02968-8.
Author contributions Bingjie Guo and Yajing Wang contributed equally to the article. Bingjie Guo and Yajing Wang performed the statistical analysis and drew the pictures. Sailong Zhang and Wenyu Liu contributed to the design and writing of the study. All authors approved the final version of the manuscript.
Funding This study was supported by grants from the National Natural Science Foundation of China (81803541) and Shanghai Science and Technology Development Foundation (22140901900).
Data availability All data reported are included and represented in the manuscript.
Declarations
Conflict of interest The authors declare that the research was con- ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent For this type of study formal consent is not required.
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Authors and Affiliations
Bingjie Guo1 . Yajing Wang2 . Wenyu Liu3 . Sailong Zhang4(D
☒ Wenyu Liu lwywinner@hotmail.com
☒ Sailong Zhang sailongzhang@126.com
1 Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
2 Department of Traditional Chinese Medicine, Second Military Medical University/Naval Medical University, Shanghai, China
3 Department of Hepatobiliary and Pancreatic Surgery, Changhai Hospital Affiliated to Naval Medical University, 168 Chang Hai Road, Shanghai 200433, China
4 Department of Pharmacology, Second Military Medical University/Naval Medical University, 325 Guo He Road, Shanghai 200433, China