Research Article A Pan-Cancer Analysis of Clinical Prognosis and Immune Infiltration of CKS1B in Human Tumors
Yan Jia ,1 Quan Tian 0,2 Kaitai Yang (D,1 Yi Liu,1 and Yanfeng Liu 1
1Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
2Department of Reproductive Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
Correspondence should be addressed to Yanfeng Liu; liu_xiaoyu2@163.com
Received 25 August 2021; Accepted 26 October 2021; Published 20 November 2021
Academic Editor: Yun Hak Kim
Copyright @ 2021 Yan Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Although more and more evidence supports CDC28 protein kinase subunit 1B (CKS1B) is involved significantly in the development of human cancers, most of the researches have focused on a single disease, and pan-cancer studies conducted from a holistic perspective of different tumor sources have not been reported yet. Here, for the first time, we investigated the potential oncogenic and prognostic role of CKS1B across 33 tumors based on public databases and further verified it in a small scale by RNA sequencing or quantitative real-time PCR. CKS1B was generally highly expressed in a majority of tumors and had a notable correlation with the prognosis of patients, but its prognostic significance in different tumors was not exactly the same. In addition, CKS1B expression was also closely related to the infiltration of cancer-associated fibroblasts in tumors such as breast invasive carcinoma, kidney chromophobe, lung adenocarcinoma, and tumor-infiltrating lymphocytes in tumors such as glioblastoma multiforme, bladder urothelial carcinoma, and brain lower grade glioma. Moreover, reduced CKS1B methylation was observed in certain tumors, for example, adrenocortical carcinoma. Cell cycle and kinase activity regulation and PI3K-Akt signaling pathway were found to be involved in the functional mechanism of CKS1B. In conclusion, our first pan-cancer analysis of CKS1B contributes to a better overall understanding of CKS1B and may provide a new target for future cancer therapy.
1. Introduction
Recently, the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) released the latest global cancer burden data for 2020, which esti- mated the incidence, mortality, and development trends of 36 cancer types in 185 countries. Based on this statistic, the number of new cancer cases worldwide in 2020 is estimated to be 19.29 million, of which 10.06 million are males and 9.23 million are females. The global cancer death in 2020 is estimated to be 9.96 million, of which 5.53 million are males and 4.43 million are females. On average, about 12,500 peo- ple every day, or about 8.7 people every minute, are diag- nosed with cancer [1]. In addition, according to this data, by 2020, China will have 4.57 million new cancers (23.7% of the world) and 3 million cancer deaths (30.1% of the world). Compared with other countries, China’s cancer inci- dence and mortality rank first in the world [2]. Behind these
figures is the high cost of treatment. According to a survey conducted by the National Cancer Center of China, the aver- age medical expenditure for each cancer patient is RMB 63,000 yuan, while the average annual household income of those surveyed is only RMB 55,000 yuan. As a result, burden of disease is quite heavy [3, 4].
It is well known that the pathogenesis of cancer is very complex. Despite all the difficulties, scientists never give up fighting it. However, limited by various factors, such as small sample size, low statistical power, and poor repeatability, the application of many research results has encountered obsta- cles [5]. With the continuous deepening of genomics research, oncomolecularbiology has gradually entered the pan-cancer stage. Pan-cancer research refers to simulta- neous analysis of multiple different types of tumor genomes to find common characteristics from different sources, so as to help people better understand tumors and provide broad- spectrum targets for clinical diagnosis and treatment [6].
| Analysis Type by Cancer | Cancer Normal | |
|---|---|---|
| Bladder Cancer brain and CNS Cancer | 2 2 | |
| Breast Cancer | 1 1 | |
| Cervical Cancer | 4 11 | |
| Colorectal Cancer | ||
| Esophageal Cancer | 2 1 | |
| Gastric Cancer | ||
| Head and Neck Cancer | 7 I 1 1 A | |
| Kidney Cancer | ||
| Leukemia | ||
| Liver Cancer | ||
| lung Cancer | ||
| Lymphoma | 4 | |
| Melanoma | ||
| Myeloma | 1 | |
| Other Cancer | 1 | |
| Ovarian Cancer | 2 | |
| Pancreatic Cancer | 1 | |
| Prostate Cancer | ||
| Sarcoma | 1 | |
| Significant Unique Analyses | 52 4 | |
| Total Unique Analyses | 452 | |
1 5 10
10 5 1
%
(a)
10
** *
*
**
* ns
The expression of CKS1B Log2 (TPM+1)
8
+
6
L
A
C
4
E
-
2
0
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
4
Normal
(b)
BLCA
BRCA
COAD
HNSC
KIRC
8
6.5
6
The expression of CKS1B Log2 (TPM+1)
7
The expression of CKS1B Log2 (TPM+1)
8
The expression of CKS1B Log2 (TPM+1)
The expression of CKS1B Log, (TPM+1)
7
The expression of CKS1B Log2 (TPM+1)
7
6.0
6
5.5
5
6
6
5
5
5.0
5
4
4
4
4.5
4
2
4.0
3
3
3
3
3.5
2
T
T
T
T
T
T
T
T
2
T
T
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
LIHC
LUAD
LUSC
STAD
UCEC
7
9
7
6.5
The expression of CKS1B Log, (TPM+1)
The expression of CKS1B Log, (TPM+1)
6
8
The expression of CKS1B Log2 (TPM+1)
7
The expression of CKS1B Log, (TPM+1)
The expression of CKS1B Log2 (TPM+1)
6
5.5
5
7
6
5
6
4.5
4
5
5
4
3
4
4
3
3.5
2
3
3
T
T
T
T
T
T
2
T
T
2.5
T
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
T Tumor
(c)
Protein expression of CKS1B in Breast cancer
Protein expression of CKS1B in Colon cancer
Protein expression of CKS1B in Lung adecocarcinoma
Protein expression of CKS1B in Ovarian cancer
Protein expression of CKS1B in Clear cell RCC
Protein expression of CKS1B in UCEC
2
2
3
2
3
3
1
⁎
1
2
2
2
Z-values
Z-values
0
Z-values
1
1
Z-values
1
0
1
-1
0
Z-values
0
0
Z-values
-2
-1
-1
0
-1
-2
-3
-2
-1
-2
-1
-4
-3
-3
-2
-3
=
-5
-4
=
=
-2
-4
-3
-
=
=
=
1
=
=
OPTAC samples
OPTAC samples
OPTAC samples
OPTAC samples
OPTAC samples
OPTAC samples
(d)
Liver HPA030762 Male, age 55 Patient id: 2429
Stomach HPA030762 Male, age 72 Patient id: 2583
Ovary
HPA030762 Male, age 33
Patient id: 2159
Liver cancer HPA030762 Male, age 57
Stomach cancer HPA030762 Male, age 59
Ovarian cancer HPA030762 Male, age 59
Patient id: 3954
Patient id: 2473
Patient id: 2473
(e)
ACC
F value = 3.41
Pr (> F) = 0.0221
HNSC
F value = 5.92
7
8
10
KICH
F value = 8.26
Pr (> F) = 0.000563
Pr ( > F) = 0.000105
9
6
6
8
5
7
4
4
6
3
2
5
2
4
1
Stage I Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
8
KIRP
F value = 8.02
11
LUAD
F value = 5.06
8
PAAD
F value = 2.85
Pr ( > F) = 3.89e-05
Pr ( > F) = 0.00185
Pr (> F) = 0.0389
7
10
6
9
7
6
5
8
7
5
4
6
4
3
5
3
Stage I Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
(f)
The Cancer Genome Atlas (TCGA) is a tumor genome pro- ject launched in 2006 by the National Cancer Institute and the National Human Genome Institute. It aims to use high-throughput genome sequencing, combined with multi- dimensional data integration analysis, draw a map of tumor genome variation and gene expression, elucidate the mecha- nism of tumor occurrence and development, adjust diagno- sis/classification criteria on this basis, and outline new cancer prevention strategy. At present, TCGA already con- tains information such as sequencing results, transcriptome analysis, copy number variation, DNA methylation, and sin- gle nucleotide variation, covering 33 tumor types [7]. ONCOMINE is one of the largest oncogene microarray databases and comprehensive data mining platforms, which integrates RNA and DNA sequencing data from GEO, TCGA, and published literature. Up to now, the database contains a total of 715 gene expression datasets and 86,733 human tumor/normal tissue samples and is still being updated [8]. The functional genomics data sets of different tumors contained in various public databases provide conve- nient tools for pan-cancer research.
CDC28 protein kinase subunit 1B (CKS1B) is an indis- pensable regulatory unit of SCFSkp2 ubiquitin-linked enzyme complex, which promotes the binding of SCF to cyclin inhibitor P27 Kipl and eventually degrades P27 Kip1, leading to the cell transition from G1 phase to S phase [9, 10]. Beyond that, CKS1B also participates in the degrada- tion of p57, p21, p130, CDT-1, RAG2, h-ORC, and UBP4, suggesting CKS1B is not only involved in cell cycle regula- tion but also in other molecular events such as transcription, DNA damage repair, cell proliferation and differentiation, cell senescence and apoptosis, and protein secretion and
transportation [11]. In recent years, an increasing number of domestic and foreign scholars have discovered that CKS1B is closely related to tumors. For example, in prostate cancer, gastric cancer, lung cancer, multiple myeloma, and ovarian cancer, it was observed to be significantly upregu- lated [12-14]. Besides, in colon cancer and breast cancer, CKS1B was found to be negatively correlated with prognosis [15, 16]. However, there is still no evidence of pan-cancer researches.
In this study, TCGA, ONCOMINE, and other databases were used for the first time to conduct a pan-cancer analysis of CKS1B. At the same time, we investigated the potential mechanisms of CKS1B in pathogenesis and clinical progno- sis of different cancers in terms of gene expression, gene alteration, patient survival, DNA methylation, immune infil- tration, and pathway enrichment.
2. Materials and Methods
2.1. Gene Expression Analysis. The mRNA expression of CKS1B in different tumor types was analyzed in ONCO- MINE database, under the settings of p value cutoff = 0.001 and fold change cutoff = 1.5. The protein expression of CKS1B in paired samples was explored in UALCAN por- tal. CKS1B expression difference between tumor and adja- cent normal tissues was analyzed in UCSC XENA platform. Available datasets for six tumors, namely, breast cancer, colon cancer, lung adenocarcinoma, ovarian cancer, clear cell renal cell carcinoma, and uterine corpus endome- trial carcinoma, were finally selected. The distribution and cellular localization of CKS1B was observed by immunohis- tochemistry images using Human Protein Atlas (THPA).
Overall survival
CKS1B
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
OV
LUAD
LUSC
MESO
PAAD
PCPG
PRAD
PEAD
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
Overall survival
Overall survival
Overall survival
Overall survival
Overall survival
Log10 (HR)
1.0
1.0
1.0
1.0
1.0
Percent survival
0.8
Logrank p = 0.029
n (high) = 141
Percent survival
0.8
Logrank p = 0.0002
n (high) = 257
Percent survival
0.8
Logrank p = 4.4e-05
Percent survival
0.8
Logrank p = 0.00099
Percent survival
0.8
Logrank p = 0.019
0.6
n (low) = 141
# (low) = 257
n (high) = 239
n (low) = 239
n (high) = 89
n (low) = 89
n (high) = 229
n (low) = 229
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0
0.2
0.2
0.2
0.2
0.2
0.0
KIRP
0.0
LGG
0.0
LGG
0.0
PAAD
0.0
SKCM
-0.6
0
50
100
150
200
0
50
100
150
200
0
50
100
150
250
200
0
20
40
60
80
0
100
200
300
Months
Months
Months
Months
Months
(a)
Disease free survival
CKS1B
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
OV
LUAD
LUSC
MESO
PAAD
PCPG
PRAD
PEAD
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
Disease free survival
Disease free survival
Disease free survival
Disease free survival
Disease free survival
Log10 (HR)
1.0
1.0
1.0
1.0
1.0
Percent survival
0.8
Logrank p = 0.059
Logrank p = 0.008
Logrank p = 0.015
n (high) = 141
Percent survival
0.8
n (high) = 257
Percent survival
0.8
n (high) = 239
Percent survival
0.8
Logrank p = 0.0015
Percent survival
0.8
Logrank p = 0.028
1.5
n (low) = 14
n (low) = 257
n (low) = 239
n (high) = 89
n (low) = 89
n (high) = 229
n (low) = 229
0.6
0.6
0.6
0.6
0.6
1.0
0.4
0.4
0.4
0.4
0.4
0.5
0.2
0.2
0.2
0.2
0.2
0
0.0
KIRP
0.0
LGG
0.0
LUAD
0.0
PAAD
0.0
SKCM
-0.5
0
50
100
150
200
0
50
100
150
0
50
100
150
200
250
0
20
40
60
80
0
100
200
300
Months
Months
Months
Months
Months
(b)
0
2 4
6
8
0
5
10
15
(c)
1.0
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
CKS1B
0.2
CKS1B
0.2
CKS1B
0.2
CKS1B
0.2
CKS1B
LGG
AUC: 0.946
Cl: 0.936-0.956
LIHC
AUC: 0.937
Cl: 0.916-0.957
LUAD
AUC: 0.955
Cl: 0.943-0.968
AUC: 0.988
0.0
0.0
0.0
0.0
PAAD
Cl: 0.978-0.999
0.0
STAD
AUC: 0.973
Cl: 0.962-0.984
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
0.0
0.2
0.4
0.6
0.8
1.0
1-specificity (FPR)
1-specificity (FPR)
1-specificity (FPR)
1-specificity (FPR)
1-specificity (FPR)
(d)
| Characteristics | Total (N) HR | (95% CI) Multivariate analysis (OS) | P value Multivariate analysis | HR (95% CI) Multivariate analysis (PFI) | P value Multivariate analysis | |
|---|---|---|---|---|---|---|
| Pathologic stage (Stage III&Stage IV vs, Stage I&Stage II) | 77 | 0.763 (0.089-6.513) | 0.805 | 3.063 (0.609-15.396) | 0.174 | |
| N stage (N1 vs.N0) | 77 | 1.037 (0.311-3.453) | 0.953 | |||
| M stage (M1 vs.M0) | 77 | 1.687 (0.635-4.483) | 0.294 | 1.423 (0.577-3.509) | 0.443 | |
| Gender (Male vs. Female) | 79 | |||||
| Age (>50 vs. < = 50) | 79 | |||||
| Weiss-Venous invasion (Present vs.Absent) | 70 | 0.913 (0.313-2.660) | 0.867 | 1.196 (0.516-2.772) | 0.677 | |
| CKS1B (High vs. Low) | 79 | 2.909 (1.094-7.733) | 0.032 | 4.497 (1.830-11.056) | 0.0001 | |
FIGURE 2: Relationship between CKS1B and survival prognosis. (a) Overall survival and (b) disease-free survival of different tumors based on CKS1B expression level (GEPIA2). (c) Forest plot of multivariate Cox regression analysis of ACC patients. (d) Predictive value of CKS1B expression for diagnosis in LGG, LIHC, LUAD, PAAD, and STAD patients.
The violin plots of CKS1B expression in different patholog- ical stages (stage I-IV) of TCGA tumors were obtained by “Pathological Stage Plot” module of GEPIA2.
2.2. Survival Prognosis Analysis. The “Survival Map” and “Survival Analysis” module of GEPIA2 were used to make OS (overall survival) and DFS (disease-free survival) analysis diagrams of CKS1B across all TCGA tumors. The log-rank test was used for hypothesis testing, and the threshold was
set as a Cox p value less than 0.05. R software (version 3.25.0) with the “forest plot” package was utilized to summa- rize and visualize the survival analysis from PrognoScan.
2.3. Bone Marrow Samples and RNA Sequencing. Total RNA was extracted from bone marrow mononuclear cells of acute myeloid leukemia patients or hematopoietic stem cell trans- plantation donors using Trizol reagent (Ambion, Inc., Carls- bad, CA, USA). Samples were analyzed and quality
| RNU6-33 | SNORD27 | 6.00 | ||||
|---|---|---|---|---|---|---|
| SNORD98 | MT1G | |||||
| SNORD20 | VPREB3 | |||||
| MIR27A | GIMAP5 | |||||
| MIR186 | BLK | |||||
| CBWD3 | XCL2 | |||||
| SNORA44 | APOA2 | |||||
| LOC399753 | TMEM110-MUSTN1 | |||||
| SNORA74 | HAMP | 5.00 | ||||
| SNORA1 | MEF2BNB-MEF2B | |||||
| HOXB5 | LOC731223 | |||||
| RNU105A | NPPC | |||||
| NKX2-3 | MLNR | |||||
| SCARNA7 | OR6K3 | |||||
| NSFP1 | OR2T33 | |||||
| IRX3 | TMEM191C | |||||
| ATP6V1G2-DDX39B | REN | 4.00 | ||||
| SCARNA6 | RNASEK-C17orf49 | |||||
| EDA2R | ZFP91-CNTF | |||||
| SRGAP2D | BEX5 | |||||
| CYP2C8 | THEM5 | |||||
| LOC100630923 | S100A3 | |||||
| HIST1H2AK | SFRP1 | |||||
| IRX6 | LCN6 | |||||
| LOC100288842 | ZNF20 | |||||
| ZNF625-ZNF20 | PTCRA | 3.00 | ||||
| PLGLB2 | OPN1SW | |||||
| LOC440895 | SUMO1P3 | |||||
| FONG | CCL7 | |||||
| CLEC2L | LINC00656 | |||||
| GPR89B | ULBP2 | |||||
| KLHL23 | FFAR1 | |||||
| GIMAP1-GIMAP5 | 4-Sep | |||||
| RPS10-NUDT3 | KIR2DL 1 | 2.00 | ||||
| RFPL4A | C1QTNF3-AMACR | |||||
| PI15 | ENTPD2 | |||||
| LOC116437 | SYS1-DBNDD2 | |||||
| RXFP1 | MGP | |||||
| PRR4 | CCIN | |||||
| AGR2 | TGFBR3L | |||||
| TMSB15B | SLC7A3 | |||||
| DFNB59 | FAM226A | 1.00 | ||||
| SYCE1 | C15orf26 | |||||
| OR2T8 | LOC284100 | |||||
| UBE2Q2P2 | ARHGAP8 | |||||
| CNTF | KIR3DS1 | |||||
| SPP1 | FOXD4L1 | |||||
| MMP7 | KRTAP16-1 | |||||
| MDS2 | CYP46A1 | |||||
| C4B 2 | FAM225A | 0.00 |
LAML-CR
LAML-NR
LAML-CR
LAML-NR
(a)
The relative expression of CKS1B
60
50
8
**
40
Relative expression of
6
30
CKS1B
4
20
2
10
0
0
LAML-CR
LAML-NR
Good DFS group
Bad DFS group
(b)
(c)
Cancer associated fibroblast_EPIC Cancer associated fibroblast_MCPCOUNTER
Cancer associated fibroblast_XCELL
Cancer associated fibroblast_TIDE
Partial_Cor
1
ACC (n = 79)
BLCA (n = 408)
BRCA (n = 1100)
BRCA-Basal (n = 191)
BRCA-Her2 (n= 82)
BRCA-LumA (n = 568)
BRCA-LumB (n = 568)
CESC (n = 306)
CHOL (n=36)
COAD (n = 458)
DLBC (n = 48)
ESCA (n = 185)
GBM (n = 153)
HNSC (n = 522)
HNSC-HPV-(n= 422)
HNSC-HPV+ (n=98)
KICH (n = 66)
KIRC (n = 533)
KIRP (n = 290)
0
LGG (n = 516)
LIHC (n = 371)
LUAD (n = 515)
LUSC (n = 501)
MESO (n = 87)
OV (n = 303)
PAAD (n = 179)
PCPG (n = 181)
PRAD (n = 498)
READ (n = 166)
SARC (n = 260)
SKCM (n = 471)
SKCM-Metastasis (n = 368)
SKCM-Primary (n = 103)
STAD (n = 415)
TGCT (n= 150)
THCA (n=509)
THYM (n = 120)
UCEC (n = 545)
UCS (n = 57)
-1
UVM (n = 80)
☒
p>0.05
☒ p … 0.05
(a)
CKS1B expression level (log2 TPM)
10.0
Purity
Cancer associated fibroblast_EPIC
CKS1B expression level (log2 TPM)
Purity
Cancer associated fibroblast_MCPCOUNT
ACC
Rho = 0.247
Rho = 0.319
P = 5.99e
ACC
Rho .= 0.247.
Rho = 0.362
P .= 3,40e-02
P == 3.40e-02.
P = 1.62e-
7.5
7.5
ACC
ACC
5.0
5.0
2.5
2.5
0.2
0.4
0.6
0.8
1.0 0.0
0.1
0.2
0.3
0.2
0.4
0.6
0.8
1.0 0
5000
10000
Purity
Infiltration level
Purity
Infiltration level
CKS1B expression level (log2 TPM)
Purity
Cancer associated fibroblast_EPIC
Purity
Cancer associated fibroblast_XCELL
10
BRAC
Rho = 0.153
P = 1.37e-02,
Rho = 0.311
CKS1B expression level (log2 TPM)
6
KICH
Rho = 0.125
P = 1.15e-23
P = $.46e-03
Rho = 0.359
P = 3.34e-03
5
8
BRAC
4
KICH
6
3
2
4
1
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
0.2
0.4
0.6
0.8
1.0
-0.1
0.0
0.1
0.2
Purity
Infiltration level
Purity
Infiltration level
CKS1B expression level (log2 TPM)
Purity
Cancer associated fibroblast_EPIC
Rho = 0.153
Rho = 0.319
CKS1B expression level (log2 TPM)
10
Purity
Cancer associated fibroblast_XCELL
7
KIRP.
P = 1.37e-02.
LUAD
Rho = 0.125
P .= 1.88e-07
P = 5.46e-03
Rho = 0.383
P. = 1.30e-19
6
8
5
KIRP
LUAD
4
6
3
0.25
0.50
0.75
1.00 0
2500
5000
7500
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
Purity
Infiltration level
Purity
Infiltration level
CKS1B expression level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
Purity
Cancer associated fibroblast_TIDE
ACC
Rho = 0.143
Rho = 0.359
CKS1B expression level (log2 TPM)
STAD
P = 5.10e-03
P .= 5.37e-13
THYM
Rho = 0.09
P = 3.38e-01
Rho = 0.36
P = 7.75e-13
8
8
7
STAD
7
THYM
6
6
5
5
4
4
0.25
0.50
0.75
1.00
-0.2
0.0
0.2
0.4
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
Purity
Infiltration level
Purity
Infiltration level
(b)
FIGURE 4: Continued.
Act CD8
Tcm CD8
Tem CD8
1
Act CD4
Tcm CD4
Tem CD4
Tfh
Tgd
Th1
Th17
Th2
Treg
Act B
Imm B
Mem B
NK
CD56bright
CD56dim
MDSC
NKT
Act DC
pDC
iDC
Macrophage
Eoisnophil
Mast
Monocyte
Neutrophil
-1
ACC
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ _
SARC
SKCM
STAD TGCT
THCA
UCEC
UCS
UVM
(c)
ADORA2A
BTLA
CD160
1
CD244
CD274
CD96
CSF1R
CTLA4
HAVCR2
IDO1
IL10
IL10RB
KDR
KIR2DL1
KIR2DL3
LAG3
LGALS9
PDCD1
PDCD1LG2
PVRL2
TGFB1
TGFBR1
TIGIT
-1
VTCN1
ACC
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
UCEC
UCS
UVM
(d)
C10orf54
CD27
CD276
1
CD28
CD40
CD40LG
CD48
CD70
CD80
CD86
CXCL12
CXCR4
ENTPD1
HHLA2
ICOS
ICOSLG
IL2RA
IL6
IL6R
KLRC1
KLRK1
LTA
MICB
NT5E
PVR
RAET1E
THEM173
TMIGD2
TNFRSF13B
TNFRSF13C
TNFRSF14
TNFRSF17
TNFRSF18
TNFRSF25
TNFRSF4
TNFRSF8
TNFRSF9
TNFSF13
TNFSF138
TNFSF14
TNFSF15
TNFSF18
TNFSF4
-1
TNFSF9
ULBP1
ACC
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
UCEC
UCS
UVM
(e)
ACC (79 samples)
GBM (166 samples)
BLCA (408 samples)
0.50
Th17_abundance
ACl_CD4_abundance
ACl_CD4_abundance
0.25
0.4
0.4
0.00
0.0
0.0
-0.25
-0.4
-0.4
-0.50
3
4
5
6
7
4
6
8
4
6
8
CKS1B_exp spearman correlation test: rho = - 0.455, p = 3.1e-05 LGG (530 samples)
CKS1B_exp spearman correlation test: rho = 0.504, p < 2.2e-16
CKS1B_exp spearman correlation test: rho = 0.462, p < 2.2e-16
SKCM (472 samples)
0.6
0.6
UCEC (546 samples)
Tgd_abundance
0.3
Th2_abundance
0.3
Th2_abundance
0.3
0.0
0.0
0.0
-0.3
-0.3
-0.3
-0.6
-0.6
2
3
4
5
6
7
3
4
5
6
7
2
4
6
8
CKS1B_exp spearman correlation test: rho = 0.489, p < 2.2e-16
CKS1B_exp spearman correlation test: rho = 0.234, p = 3.01e-07
CKS1B_exp spearman correlation test: rho = 0.401, p < 2.2e-16
(f)
controlled by BGI Gene Technology Company (China). After passing this test, cDNA library was constructed according to the TruSeq RNA Sample Preparation Kit (Illu- mina, San Diego, CA, USA). Each library was sequenced using single-reads on a HiSeq2000/1000 (Illumina). Cuf- flinks were used to measure gene expression levels in RPKM (reads per kilobase per million mapped reads).
2.4. Quantitative Real-Time PCR (RT-qPCR). Total RNA extraction of brain tissues from GEM patients and quantita- tive real-time PCR reaction was performed using Fast 200 Kit (Feijie Biotechnology Co., Ltd., Shanghai, China) and One Step TB Green PrimeScript RT-PCR kit (MBI Fermen- tas, St. Leon-Roth, Germany), respectively. The specific operation steps were carried out in accordance with instruc- tions. Relative expression levels of transcription products were normalized to GAPDH. The primer sequences were used as CKS1B-F: 5’-GGACAAATACGACGACGAGGA- 3’ and CKS1B-R: 5’-CTGACTCTGCTGAACGCCAAG-3’ and GAPDH-F: 5’-CACCCTGTTGCTGTAGCCAAA-3’ and GAPDH-R: 5’-CACCCTGTTGCTGTAGCCAAA-3’. Conditions for PCR were 30 cycles of denaturation (94℃, 1 min), annealing (60℃, 45 s), extension (72℃, 30 s), and one cycle of final extension (72℃, 10 min).
2.5. Immune Infiltration Analysis. The interactive online databases TIMER and GEPIA2 were used to study the rela- tionship between CKS1B expression and abundance of
immune infiltration in tumors. B cells, CD4+ T cells, CD8+ T cells, and cancer-associated fibroblasts (CAFs) were selected as research parameters. XCELL, MCPCOUNTER, TIDE, and EPIC algorithms were applied for immune infil- tration estimations. p values and partial correlation (cor) values were obtained via the purity-adjusted Spearman’s rank correlation test. Data were visualized as heat maps and scatter plots. In addition, Pearson correlation analysis was performed to evaluate the level of tumor-infiltrating lymphocytes (TILs). To further investigate the association between CKS1B and immune cell movement and regulation, we also assessed chemokines/chemokine receptors and immunosuppressive factor/immunoactivating factor profiles based on “Chemokine” and “Immunomodulator” modules of TISIDB web portal.
2.6. Gene Enrichment Analysis. The protein name “CKS1B” and organism “Homo sapiens” were entered into STRING website. The specific parameters were set as follows: network type (“full network”), meaning of network edges (“evi- dence”), active interaction sources (“experiments”), mini- mum required interaction score (“low confidence (0.40)”), maximum number of interactors to show (“no more than 50 interactors” in 1st shell). As a result, the available CKS1B binding proteins were identified. Subsequently, GeneMA- NIA was applied to do a protein interaction network. Next, Jvenn was used for cross-analysis to screen out common proteins and represented them as Venn diagram. In
CDC16
OCHAZ
OCHAT
BLOMTRAT
COKTS
BUSP1
CDK1
OCH33
OCHE
CKS2
OCH02
OBES
COKS4
ERH
CDK2
GALKS
OCNET
GALKY
COK16
C2AFZ
CUL1
LONPI
CONEZ
U
LONPZ
COKIT
@CNB2
SKP1
OKSID
COCE
DWARS
COKY
FZR
CKS18
SKP2
COK18
PRAHYTI
022
COC20
@CNB1
CDK3
EN1 -COK3
CDC23
COC27
TRIMIS
CDH1
FOXMI
TIK
COK2
G
CONDI
-
ACATI
RBL2
CONNIE
COKNIA
COKS
COTI
GUNN
GOKNIA
CCNA2
NEDDB
(a)
(b)
1
2
CCN12
CCNB1
CCNTG
CCK1
CCK2
CCK3
CCKNIA
CKB2
SKP1
SKP2
Spearman_cor
1
ACC (n = 79)
BLCA (n = 408)
BACA (n = 1100)
31
10
10
BACA-Base1 (n = 191)
BACA-Har2 (n = 82)
BACA-LamA (n = 568)
BACA-LamB (n = 219)
CDSC (n = 306)
CHOL (n=36)
COAD (n = 458)
DOBC (n = 43)
E8CA (n = 185)
GBM (n = 158)
HNSC (n = 522)
HNSC-HPV (n = 422)
HNSC-HPV (n=98)
KICH (n = 66)
KIAC (n = 533)
KIAP (n = 290)
LGG (n = 516)
LIHC (n = 371)
- 0
LUAD (n = 515)
LUSC (n = 510)
ME8O (n = 87)
CV (n = 303)
PAAD (n = 179)
PCPG (n = 181)
ARAD (n = 498)
ARAD (n = 166)
SAAC (n = 260)
SKCM (n = 471)
SKCM metasatic (n = 368)
SKCM-primary (n = 103)
BTAD (n = 415)
TGCT (n= 150)
THCA (n=509)
THYM (n = 120)
UCEC (n = 545)
UC8 (n = 57)
UVM (n = 80)
-1
☒ P> 0.05
☒ PO … 05
(c)
(d)
Protein kinase regulator activity
Cyclin-dependent protein serine/threonine kinase activity
Cyclin-dependent protein kinase activity
Cyclin-dependent protein serine/threonine kinase regulator activity
Transferase complex, transferring phosphorus- containing groups
Protein kinase complex
Serine/threonine protein kinase complex
Cyclin-dependent protein kinase holoenzyme complex
Cell cycle G1/S phase transition
Regulation of cyclin- dependend protein serine/ threonine kinase activity
Regulation of cell cycle phase transition
Regulation of mitotic cell cycle phase transition
0
5
10
15
20
25
-Log10 (p.adjust)
☐ BP
☐ CC
☐ MF
(e)
P.adjust
0.0020
Cell cycle
Cellular senescence
Oocyte meiosis
Human T-cell leukemia
virus 1 infection
Viral carcinogenesis
Progessterone-mediated
Oocyte maturation
Human papillomavirus
infection
0.0015
Epstein-Barr virus
infection
FoxO signaling pathway
Ubiquitin mediated
proteolysis
Small cell lung cancer
P53 Signaling pathway
P13K-Akt signaling
pathway
Gastric cancer
0.0010
Human immunodeficiency
virus 1 infection
Hepatitis B
Cushing syndrome
Prostate cancer
Measles
Thyroid cancer
0.0005
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Generatio
☐ 3
☐
14
☐ 24
(f)
7
8
p-value = 0
R = 0.67
p-value = 0
8
p-value = 0
Log2 (CDK1 TPM)
CDK1
Log2 (CCNA2 TPM)
6
R = 0.64
CCNA2
Log2 (CCNB1 TPM)
R = 0.68
CCNB1
6
5
6
4
4
3
4
2
2
2
1
0
0
0
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
Log2 (CKS1B TPM)
Log2 (CKS1B TPM)
Log2 (CKS1B TPM)
10
8
p-value = 0
R = 0.69
p-value = 0
R = 0.64
p-value = 0
Log2 (CCNB2 TPM)
CCNB2
CKS2
R = 0.48
Log2 (CKS2 TPM)
8
Log2 (SKP2 TPM)
6
SKP2
6
6
4
4
4
2
2
2
0
0
0
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
Log2 (CKS1B TPM)
Log2 (CKS1B TPM)
Log2 (CKS1B TPM)
(g)
KEGG_DRUG_METABOLISM_CYTOCHROME_P450
REACTOME_GLUCURONIDATION
REACTOME_GLUCURONIDATION
Enrichment score
0.0
Enrichment score
0.0
Enrichment score
0.0
NES == 2.951
-0.2
-0.2
-0.2
p.adj =. 0.018
FDR =0.012
-0.4
-0.4
-0.4
NES = - 2.414
NES = - 2.225
-0.6
p.adj = 0.018
-0.6
p.adj = 0.018
-0.6
FDR = 0.012
-0.8
FDR = 0.012
-0.8
10000
20000
30000
10000
20000
30000
10000
20000
30000
Rank in ordered dataset REACTOME_G2_M_CHECKPOINTS
Rank in ordered dataset
Rank in ordered dataset
REACTOME MITOTIC SPINDLE CHECKPOINT
REACTOME MITOTIC SPINDLE CHECKPOINT
Enrichment score
0.6
NES=+2.807
Enrichment score
0.6
NES == 2.850
Enrichment score
0.0
p.adj = 0.018
p.adj = 0.018
0.4
FDR = 0.012
0.4
FDR = 0.012
-0.2
0.2
0.2
-0.6
NES =- 2.222
p.adj =0.018
0.0
0.0
FDR = 0.012
10000
20000
30000
10000
20000
30000
10000
20000
30000
Rank in ordered dataset
Rank in ordered dataset
Rank in ordered dataset
(h)
combination with KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology) database, and “ggplot2” R package, the enrichment pathway was obtained and visual- ized. Moreover, the heat maps of selected genes were pro- vided by “Gene_Corr” module of TIMER2, which included
cor and p values from the purity adjusted Spearman rank correlation test. Finally, the “Correlation Analysis” module of GEPIA2 was used to perform a pairwise gene Pearson correlation analysis of CKS1B and selected genes, and the log2 TPM was applied for dot plots. GSEA (gene set
| Gene set name | NES | p value | FDR q-val |
|---|---|---|---|
| KEGG_DRUG_METABOLISM_CYTOCHROME_P450 | -2.414 | 0.002 | 0.012 |
| KEGG_RETINOL_METABOLISM | -2.368 | 0.002 | 0.012 |
| KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 | -2.317 | 0.002 | 0.012 |
| KEGG_STEROID_HORMONE_BIOSYNTHESIS | -2.289 | 0.002 | 0.012 |
| KEGG_ASTHMA | -2.267 | 0.002 | 0.012 |
| KEGG_ASCORBATE_AND_ALDARATE_METABOLISM | -2.252 | 0.002 | 0.012 |
| REACTOME_GLUCURONIDATION | -2.225 | 0.002 | 0.012 |
| REACTOME_PD_1_SIGNALING | -2.222 | 0.002 | 0.012 |
| KEGG_ALLOGRAFT_REJECTION | -2.209 | 0.002 | 0.012 |
| WP_PREGNANE_X_RECEPTOR_PATHWAY | -2.198 | 0.002 | 0.012 |
| REACTOME_G2_M_CHECKPOINTS | 2.807 | 0.003 | 0.012 |
| REACTOME_MITOTIC_G1_PHASE_AND_G1_S_TRANSITION | 2.819 | 0.003 | 0.012 |
| REACTOME_MITOTIC_SPINDLE_CHECKPOINT | 2.85 | 0.003 | 0.012 |
| REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION | 2.857 | 0.003 | 0.012 |
| REACTOME_M_PHASE | 2.901 | 0.003 | 0.012 |
| REACTOME_MITOTIC_PROMETAPHASE | 2.914 | 0.003 | 0.012 |
| WP_CELL_CYCLE | 2.951 | 0.003 | 0.012 |
| KEGG_CELL_CYCLE | 2.976 | 0.003 | 0.012 |
| WP_RETINOBLASTOMA_GENE_IN_CANCER | 2.979 | 0.003 | 0.012 |
| REACTOME_CELL_CYCLE_CHECKPOINTS | 3.11 | 0.003 | 0.012 |
enrichment analysis) was performed using the clusterProfi- ler package in R.|ES | >1, p < 0.05, and FDR < 0.25 were con- sidered statistically significant.
2.7. Genetic Alteration Analysis. The “TCGA Pan Cancer Atlas Studies” in “Quickselect” section of cBioPortal web was logged, and keyword “CKS1B” was entered to check the gene variation characteristics. The results of change fre- quency, mutation type, and CNA (copy number change) for all TCGA tumors were observed in “Cancer Type Summary” module. The mutation site information of CKS1B can be dis- played in the schematic map of protein structure or 3D structure via the “Mutations” module. Kaplan-Meier plots with log-rank p values were generated using the “Compari- son” module to obtain data on the overall, disease-free, and progression-free survival differences in tumor cases that with and without CKS1B gene alterations.
2.8. Methylation Analysis. The methylation status of CKS1B in tumor and adjacent normal tissues was assessed by Disea- seMeth database (version 2.0). The relationship between CKS1B expression and its DNA methylation was investi- gated using MEXPRESS database.
3. Results
3.1. CKS1B Is Highly Expressed in Most Types of Human Cancers and Related to Disease Progression. We first ana- lyzed basal expression levels of CKS1B in different blood cells, tumor cell lines, and tumor tissues using Consensus database. As shown in Figure S1A-C, CKS1B was expressed in almost all detected cells and tissues,
suggesting it had low cell and tissue type specificity. Then, based on ONCOMINE and UCSC XENA data platform, we found a total of 31 tumors with normal (or highly limited normal) control, of which 29 had statistically differences in the expression level of CKS1B (p<0.05). More specifically, CKS1B was remarkably higher in all 26 tumors than normal tissues, except KICH (kidney chromophobe), LAML (acute myeloid leukemia), and PRAD (prostate adenocarcinoma) (Figures 1(a) and 1(b)). CKS1B expression in paired samples was shown in Figure 1(c) and Figure S1D. Meanwhile, through UALCAN and THPA websites, we found CKS1B protein in BRCA (breast invasive carcinoma), COAD (colon adenocarcinoma), LUAD (lung adenocarcinoma), OA (ovarian cancer), RIRC (kidney renal clear cell carcinoma), UCEC (uterine corpus endometrial carcinoma), STAD (Stomach adenocarcinoma), LIHC (liver hepatocellular carcinoma), etc. was also higher than corresponding control groups (Figures 1(d) and 1(e)). In addition, with the help of “Pathological Stage Plot” module of GEPIA2, we observed increased expression of CKS1B in most tumors with disease progression, especially in ACC (adrenocortical carcinoma), KICH, and KIPR (kidney renal papillary cell carcinoma) (Figure 1(f), Figure S1E).
3.2. High Expression of CKS1B Correlates with Tumor Prognosis. Tumor cases were divided into high CKS1B expression group and low CKS1B expression group. The correlation between CKS1B and prognosis of patients with different tumors was studied by GEPIA2. As shown in Figures 2(a) and 2(b), highly expressed CKS1B was linked to poor OS and DFS in KIRP, LGG (brain lower grade
| Structural varient data + + | + | + | + + | + | + | + | + | + | + + | + | + | + | + | + | + | + | + | + + + | |||||
| Mutation data + + | + + + + + + + | + + | + + + | + | + | + | + | + + | + | + | + | + | + | + | + | + | + | + | + + + | ||||
| CNA data | + + | + | + | + | + | + | + | + | + + | + | + | + | + | + | + + + | ||||||||
Alteration frequency
15%
10%
5%
Cholangiocarcinoma
Hepatocellular carcinoma Invasive breast carcinoma
Non-small cell lung cancer
Ovarian epithelial tumor Endometrial carcinoma
Bladder urothelial carcinoma
Pancreatic adenocarcinoma
Sarcoma
Adrenocortical carcinoma
Miscellaneous neuroepithetial tumor
Esophagogastric adenocarcinoma
Pheochromocytoma
Prostate adenocarcinoma
Cervical squamous cell carcinoma
Pleural mesothelioma
Cervical adenocarcinoma
Mature B-cell neoplasms
Melanoma
Thymic epithetial tumor
Esophageal squamous cell carcinoma
Colorectal adenocarcinoma
Head and neck squamous cell carcinoma
Diffuse glioma
Glioblastoma
Renal clear cell carcinoma
Renal non-clear cell carcinoma
Leukemia
Undifferentiated stomach adenocarcinoma
Seminoma
Non-seminomatous germ cell tumor
Well -differentiated thyroid cancer
Ocular melanoma
· Mutation
Structural variant
· Deep deletion
· Amplification
· Multiple alterations
(a)
# CKS1B Mutations
5
Q5H
0
CKS
0
79aa
(b)
(c)
Overall
Disease free
Disease -specific
Progression free
100%
Logrank test p-value 0.0100
100%
Logrank test p-value 0.512e
4
100%
Logrank test p-value 0.0715
100%
-
Logrank test p-value 0.355
90%
90%
90%
90%
80%
80%
80%
80%
70%
70%
70%
70%
60%
60%
60%
60%
50%
50%
50%
50%
40%
40%
40%
40%
30%
30%
30%
30%
20%
20%
20%
20%
10%
ACC
10%
ACC
10%
ACC
10%
ACC
0
0
0
0
0
10
20
30
40
50
60
70
80
90
100
10
120
130
140
150
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
Overall survival (months)
Disease free (months)
Disease -specific (months)
Progression free (months)
Altered group
Unaltered group
(d)
14k
P < 0.0001
CKS1B: mRNA expression, RSEM (based normalized from iluminaHiseq_RNAsecv2)
12k
o
10k
8
8k
6k
8
4k
2k
0
0
8
Deep deletion
Shallow deletion
Diploid
Gain
Amplification
CKS1B: Putative copy-number alterations from GISTIC
CKS1B
Truncating (vus)
not profiled for mutations
Not mutated
Diploid
Gain
· Structural varient
Deep deletion
· Missense (vus)
Shallow deletion
. Amplification
(e)
glioma), LUAD, PAAD (pancreatic adenocarcinoma), and SKCM (skin cutaneous melanoma) (all p < 0.05). Interest- ingly, it was not associated with OS in LAML and LUSC. Moreover, high CKS1B expression was even meant better OS in KIRC (kidney renal clear cell carcinoma) (p = 0.026) and better DFS in GBM (glioblastoma multiforme) (p=0.046) (Figure S2A and S2B). To verify this conclusion, on one hand, we collected bone marrow samples from LAML patients and divided them into
remission (CR) group and nonremission (NR) group according to the degree of bone marrow remission after chemotherapy. By RNA sequencing, CKS1B was not found among the top 50 differentially expressed genes between the two groups. More specific data showed that although CKS1B in NR group was higher than that in CR group (63.5 vs. 57.42), the difference was not statistically significant (p=0.2083) (Figure 3(a)). On the other hand, through retrospective analysis of clinical data, GEM
p-value = 3.218e-03
0.125
Methylation value
0.1
0.075
0.05
0.025
Disease
Normal
Sample groups
(a)
Age at initial pathologic diagnosis Histological type
r =- 0.053
p = 0.088
New tumor event after initial tr …
p = 5.359e-6
Sinusoid invasion
p = 0.867
Weiss score Weiss venous invasion
r = 0.316*
p =0.313
Gender
p = 0.185
Tumor stage simplified
p = 0.026
Sample type
NaN
Os
154973700
CKS1B expression
r = 0.215
r =- 0.358 **
r =- 0.400 ***
AKS1B
r = 0.011
r = 0.330 **
r = 0.328 **
r =- 0.195
r = 0.339 **
r =- 0.045
r =- 0.353 **
r = 0.310 **
cg17833341 cg04915414
r =- 0.374 ***
154977000
r =- 0.333 **
cg10019844
r =- 0.237*
cg17891149
r =- 0.197
r =- 0.237*
cg21786227
154980200
Legend
Histological type
New tumor event initial tr …
Myxoid type
☐ Usual type
Sinusoid invasion
No
☐ Yes
Null
Absent
☐ Present
Null
Weiss venous invasion
Gender
Absent
☐ Present
☐ Null
Tumor stage simplified
Female
☐ Male
CpG dinucleotide
Stage 1
☐ Stage 2
Stage 3 Solid tissue normal
Stage 4
Null
CpG island
Sample type
Primary tumor
☐
Gene
Statistics
p >= 0.05
*p <0.05
** p < 0.01
*** p < 0.001
Transcript
(b)
patients were divided into good prognosis and bad prognosis groups according to DFS, and 30 samples were selected (15 cases in each group). RT-qPCR results showed that CKS1B
mRNA in patients with good DFS was higher than that in patients with bad DFS (p=0.0006) (Figure 3(b)). These data indicated that the prognostic significance of CKS1B
expression level in different tumor types was not completely the same. Besides, we specifically discussed the predictive value of CKS1B for clinical outcomes in subgroups of ACC, and results were shown in Figure 2(c): high expression of CKS1B was an independent risk factor for OS (HR =2.909, p = 0.032) and progression-free interval (PFI) (HR = 4.497, p = 0.001).
In order to evaluate the clinical diagnostic value of CKS1B, we also calculated the area under ROC curve of LGG, LIHC (liver hepatocellular carcinoma), LUAD, PAAD, STAD, BRCA, COAD, ESCA (esophageal carcinoma), LUSC (lung squamous cell carcinoma), OV (ovarian serous cysta- denocarcinoma), READ (rectum adenocarcinoma), KIRC, and GBM, most of which were above 0.9, indicating that CKS1B has high sensitivity and specificity for the diagnosis of these tumors (Figure 2(d) and Figure S2C).
3.3. CKS1B Correlates with Tumor Immune Infiltration. Immune system plays a crucial role in the occurrence, devel- opment, and treatment of tumors [17]. Tumor-infiltrating immune cells are believed to be able to independently pre- dict tumor metastasis and prognosis [18-20]. Considering the upregulation of CKS1B was associated with a variety of tumor progression and prognosis, we speculated CKS1B might be involved in tumor immune response. To confirm this hypothesis, we did a series of comparisons by TIMER and GEPIA2 databases and observed a statistically positive correlation between CKS1B expression and CAFs infiltration in ACC, KICH, and KIRP, but a negative correlation in BRCA, LUAD, LUSC, STAD, and THYM (thymoma) (Figure 4(a)). The scatter plots based on one of XCELL, MCPCOUNTER, TIDE, and EPIC algorithms were shown in Figure 4(b). Moreover, we explored the role of CKS1B in immune regulation by ESTIMATE database. The heat maps about CKS1B and tumor-infiltrating lymphocytes (TILs), immunosuppressive factors, and immunostimulatory factors were presented in Figures 4(c)-4(e), respectively. Figure 4(f) was another scatter plot reflecting CKS1B and certain TILs in specific tumors. For example, CKS1B was negatively correlated with Th17 infiltration in ACC (r = - 0.455, p = 3.1e - 05) and UCEC (r =- 0.401, p =2.2e - 16), but positively correlated with Act_CD4 infiltration in GEM (r = 0.504, p=3.1e- 05) and BLCA (r =0.462, p = 2.2e - 16). Besides, heat maps of CKS1B expression with B lymphocytes, T lymphocytes, chemokines, and chemokine receptors were shown in Figure S3A-D.
3.4. Enrichment Analysis of CKS1B-Related Partners. To fur- ther investigate the mechanism of CKS1B in tumorigenesis, we attempted to screen out the binding protein map target- ing CKS1B by STRING tool (Figure 5(a)) and draw a protein interaction network by GeneMANIA database (Figure 5(b)). A cross-analysis of the above two sets of data revealed that there were 10 common members, namely, CCN2, CCNB1, CCNB2, CDK1, CDK2, CDK3, CDKN1A, CKS2, SKP1, and SKP2 (Figure 5(c)). The expression of these genes in dif- ferent tumors was presented as a heat map (Figure 5(d)). As shown in Figure 5(g), CKS1B was positively associated with CDK1 (r=0.67), CCN2 (r=0.64), CCNB1 (r=0.68),
| Cancer type | Cor | p value | Sig |
|---|---|---|---|
| ACC | 0.451 | <0.001 | |
| BLCA | 0.274 | <0.001 | |
| BRCA | 0.344 | <0.001 | |
| CESC | 0.089 | 0.134 | |
| CHOL | 0.146 | 0.395 | |
| COAD | 0.082 | 0.105 | |
| DLBC | 0.097 | 0.568 | |
| ESCA | -0.087 | 0.277 | |
| GBM | 0.041 | 0.622 | |
| HNSC | 0.207 | <0.001 | |
| KICH | 0.399 | 0.001 | |
| KIRC | 0.123 | 0.025 | ∗ |
| KIRP | 0.086 | 0.155 | |
| LAML | 0.141 | 0.272 | |
| LGG | 0.415 | <0.001 | |
| LIHC | 0.149 | 0.005 | |
| LUAD | 0.483 | <0.001 | |
| LUSC | 0.264 | <0.001 | |
| MESO | 0.355 | 0.001 | |
| OV | 0.233 | <0.001 | |
| PAAD | 0.492 | <0.001 | |
| PCPG | -0.054 | 0.472 | |
| PRAD | 0.133 | 0.003 | |
| READ | 0.207 | 0.017 | ∗ |
| SARC | 0.301 | <0.001 | |
| SKCM | 0.248 | <0.001 | |
| STAD | 0.422 | <0.001 | |
| TGCT | 0.118 | 0.159 | |
| THCA | -0.025 | 0.582 | |
| THYM | -0.433 | <0.001 | |
| UCEC | 0.126 | 0.004 | |
| UCS | 0.047 | 0.729 | |
| UVM | -0.105 | 0.355 |
CCNB2 (r=0.69), CKS2 (r=0.64), and CKP2 (r=0.48) (all p < 0.001). Moreover, the GO data in Figure 5(e) demon- strated that “cell cycle regulation” and “protein kinase activ- ity regulation” were involved in the influence of CKS1B on tumor pathogenesis. KEGG data in Figure 5(f) indicated that most of these selected genes were linked to cell cycle, cell senescence, and viral infection (such as Epstein-Barr virus, HPV virus, and hepatitis B virus). FOXO, P53, and PI3K- Akt were the main participating molecules and signaling pathway.
To specifically evaluate the function of CKS1B-related differentially expressed genes (DEGs), we used GSEA for enrichment analysis. As shown in Table 1 and Figure 5(h),
BRCA ***
BLCA ** ACC ***
UVM
BLCA ** ACC UVM
UCS
BRCA*
UCS
CESC
0.5
UCEC **
CESC
0.4
UCEC ***
CHOL
0.25
THYM ***
CHOL
0.2
THYM
COAD
THCA
COAD ***
THCA ***
0
DLBC
TGCT
DLBC*
TGCT
-0.25
-0.2
ESCA
STAD ***
-0.5
ESCA
STAD ***
-0.4
GBM
SKCM ***
GBM
SKCM
HNSC ***
SARC ***
HNSC ***
SARC ***
KICH ***
READ*
KICH
READ
KIRC*
PRAD **
KIRC
PRAD
KIRP
PCPG
KIRP*
PCPG
LAML
PAAD ***
LAML*
PAAD
LGG ***
OV ***
LIHC **
LGG
OV
LUAD*ĽUSC.MESO **
LIHC*
LUAD LUSC
MESO
(a)
(b)
CKS1B-related DEGs were mainly enriched in drug metabo- lism related clusters, such as cytochrome p450 (NES =- 2.414, p.adj =0.018, and FDR = 0.012) (Figure 5(h) a) and glucuronidation (NES =- 2.225, p.adj =0.018, and FDR=0.012) (Figure 5(h) b); cell proliferation-related clusters (Figure 5(h) c), such as G2-M checkpoint (NES =2.807, p.adj = 0.018, and FDR =0.012) (Figure 5(h) d) and mitotic spindle checkpoints (NES =2.850, p.adj = 0.018, and FDR = 0.012) (Figure 5(h) e); and apoptosis related clusters, such as PD-1 signal path- way (NES =- 2.222, p.adj =0.018, and FDR =0.012) (Figure 5(h) f).
3.5. Genetic Alteration Analysis of CKS1B. The total fre- quency of CKS1B genetic alteration in patients with 33 tumor types was 3.54% (388/10953), and the top five tumors with the highest frequency were CHOL (cholangiocarci- noma) (16.67%), LIHC (11.56%), BRCA (9.5%), nonsmall cell lung cancer (9.19%), and UCEC (8.77%). On the con- trary, CKS1B genetic variation was hardly observed in KIRC, leukemia, undifferentiated STAD, seminoma, nonsemino- matous germ cell tumors, well-differentiated thyroid carci- noma, and ocular melanoma. “Amplification” was the most common type of genetic variation in all tumor cases. In addi- tion, “mutation” in CHOL, COAD, HNSC (head and neck squamous carcinoma), and “structural variant” in pleural mesothelioma also had a high incidence (Figure 6(a)). The location, type, case number of CKS1B genetic variation, and 3D structure of CKS1B protein were further presented in Figures 6(b) and 6(c), respectively. We then investigated the potential association between CKS1B alteration and sur- vival outcomes in tumor patients. Take ACC for instance, patients with altered CKS1B showed a worse OS (p = 0.016 ) and DFS (p=9.542E- 4), but not the disease-specific (p = 0.0715) and progression-free survival (p = 0.355), com- pared with patients without CKS1B alteration
(Figure 6(d)). The dot plot in Figure 6(e) indicated the rela- tionship between the copy number of CKS1B and mRNA expression. It could be seen that the mRNA expression level of ACC samples with CKS1B deletion was lower than that of CKS1B amplification.
3.6. CKS1B DNA Methylation Analysis Data. Methylation analysis result in ACC demonstrated that CKS1B methyla- tion was significantly lower in tumor than corresponding normal tissues (Figure 7(a)). Beyond that, we also found 4 methylation sites (cg04915414, cg10019844, cg17891149, and cg21786227) which negatively correlated with CKS1B expression and 1 methylation site (cg17833341) which posi- tively correlated with CKS1B expression in DNA sequence (Figure 7(b)).
3.7. CKS1B Correlates with Tumor Mutational Burden and Microsatellite Instability. Tumor mutation burden (TMB) is the total number of mutations per million bases in the coding region of gene exons that encode specific tumor cell proteins, including insertions, substitutions, deletions, and other forms of mutations [21]. It is also an emerging bio- marker for the prediction of immunotherapy in certain tumors, such as lung cancer, malignant melanoma, and bladder cancer [22-24]. Microsatellite instability (MSI) is a genetic change. In the process of normal cell proliferation, there is a complete DNA mismatch repair system, which can detect the replication errors of microsatellite sequence in time and quickly correct it, so that the microsatellite sequence can be replicated in high fidelity, thus, maintaining the stability of it [25]. Due to the DNA mismatch repair defects in process of tumorigenesis, errors in the replication cannot be detected in time, causing insertion or deletion of repeated units, or changes in the length of microsatellite sequences, which eventually leads to MSI [26]. A large num- ber of clinical observations, retrospective studies, and meta-
analysis have confirmed that MSI is closely related to tumor prognosis [27]. Here, we analyzed the relationship between CKS1B expression and TMB/MSI in the TCGA database. As shown in Table 2 and Figure 8(a), CKS1B was negatively correlated with TMB in THYM, but positively correlated with it in ACC, UCEC, STAD, SKCM, SARC (Sarcoma), etc. (all p < 0.05). Besides, CKS1B was also negatively corre- lated with MSI in LUSC and LAML, but positively correlated with it in UCEC, THCA, STAD, SARC, LIHC, KIRP, HNSC, DLBC (lymphoid neoplasm diffuse large B-cell lymphoma), COAD, BRCA, and BLCA (bladder urothelial carcinoma) (Table 3 and Figure 8(b), all p < 0.05). In combination with the foregoing, our results indicated that CKS1B had both tumor prognosis and therapeutic effect prediction value. This point deserves further study.
4. Discussion
CKS1B, also known as cell cycle-dependent protease regula- tory subunit, is a small molecule protein (9KD) encoded by CKS1 gene in the lq21 region of human chromosome and participate a lot of important physiological and pathological processes. Recently, more and more scholars have discov- ered that CKS1B is closely related to the occurrence and development of malignant tumors. For example, Fujita et al. found CKS1B protein was highly expressed in nonsmall cell lung cancer patients [12]. Shrestha et al. confirmed both CKS1B mRNA and protein in gastric cancer cells were sig- nificantly higher than those in normal control cells [11]. Liu et al. reported CKS1B in breast cancer was associated with patient’s age, estrogen, and progesterone receptor levels and increased with malignant degree [15]. Besides, CKS1B was also found to be upregulated in patients with prostate cancer, colorectal cancer, leukemia, retinoblastoma, and other malignant diseases or animal models [10, 28, 29]. Therefore, CKS1B is generally regarded as a cancer- promoting factor. However, most studies of CKS1B have focused on a single disease, and pan-cancer analysis of it from a holistic perspective has not been reported yet. Here, we searched several of the most important databases, such as TCGA, TIMER, and GEPIA, to comprehensively summa- rize CKS1B gene expression, genetic changes, methylation modifications, and prognosis analysis in different tumors.
Our results revealed that although CKS1B was highly expressed in most tumors, its survival and prognostic signif- icance varied among them. For example, high CKS1B expression was associated with poor OS and DFS in KIRP, LGG, LUAD, PAAD, and SKCM. In view of this, identifying high-risk patients as soon as possible, formulating personal- ized treatment plans, and strengthening regular follow-up of these patients are expected to improve their prognosis. How- ever, CKS1B showed no correlation with OS of LUSC and LAML. More even, its high expression was related to favor- able OS in RIRC and better DFS in GEM. Our RNA sequencing in LAML and RT-qPCR in GEM also confirmed this. While whether the current evidence based on databases could fully and truly reflect the prognostic significance of CKS1B in other tumors need to be further verified by more basic experiments.
| Cancer type | Cor | p value | Sig |
|---|---|---|---|
| ACC | -0.107 | 0.349 | |
| BLCA | 0.153 | 0.002 | |
| BRCA | 0.063 | 0.045 | ∗ |
| CESC | 0.025 | 0.669 | |
| CHOL | 0.242 | 0.155 | |
| COAD | 0.164 | 0.001 | |
| DLBC | 0.344 | 0.017 | ∗ |
| ESCA | 0.117 | 0.142 | |
| GBM | 0.031 | 0.704 | |
| HNSC | 0.266 | <0.001 | |
| KICH | 0.075 | 0.553 | |
| KIRC | 0.068 | 0.212 | |
| KIRP | 0.142 | 0.017 | ∗ |
| LAML | -0.278 | 0.022 | ∗ |
| LGG | -0.049 | 0.270 | |
| LIHC | 0.103 | 0.047 | ∗ |
| LUAD | -0.045 | 0.312 | |
| LUSC | -0.123 | 0.006 | |
| MESO | -0.038 | 0.736 | |
| OV | -0.053 | 0.384 | |
| PAAD | 0.089 | 0.244 | |
| PCPG | -0.021 | 0.779 | |
| PRAD | -0.011 | 0.807 | |
| READ | 0.158 | 0.052 | |
| SARC | 0.277 | <0.001 | |
| SKCM | 0.073 | 0.113 | |
| STAD | 0.223 | <0.001 | |
| TGCT | 0.009 | 0.913 | |
| THCA | 0.15 | 0.001 | |
| THYM | -0.018 | 0.842 | |
| UCEC | 0.191 | <0.001 | |
| UCS | 0.181 | 0.182 | |
| UVM | 0.162 | 0.150 |
We also investigated the relationship between CKS1B and TMB and MSI. It has been demonstrated that these two indicators can predict patient’s response to multiple drugs, especially immune checkpoint inhibitors [30-32]. In this work, CKS1B was shown to be positively associated with TMB and MSI in UCEC, STAD, LIHC, etc., so we speculated that these types of tumors may benefit from immune ther- apy. CKS1B may be used as an evaluation index of chemo- therapeutic responsiveness and provide reference value for clinical drug guidance of some tumors. In addition, we com- pared the difference of DNA methylation status in the non- promoter region of CKS1B. In cases of ACC, we found CKS1B methylation was significantly lower in tumor tissues than adjacent normal tissues. The potential role of CKS1B
DNA methylation in tumourgenesis is worthy of further study.
Occurrence and progression of tumors are not only caused by genetic changes of tumor cells themselves but also the microenvironment also plays a key role in this process [33, 34]. Tumor microenvironment includes cells and extra- cellular matrix, among which CAF is one of the most impor- tant members and accounting for about 50% of total number of cells [35]. CAFs can produce a variety of cytokines and metabolites through direct contact or paracrine and involve in tumor proliferation, metastasis, angiogenesis, drug resis- tance, etc. [36-38]. Here, we found CKS1B was positively correlated with CAFs infiltration in ACC, KICH, and KIRP, but negatively in BRCA, LUAD, STAD, and THYM. Previ- ous studies have reported that high expression of CKS1B could induce drug resistance of lung cancer cells to cisplatin and adriamycin, but it is unclear whether CAFs are involved [14, 39]. Although we are temporarily unable to provide more specific data on CKS1B and CAFs in the LUAD research, we believe that the results of this paper can provide a new idea for future research on CKS1B and lung cancer drug resistance to a certain extent. The mechanism by which CKS1B and CAFs affect tumor microenvironment will be an interesting research direction. Moreover, we analyzed the association between CKS1B and expression of TILs, immu- nosuppressive factors, and immunostimulatory factors in tumor microenvironment. For example, in LGG, CKS1B was positively correlated with Tgd, IL10RB, CD276, and CD48. This was consistent with the conclusion reported by Zou et al. that CD48 was highly expressed and had a poor prognosis in the malignant progression of glioma [40]. Our study provides useful information about the involvement of CKS1B in immune regulation.
5. Conclusions
Our first pan-cancer analysis of CKS1B demonstrated a sta- tistical association between CKS1B and tumor clinical prog- nosis, immune cell infiltration, DNA methylation, tumor mutation burden, and microsatellite instability across multi- ple tumors. It is helpful to understand the role of CKS1B from a holistic perspective. However, there are some limita- tions of our studies. In the future, we will focus on verifying these obtained data through basic experiments to better understand the mechanism and regulatory network of CKS1B.
Data Availability
Some of the original data can be obtained directly from TGGA, OCOMINE, and other databases, further inquiries (RNA sequencing and PCR data) can be directed to the cor- responding author.
Consent
Informed consent was signed by all the participants.
Conflicts of Interest
The authors have declared that no competing interest exists.
Acknowledgments
This study was authorized by the Medical Ethics Committee of the Xiangya Hospital, Central South University. This work was supported by the National Natural Science Foun- dation of China (grant number 81600135).
Supplementary Materials
Figure S1: CKS1B expression in different types of human tumors. The basal expression level of CKS1B in different (a) blood cells, (b) tumor cell lines, and (c) tumor tissues using Consensus database. (d) The expression of CKS1B in paired tumors and normal tissues of CHOL, ESCA, KIRP, READ, COADREAD, THCA, KICH, and PRAD. (e) Corre- lations between CKS1B and tumor stages in BRCA, LIHC, and THCA patients based on GEPIA2. * p<0.05; ** p < 0.01; *** p < 0.001. Figure S2: correlation of CKS1B expres- sion level with survival prognosis. (a) Overall survival and (b) disease-free survival of different tumors based on CKS1B expression level (GEPIA2). (c) Predictive value of CKS1B expression for diagnosis in BRCA, COAD, ESCA, LUSC, OV, READ, KIRC, and GEM patients. Figure S3: correlation of CKS1B expression with tumor immune infiltration. Heat maps of the relationship between CKS1B expression and (a) B lymphocytes, (b) T lymphocytes, (c) chemokines, and (d) chemokine receptors. (Supplementary Materials)
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