RESEARCH
ESCO2’s oncogenic role in human tumors: a pan-cancer analysis and experimental validation
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Yue Huang11, Dapeng Chen21, Yi Bai3, Yamin Zhang3, Zhiwen Zheng1, Qingfeng Fu1, Bocun Yi1, Yuchen Jiang1, Zhihong Zhang1* and Jianqiang Zhu1*
Abstract
Purpose Establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function.
Methods We thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UALCAN and Tumor Immune Single- cell Hub (TISCH). The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2’s impacts on clear cell renal cell carcinoma (ccRCC) cells’ proliferative and invasive capacities in vitro.
Results In our study, 30 of 33 cancer types exhibited considerably greater levels of ESCO2 expression in tumor tissue using TCGA and GTEx databases, whereas acute myeloid leukemia (LAML) exhibited significantly lower levels. Kaplan-Meier survival analyses in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD) demonstrated that tumor patients with high ESCO2 expression have short survival periods. However, in thymoma (THYM), colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), ESCO2 was a favorable prognostic factor. Moreover, ESCO2 expression positively correlates with tumor stage and tumor size in several cancers, including LIHC, KIRC, KIRP and LUAD. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2’S knockdown significantly inhibited the A498 and T24 cells’ proliferation, invasion, and migration.
+Yue Huang and Dapeng Chen contributed equally to this work.
*Correspondence: Zhihong Zhang drzhangzhihong@163.com Jianqiang Zhu
Full list of author information is available at the end of the article
☒ BMC
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Conclusions In conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.
Keywords Pan-cancer, ESCO2, Cell cycle, Single-cell
Introduction
Cancer in most nations gradually displaces cardiovas- cular disease as the leading cause of premature mortal- ity [1]. According to statistics, there will be 19.3 million new cancer cases and almost 10 million cancer-related deaths in 2020. The global cancer burden is expected to reach 28.4 million patients by 2040 [2]. Cancer treatment has steadily transitioned into the era of precision therapy because of the advanced high-throughput sequencing technology. Advanced cancer patients frequently receive targeted therapy and immunotherapy in the clinic. For instance, palbociclib (a CDK4/6 inhibitor) in conjunc- tion with letrozole (24.8 months) resulted in consider- ably longer progression-free survival than letrozole alone (14.5 months) in patients with previously untreated ER- positive, HER2-negative advanced breast cancer [3]. Due to the identification of molecular targets and the grow- ing understanding of their cellular effects, small molecule inhibitors have been created as a primary therapeutic approach for cancer treatment. The discovery of new molecular targets may contribute to advancing com- bined therapies, eradicating drug resistance, and increas- ing therapeutic efficacy. Small molecule inhibitors have a bright future; thus, scientists should investigate novel biomarkers to find potential therapeutic targets.
The cell cycle is a tightly controlled process that sup- ports genetic material replication and cell proliferation. One of the distinguishing characteristics of malignan- cies is abnormal cell proliferation brought on by cell cycle dysregulation [4]. Dysregulation of the cancer cell cycle facilitates cell proliferation, which is driven by excessive mitotic signaling, inhibitory checkpoint failure, or both. Scientists have demonstrated that it is effective to target proteins associated with the cell cycle in order to limit tumor growth. The most notable successes in targeting cell cycle mechanisms have been inhibitors of CDK4 and CDK6 [5-7]. The introduction of these CDK4/6 inhibi- tors for clinical use constitutes a milestone in the treat- ment of breast cancer and may have broad ramifications for the management of other tumor types [8]. Despite the current success of CDK4/6 inhibitors, cancer therapy that targets cell cycle proteins is still in its infancy. An in- depth exploration of cell cycle regulatory mechanisms and their role in cancer will guide current cancer treat- ments and identify new therapeutic targets.
Establishment of sister chromatid cohesion N-acetyl- transferase 2 (ESCO2) was identified as an effective tar- get for cancer therapy, which is a pivotal protein in the
cell division process [9]. ESCO2, the human homolog of yeast ECO1, works on proliferating cell nuclear antigen (PCNA) to stimulate sister chromatid cohesion [10, 11]. Acetylation of the SMC3 subunit of the adhesive protein via ESCO2 acetyltransferase facilitates sister chromatid cohesion, inhibiting cohesin release from chromatin [12]. The cohesin is a multiprotein complex whose typical role is to bind sister chromatids from S-phase to anaphase to prevent premature segregation of sister chromatids and to ensure equal segregation of chromosomes [13, 14]. Hence, the presence of ESCO2 ensures correct chromo- somal segregation and makes recombinant DNA repair possible. Recent research has demonstrated that ESCO2 is strongly linked to the formation of several cancer types [15-19]. ESCO2 knockdown in human gastric cancer cell lines in vitro significantly inhibited cell proliferation and induced apoptosis by regulating P53 [17]. Meanwhile, ESCO2 can promote LUAD cell proliferation and metas- tasis by promoting aerobic glycolysis [16]. Similarly, Fu et al. identified that 53BP1-MDS ring-like structure dis- ruption caused by ESCO2 depletion in colorectal cancer cells, which reduced the effectiveness of non-homol- ogous end joining (NHEJ) repair and made cancer cells more susceptible to chemotherapy [20]. However, Guo et al. discovered that ESCO2 overexpression in colon cancer reduced MMP2’s transcriptional activity to limit tumor metastasis [19]. ESCO2’s involvement in cancer is therefore controversial. Investigating ESCO2’s biologi- cal function in other malignancies is necessary. A pan- cancer analysis can investigate the oncogene in several malignancies, allowing oncogenes’ effector function and differential expression analyses.
We identified ESCO2’s abnormal expression value in tumor and normal samples by pan-cancer analysis and then validated its protein value in clinical tissues. Sub- sequently, we confirmed the value of ESCO2 in assess- ing prognosis and performed gene enrichment analysis, single cell analysis and immuno-infiltration analysis for ESCO2.Moreover, we conducted in vitro experiments to corroborate our bioinformatics findings. ESCO2 is a unique and useful biomarker and a prospective molecu- lar target.
Materials and methods
Data preparation and differential expression analysis The Cancer Genome Atlas (TCGA) database (https:// portal.gdc.cancer.gov/), which has 11,315 total samples, was employed to download the bulk mRNA sequencing
profiles of 33 distinct cancer samples and their corre- sponding normal tissues. All gene expression data were standardized using the Transcripts Per Kilobase of Exon Model per Million Mapped Reads (TPM) for all sam- ples. In the meanwhile, we collected the relevant clini- cal information on cancer patients. The Progression Free Interval (PFI), Disease Free Interval (DFI), and Disease- Specific Survival (DSS) timings were retrieved from UCSC Xena(https://xena.ucsc.edu/), and the Overall Survival (OS) time was acquired from TCGA.
Then, using the TIMER [21] and Xena Shiny databases (https://shiny.hiplot.cn/ucsc-xena-shiny/)-powerful online platforms that incorporated mRNA sequencing data from the TCGA and GTEx datasets [22]-we analyzed the ESCO2’s mRNA expression value between tumor and normal samples for 33 types of cancer. Furthermore, the ESCO2’s protein level was determined using the UAL- CAN online platform (https://ualcan.path.uab.edu/index. html). Finally, three pairs of paraffin-embedded kidney renal clear cell carcinoma (KIRC), bladder urothelial carcinoma (BLCA), and adjacent samples were collected from the Second Hospital of Tianjin Medical Univer- sity. The pathologic type of these individuals was clear cell renal cell carcinoma (ccRCC, i.e., KIRC) or BLCA, and none of them received preoperative chemotherapy. More crucially, all patients have approved the use of the surgical material for academic research and publications. All methods were approved by The Institutional Review Committee and the Medical Ethics Committee of the Second Hospital of Tianjin Medical University. Written informed consents have been obtained from all subjects.
Survival analysis and relationship with clinical stage
We filtered the cancer patients to assess the ESCO2’s impacts on the cancer cases’ prognosis. (a) The aver- age value of several samples from the same patient was thought to represent the ESCO2 expression level. (b) can- cer patients with a follow-up time of zero were excluded. After that, the survival R package was applied to run the survival analysis. The survfit function was utilized to model the Kaplan-Meier survival curve. The best-cutoff point was discovered using the “surv cutpoint” function in a survminer R package, which can repeatedly check all viable cutting points to get the highest rank statistic. We next divided each cancer type’s patients into two groups using the appropriate cutoff point for gene expression. The Kaplan-Meier survival curves were compared based on the two-sided long-rank test. Moreover, we performed Receiver operating characteristic curve (ROC) testing to assess the ESCO2’s ability to predict cancer across all types. Then, we investigated the relationship between ESCO2’s expression level and clinical parameters, includ- ing tumor size, stage, and grade.
Functional enrichment analysis of ESCO2
We extracted the RNA sequencing profiles of cancer types where ESCO2 has prognostic value to investigate ESCO2’s carcinogenesis in malignancies. Hence, 12 can- cer types, including thymoma (THYM), colon adeno- carcinoma (COAD), rectum adenocarcinoma (READ), adrenocortical carcinoma (ACC), kidney chromophobe (KICH), KIRC, kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocel- lular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD), were rolled into the next analysis. For each cancer type, individuals were grouped into ESCO2-high and ESCO2-low subgroups relied on the median value of ESCO2, respectively. After that, gene set enriched in two groups were detected by gene set enrichment analy- sis (GSEA). Gene sets, the 50 hall markers, were obtained from MSigDB [23] (https://www.gsea-msigdb.org/gsea/ msigdb).
The hunt for ESCO2-related regulatory genes
The above data shows that ESCO2 is a negative onco- gene in most cancers. Therefore, we extracted the RNA- seq matrix from KICH, KIRC, LGG, LIHC, LUAD, and PAAD to study ESCO2-related regulatory genes. ESCO2 had a negative impact on these cancer types, which also accounted for more than 150 cases. Similarly, we split patients into ESCO2-high and ESCO2-low groups based on the median value of ESCO2 in each cancer type. Following that, differentially expressed genes (DEGs) between ESCO2-high and the ESCO2-low group were performed using limma R packge [24](p<0.05, Log FC>1). After identifying the intersecting genes of DEGs in each cancer, we calculated the association between ESCO2 and these DEGs by spearman’s correlation analy- sis for each cancer type, respectively (p<0.05, Cor>0.4). Intersecting genes for these highly correlated genes were considered ESCO2-related regulatory genes. Then, using the clusterProfiler R [25] package, the Kyoto Encyclope- dia of Genes and Genomes (KEGG) [26-28] and Gene Ontology (GO) pathway enrichment analyses were per- formed on ESCO2-related regulatory genes. Incorpo- rating ESCO2-related regulatory genes into the String database (https://cn.string-db.org/) to form a PPI net- work enables the identification of core genes.
Single-cell analysis of ESCO2 in cancers
Tumor Immune Single-cell Hub (TISCH, http://tisch. comp-genomics.org/home/) is a robust database created for multiple single-cell analyses containing nearly 190 single-cell databases. We estimated the ESCO2 expres- sion level across multiple cancer types in each cell type. Our study focused on the distribution of ESCO2 in KIRC cell subpopulations. The expression levels of ESCO2 in
each cell type were quantified and visualized by a heat- map, scatter diagrams, and violin plots. In addition to the single-cell dataset of KIRC contained in the TISCH data- base, we also downloaded additional single-cell sequenc- ing data from GSE156632 [29] in the Gene Expression Omnibus (GEO) database to validate ESCO2 expression level in each cell types.
Cell culture and siRNA transfection
The Cell Resource Center Affiliated with the Chinese Academy of Medical Sciences provided the human ccRCC cell lines A498 and human BLCA cell lines T24. In a 5% CO2 humidified cell incubator at 37 ℃, A498 and T24 cells were grown in Dulbecco’s modified Eagle medium (DMEM) (Gibco BRL Life Technologies Inc., USA) with 10% FBS (Gibco BRL Life Technologies Inc., USA) and 1% penicillin-streptomycin (Hy-clone, CA, USA).
Small interfering RNAs (siRNAs) were ordered from GenePharma. The si-NC and si-ESCO2 siRNA were transfected into A498 and T24 cells with a Transfec- tion reagent. When cells occupied 70-80% of a 50 mm Petri dish, transfection experiments were conducted; the whole medium was aspirated, and 2 mL of opti-MEM was applied to each well. The siRNA (GenePharma, Shang- hai, China) and Transfection reagent (Cat#:L3000075, Thermo Fisher Scientific, United States) were added to 500 µL opti-MEM, mixed, and incubated for 20 min at room temperature. After that, the siRNA-Transfection reagent mixture was added to each well. Each well was switched to a complete medium 6 h after the transfection assay. The sequences for ESCO2 siRNA are as follows: si- ESCO2-1 (forward: GCAAAUCAAGGCUCACCAUT T; reverse: AUGGUGAGCCUUGAUUUGCTT) and si- ESCO2-2 (forward: CUCUUAGACCAGGAUUAUCTT; reverse: GAUAAUCCUGGUCUAAGAGUG).
qPCR and western blotting analysis
After the cells were transfected for 48 and 72 h, we har- vested cells for RNA and protein extraction, respec- tively. Following the protocol, the total RNAs of cells were extracted using TRIzol reagents (Invitrogen). We then used cDNA reverse transcription kits to synthesize cDNA, and the mRNA expression level was calculated through qPCR assay in q225. We used the 2-44Ct method to calculate GAPDH as an internal reference for mRNA. The ESCO2’s sequences of primers are as follows: F, CAC TGGGACGCACCCAAAA, R, CACTTGCCTTGTCGC AAAAG (Sangon biotech). The following are the primer sequences for the GAPDH gene .: F, CAGGAGGCATTG CTGATGAT, R, GAAGGCTGGGGCTCATTT (Sangon biotech).
The tissues or cells were collected and lysed in RIPA lysis buffer (Cat#:89,901, Thermo Fisher Scientific, USA)
with supplementation of a protease inhibitor cocktail (Cat#:539,131, Roche, Switzerland) in the ice for 30 min. The total protein extracts were measured by the ABC method (Cat#:23,227, Thermo Fisher Scientific, USA), separated by 10% sodium dodecyl sulfate-polyacryl- amide gel electrophoresis (SDS-PAGE), and transferred to the nitrocellulose membrane. The membrane was blocked using 5% non-fat milk or 5% BSA (Solarbio, Beijing, China), then incubated with primary antibod- ies anti- (ESCO2,Cat#:23525-1-AP, diluted 1:2000, Pro- teintech, USA; CDK1, Cat#:23525-1-AP, diluted 1:2000, Proteintech, USA). The proper secondary antibody is chosen based on the primary antibody’s type, including goat anti-mouse HRP-conjugated IgG (Cat#:SA00001-1, 1:2000 dilutions, Proteintech, USA) and goat anti-rabbit HRP-conjugated IgG (Cat#:SA00001-2, 1:2000 dilutions, Proteintech, USA). The target band signals were captured with the help of a BIO-RAD ChemiDoc XRS chemilumi- nescence system (Bio-Rad Inc., CA, USA). The densitom- etry of all Western blots was analysed using a Gel-Pro analyzer (Media Cybernetics, USA). Imagej software was used to perform quantitative analysis with ß-actin as a control (1.52a, National Institutes of Health, Bethesda, MD, USA).
Cell proliferation and colony formation assay
Briefly, 2000 cells per well were planted onto 96-well plates for the cell proliferation assay, and transfection assay was conducted after cell fixation. Cells were cul- tured in the incubator for 12, 24, 48, and 72 h. After that, each well received 10 µL of Cell Counting Kit-8 reaction solution (CCK8, Cat#:CA1210, Solarbio Science & Tech- nology Co., Ltd., Beijing, China) and was incubated for 1 h. The light absorption values (OD) for si-NC and si- ESCO2 groups were captured at 450 nm on a Varioskan Flash Multimode plate reader (Thermo Fisher Scientific Inc., USA).
Cancer cells per well were seeded onto a 12-well plate, and the medium was changed every two days. After being stained with 0.5% crystal violet (Cat#: C0121, Beyotime) for 20 min and fixed with 4% paraformaldehyde at room temperature for 10 min, colonies were counted 14 days later.
Ethynyl-2-Deoxyuridine (EdU) incorporation assay
Cancer cells were seeded in 3.5 cm confocal dishes (Thermo Fisher Scientific Inc., USA) with 50,000 cells per dish. An appropriate amount of 50 uM EdU (Cat#:K1077, ApexBio Technology LLC) medium was prepared by diluting the EdU solution with cell culture medium at a ratio of 1000:1.1 ml of 50 uM EdU medium was added after the entire medium had been aspirated, and it was then incubated for 2 h. After discarding the medium, it was washed with PBS, fixed with 4% paraformaldehyde,
and then permeabilized with 0.5% TritonX-100 (Cat#:X100, Sigma-Aldrich, USA). 200 um of 1x Apollo (Cat#:K1077, ApexBio Technology LLC) staining reaction solution was added to each dish, incubated in the dark for 30 min. Nuclei were stained with DAPI (Cat#:MBD0015, Sigma-Aldrich, USA) for 10 min. Using a Nikon ECLIPSE 80i fluorescence microscope, images were captured, and ImageJ was used to count the cell number (1.52a, National Institutes of Health, Bethesda, MD, USA).
Transwell migration and wound healing assay
Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) was evenly spread in transwell chambers (Cat#: 3422, Corn- ing Inc., Corning, NY, USA), and cells starved for 24 h were seeded in a serum-free medium in the upper cham- ber. Medium containing 10% FBS was added to the lower chamber as a catalyst. The upper chamber was removed and separated after roughly 24 h, then fixed with metha- nol for 10 min, rinsed with PBS, and stained with 0.1% crystal violet (Cat#:G1064, Solarbio Science & Technol- ogy Co., Ltd., Beijing, China). Under a microscope, cells that had been pierced were viewed and counted (CKX41, Olympus, Tokyo, Japan).
Cells were seeded uniformly in micro-insert 4-wells in a u-dish35 mm, high ibiTreat (ibidi GmbH, Germany). The cul- ture inserts were removed when the cell density reached 90%. The 500 mm wide scratching gaps were washed with phosphate-buffered solution (PBS, Cat#:D1020, Solarbio Science & Technology Co., Ltd., Beijing, China) and were replaced with fresh medium. The dynamic changes of wells were recorded at 0, 12, 24 and 48 h on a microscope (Olympus, Japan).
Statistical analysis
Using the Wilcox test, comparisons between the two groups were made. For analyzing three or more groups, the one-way ANOVA test was performed. For all sta- tistical calculations, R studio and GraphPad software were used. The threshold for significance was defined at P<0.05.
Results
ESCO2 expression level in various cancer
Typically, tumor tissue exhibits higher or lower oncogene expression levels than normal tissue. Using the TCGA database, our work found that ESCO2 is substantially expressed in the tumor tissue of most cancer types. How- ever, the ESCO2 expression value did not significantly change in KICH, PAAD, pheochromocytoma and para- ganglioma (PCPG), or READ (Fig. 1A). Considering only a few normal samples available in the TCGA database, we combined the TCGA and GTEx databases. 30 of 33 can- cer types exhibited considerably greater levels of ESCO2 expression in tumor tissue, whereas acute myeloid
leukemia (LAML) exhibited significantly lower levels. In the ACC and PCPG, the ESCO2 expression levels of the tumor and normal tissues were similar (Fig. 1B). ESCO2 expression was raised in most cancers, supporting its oncogene function.
Furthermore, we evaluated the ESCO2 protein lev- els. We only found significantly higher protein levels of ESCO2 in tumor tissues of uterine corpus endometrial carcinoma (UCEC), breast invasive carcinoma (BRCA), and LUAD due to the UALCAN database’s limited inclu- sion of cancer types (Fig. 1C). To further explore the alterations in ESCO2’s protein levels in carcinogenesis, we collected samples of KIRC and BLCA. In KIRC and BLCA, the protein level of tumor samples was noticeably greater than para-cancerous ones (Fig. 1D).
ESCO2’s prognostic value across cancers
Using the TCGA database and focusing on OS, DFI, DSS, and PFI, we conducted a Kaplan-Meier survival analy- sis in each cancer to examine the association between ESCO2 and the cancer patient’s prognosis. Kaplan-Meier survival analyses in ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and PAAD demonstrated that tumor patients with high ESCO2 expression have short survival periods (Fig. 2D, E, F, G, H, I, J, K and L). While ESCO2’s expression was linked to better outcomes in patients with THYM, COAD, and READ (Fig. 2A, B and C). ROC analysis revealed that the ESCO2 performed well in predictive accuracy, with AUC values in ACC, KICH, KIRP, LGG, MESO, LIHC, and LUAD performing well (Figure S1A). High ESCO2 expression was linked to a worse prognosis in cancer patients with KIRP, LIHC, and PAAD, according to Kaplan-Meier survival analysis for DFI (Figure S1B). Similarly, as shown in Figures S2 and S3, cancer patients with increased ESCO2 expression had worse DSS and PFI across various malignancies. Our investigation revealed that ESCO2 might enhance tumor initiation and progression.
ESCO2 was positively related to tumor progression
To confirm the ESCO2 role in cancer progression, a cor- relation analysis was performed between the ESCO2’s mRNA value and clinical features. We discovered that ESCO2 expression was significantly increased in advanced tumor stage in several malignancies, including KICH, KIRC, KIRP, LUAD, and LIHC. On the contrary, ESCO2 expression was higher in lower tumor stages than in higher tumor stages in COAD (Fig. 3A).
In LIHC, KIRC, LGG, and PAAD, ESCO2 expression was positively correlated with tumor grade (Fig. 3C). Similarly, ESCO2 may also affect the tumor size: Tumor size and ESCO2 expression were positively linked in LIHC, KIRC, KIRP, and LUAD. (Figs. 3D). Furthermore, ESCO2 was crucial for the metastasis of COAD, KIRC,
A
C
Uterine corpus endometrial carcinoma
ESCO2 Expression Level (log2 TPM)
2
2
+*
O
N
1
1
1
0
D
F
ACC. Tumor (n=79)
BLCK ImoTe
BLCA.Normal (n=19) BRCA. Tumor (n=1053)
BRCA-R-LT BRCA-Basal. Tumor (n=190)
BRCA.Normal (n=112)
BRCA-Herz, Jumelage
BRCA-LumB.Tumor (n=217)
BRCA-LumA Tumor (naccs)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL. Tumor In-go)
CHOL Normal
COAD. Tumor (n=457)
COAD.Normal (n=41)
DLBC.Tumor (n=48)
ESCA Tumor (n=184)
ESCA Normal (n=11)
Tur Je- 1931
GBM. Tumor (n= 155)
GBM.Normal (n=5)
HNSC.Tumor (n=620)
HNSC,Normal (n=44)
NOCH
HNSC-HPV. Turnce (n=97)
HNSC-HPV — Tumor (1-421)
KICH. Tumor (n=66)
KICH.Normal (n=22)
KIRC.Tumor (n=533)
KIRC,Normal (n=72)
KIRP Tumor (n=390)
MIDO Not
1731-
LAML Tumor (n=173)
LGG. Tumor 219
LIHC. Tumor (n=371)
LIHC, Normal (n=50)
LUAD.Tumor (n=515)
LUAD.Normal (n=59)
LOW In Der
LUSC. TUTTO
LUSC.Normal (n=51)
MESO. Tumor (n=87)
Normal [n=31)
Primary Tumor (n=100)
CV.Tumor (n=303)
PAAD.Tumor (n=178)
DAAD, Normal (n 4)
Pepe Time nelze- PCPG.Tumor (n=179)
PCPG. NOTRE PRAD. Tumor (n-497) PRAD.Normal (n=52)
READ. Tumor (n=166)
DEAD Nocmal ine10)
SARC. Tumor (n=269)
SKCM. Tumor (n= 105)
SKCM.Metastasis (n-368)
STAD.Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n= 150)
THCA Tumor (nebo !!
THCANON
THYM. Tumor (n=120)
UCEC.Tumor (n=645)
UCEC.Normal (n=35)
DES. Tume: UCS. Tumor (n=57)
UVM. Tumor (n=80)-
Breast cancer
B
ns
ns
ESCO2 (log2(tpm+0.001))
Normal (n=18)
Primary Tumor (n=125)
0
3
Lung adenocarcinoma
-
5 E
Tumor Normal
2
i
1.
-5
+
0
-1
-2
-10
-3
SARC SKCM
-4
AC
BLC
BRCA
CESC
CHOL
COAD
DL8C
ESCA
GBM
HNSC
KICH
KIRC
KIRp
LAML
LGG
LIHC
LUAD
LUSC
ou
PAAD
PCPG
PRAD
READ
STAD
TGCT
THCA
THYM
UCEC
UCS
Normal (n=111)
Primary Tumor (n=111)
D
KIRC
BLCA
P1
P2
P3
P1
P2
P3
N
T
N
T
N
T
N
T
N
T
N
T
0.62
1.01
0.69
0.88
0.56
0.79
0.43
0.70
0.56
1.35
0.58
0.72
ESCO2
ESCO2
00
B-actin
B-actin
and LUAD (Fig. 3B). Tumor stage, size, and metastasis are the most widely used clinical indicators for evaluat- ing the progression of cancer. Our investigation verified the connection between ESCO2 and the aforementioned indicators, indicating that ESCO2 is involved in tumor progression.
Analysis of ESCO2-related regulatory pathways
We gathered RNA sequencing profiles of cancer types where ESCO2 could significantly affect patients’ over- all survival times to examine the ESCO2 oncogenic role in malignancies. For each cancer type described in the methods, cancer cases were grouped into ESCO2-high and -low subgroups according to the median value of ESCO2, respectively. Gene set enriched in two groups were then detected by gene set enrichment analysis. For visualization, we chose the top-10 NSE-ranked enriched pathways. Intriguingly, GSEA results were remark- ably consistent across the 12 cancer types, indicating the validity of our findings (Fig. 4A, B, C, D, E, F, G, H, I, J, K and L). Genes upregulated in the ESCO2-high group showed the E2F targets enrichment, G2M check- point, mitotic spindle and Mtorc1 signaling, which were
associated with cell cycle and proliferation. Meanwhile, these ESCO2-high patients exhibited highly enriched DNA repair. In addition to cell proliferation-related path- ways, glycolysis and fatty acid metabolism pathways were enriched in the ESCO2-high group in several cancer types. Furthermore, ESCO2 seemed to participate in the modulation of cancer inflammation, such as IL2 STAT5 signaling.
To investigate ESCO2-related regulatory genes, we divided cancer patients into ESCO2-high and ESCO2- low groups based on the median ESCO2 value in six cancer types specified in the materials and methods. We then obtained 2419 intersecting genes, which mainly enriched on histone binding, ATP hydrolysis activity and helicase activity (Fig. 5A and B). The results of the GO analysis also confirmed that ESCO2 was primarily responsible for controlling cell growth. After spearman’s correlation analysis, we further identified the ESCO2- related regulatory genes. 249 ESCO2-related regulatory genes were collected in our study, which was highly cor- related with ESCO2 across cancer types (Fig. 5C). These ESCO2-related regulatory genes were primarily involved in cell-division-related processes, like the mitotic cell
A
TCGA-THYM-OS
B
TCGA-COAD-OS
C
TCGA-READ-OS
1.00
1.00-
1.00
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
0.50
0.50
0.50
0.25
p<0.001
0.25
p=0.009
0.25
p=0.002
ESCO2
ESCO2
ESCO2
0.00
high
low
0.00
high
low
0.00
high
low
high
68
52
34
18
11
6
1
high
138
67
23
10
4
0
0
high
90
45
13
4
3
2
low
51
36
19
5
2
1
1
low
225
112
27
13
5
4
1
low
52
25
4
1
0
0
0
2
4
6
8
Time(years)
10
12
0
2
4
6
8
10
12
Time(years)
0
2
4
Time(years)
€
8
10
D
TCGA-ACC-OS
E
TCGA-KICH-OS
F
TCGA-KIRC-OS
1.00
1.00
1.00
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
0.50
0.50
0.50
0.25
p<0.001
0.25
p<0.001 ESCO2
0.25
p<0.001
ESCO2
0.00
+ high
ESCO2
0.00
high
0.00
high
high
43
25
10
4
3
1
0
high
7
5
1
0
0
0
0
high
54
29
18
5
4
1
0
low
35
33
20
12
5
3
2
low
57
49
41
33
23
6
2
low
478
327
199
93
37
12
1
0
2
4
6
8
Time(years)
10
12
0
2
4
Time(years)
6
8
10
12
0
2
4
Time(years)
6
8
10
G
H
12
I
TCGA-KIRP-OS
TCGA-LGG-OS
1.00
TCGA-LIHC-OS
1.00
1.00
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
0.50
0.50
0.50
0.25
p<0.001
0.25
p<0.001
0.25
ESCO2
p<0.001
ESCO2
0.00
high
+
low
0.00
high
-
ESCO2
0.00
high
low
high
29
8
4
2
0
0
0
0
0
high
99
44
16
9
4
4
2
1
1
0
0
high
206
69
31
11
1
1
low
258
143
72
35
13
4
1
1
1
low
409
196
73
43
20
12
6
3
0
0
0
low
159
71
32
15
5
0
0
2
4
6
Time(years)
8
10
12
14
16
1
0
2
4
6
8
10
K
Time(years)
12
14
18
18
20
L
0
2
Time(years)
4
6
8
10
TCGA-LUAD-OS
TCGA-MESO-OS
1.00
TCGA-PAAD-OS
1.00
1.00
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
0.50
0.50
0,50
0.25
p<0.001
0.25
p<0.001
0.25
ESCO2
p<0.001
ESCO2
0.00
high
ESCO2
high
₩ low
0.00
0.00
high
high
142
60
15
6
4
2
2
1
1
1
0
high
63
14
1
0
0
high
148
24
5
1
0
low
361
162
63
32
13
7
4
2
2
2
0
low
22
16
7
3
0
low
29
12
6
1
0
0
2
4
6
8
10
12
14
16
18
20
Time(years)
0
2
Time(years)
4
6
8
0
2
Time(years)
4
6
8
cycle and DNA metabolic processes (Fig. 5F). We suc- cessfully identified the core genes by building the PPI network of ESCO2-related regulatory genes. The regu- latory genes network’s nucleus is CDK1 (Fig. 5D). Cell cycle kinase family member Cyclin-dependent kinase 1 (CDK1) significantly impacts cell cycle progression [30, 31]. After the ESCO2 knockdown, we discovered that the CDK1 expression was significantly decreased (Fig. 5E). Therefore, CDK1 may be an important signaling pathway regulated by ESCO2 in KIRC.
ESCO2 analysis at the single-cell level
Recent studies have shown that cell cycle proteins play an additional role in tumor development, affecting not only
tumor cells but also their microenvironment and modu- lating anti-tumor immune responses [32]. For instance, inhibition of CDK4/6 enhances tumor cell immunoge- nicity through multiple mechanisms [7]. Meanwhile, CDK4/6 inhibitors substantially reduce the proliferation of regulatory T cells and encourage cytotoxic T cells to eliminate tumor cells [33]. Therefore, we next explored the ESCO2’s role in the anti-tumor immune response. By scRNA-seq, we can investigate the ESCO2 at the single- cell level. Using single-cell sequencing of COAD, KIRC, KICH, LIHC and PAAD, we observed that ESCO2 was expressed not only in malignant cells but also in endo- thelial and immune cells, including T cells, macrophages, NK cells and B cells (Fig. 6A). Significantly, ESCO2 was
A
TCGA-COAD
ESCO2 mRNA expression level
40
p=0.0004
ESCO2 mRNA expression level
TCGA-LIHC p<0.0001
TCGA-KICH
TCGA-KIRC
20-
ESCO2 mRNA expression level
4-
p=0.0003
ESCO2 mRNA expression level
8-
p=0.0008
30-
15-
3-
6-
20-
10-
2-
4-
10-
5-
1-
2
0
0
0
-
0
Tumor stage
1
2
3
4
Tumor stage
1
2
3/4
Tumor stage
1
2
3
4
Tumor stage 1
2
3
4
TCGA-KIRP p<0.0001
TCGA-LUAD p=0.0125
B
TCGA-COAD
TCGA-KIRC
TCGA-LUAD
ESCO2 mRNA expression level
8-
ESCO2 mRNA expression level
20-
ESCO2 mRNA expression level
40
p=0.0025
ESCO2 mRNA expression level
8-
p=0.0059
ESCO2 mRNA expression level
20-
p=0.0364
6-
15-
30-
6-
15-
4-
10-
20-
4-
10-
2-
5-
10-
2-
5-
0
0
0
0
0
Tumor stage
1
2
3
4
Tumor stage 1
2
3
4
no-metastasis
metastasis
no-metastasis
metastasis
no-metastasis
metastasis
C
ESCO2 mRNA expression level
TCGA-LIHC p=0.0137
ESCO2 mRNA expression level
TCGA-KIRC p=0.0028
ESCO2 mRNA expression level
TCGA-LGG p<0.0001
ESCO2 mRNA expression level
TCGA-PAAD p=0.0004
20
8.
25
8-
15-
6.
20
6-
15-
10-
4.
4.
10-
5-
2-
5-
2-
0
0
0
0
Tumor grade 1
2
3/4
Tumor grade
1
2
3
4
Tumor grade
2
3
Tumor grade 1
2
3
D
ESCO2 mRNA expression level
TCGA-LIHC p<0.0001
TCGA-LUAD p=0.0101
20
ESCO2 mRNA expression level
TCGA-KIRC p=0.0178
8-
ESCO2 mRNA expression level
TCGA-KIRP p<0.0001
8-
ESCO2 mRNA expression level
20
15-
6-
6-
15-
10-
4
4-
10-
5.
2-
2-
5-
0
0
0
E
0
Tumor size
1
2
3/4
Tumor size
1
2
3/4
Tumor size
1
2
3/4
Tumor size
1
2
3
4
strongly expressed in proliferating T cells. We found that ESCO2 was almost exclusively expressed in proliferating T cells in KIRC (Fig. 6B). To further validate the results observed in the TICSH database, we subjected all quality single cells of KIRC(GSE156632) to single-cell processing procedures. Similarly, ESCO2 was significantly expressed in proliferating T cells, but we observed a higher expres- sion of ESCO2 in tumor cells and macrophages as well (Fig. 6C, D and E).
ESCO2 Promotes Human ccRCC and BLCA cell proliferation and invasion in vitro
To confirm ESCO2’s biological effector role in carcino- genesis, we first knocked it down in human ccRCC cells.
qt-PCR and Western blotting results showed that ESCO2 was successfully knocked down (Fig. 7A). Results from the CCK8 assay, colony formation assay, and EdU stain- ing showed that the A498 cells’ ability to proliferate was significantly decreased by ESCO2 suppression than the control group (Fig. 7B, C and F). This is consistent with our previous bioinformatic analysis that ESCO2 signifi- cantly regulates cell cycle progression. To further identify the potential role of ESCO2 in the invasive and migra- tion of A498 cells. According to the findings of Transwell and Wound Healing, silencing ESCO2 inhibits invasion and migration (Fig. 7D and E). To confirm ESCO2 as a potential pan-cancer biomarker, we further validated the carcinogenesis of ESCO2 in bladder cancer cell lines.
A
B
C
·
TCGA-READ
TCGA-COAD
TCGA-THYM
-
Uk
D
E
F
LA
*
TCGA-KIRC
M
TCGA-KIRP
TCGA-MESO
MYC TARGETS VE
sư
La
G
H
1
TCGA-LIHC
TCGA-LUAD
TCGA-ACC
**
63
J
K
L
TCGA-PAAD
TCGA-LGG
..
TCGA-KICH
-
- ON DECKPONT
0
According to the results of the experiments in vitro, the proliferative capacity of T24 cells was significantly inhib- ited after ESCO2’s knockdown (Fig. 8A, B, C and F). Furthermore, the invasive ability of the T24 cells after knockdown of ESCO2 significantly decreased compared with the control group (Fig. 8D and E). We confirmed, therefore, that ESCO2 is required for the proliferation and invasion of human ccRCC and BLCA cells.
Discussion
Cancer treatment has evolved significantly, with the introduction of immunotherapy and targeted medicines increasing patient survival rates for those with advanced or metastatic cancer, but overall clinical outcomes remain disappointing. The challenge of treating advanced cancers has inspired researchers to explore the underly- ing mechanisms that contribute to cancer growth, which will help identify potentially effective therapeutic targets. Unquestionably, one of the most promising therapeu- tic targets is cell cycle related protein. Palbociclib, the first CDK4/6-specific inhibitor, was created in 2004 and has proven effective against a variety of human cancer cell lines [34]. All three CDK4/6 inhibitors (palbociclib,
ribociclib, and abemaciclib) are currently approved by the United States Food and Drug Administration (FDA) for the treatment of breast cancer. In addition, numerous inhibitors of cell-cycle proteins, including CDK9, CDK2, and CDK5, are in clinical trials [35, 36]. Cell cycle regu- lating proteins such as CDCA4, CDCA8, and KIF2C have been linked to tumor growth and progression, affecting the proliferation, migration, invasion and metastasis of cancer cell lines [37-39]. ESCO2 was first reported in Roberts syndrome, whose inactivation mutation causes Roberts Syndrome [11]. Subsequent researchers have identified that ESCO2 is crucial in controlling cell mitosis and preserving genomic stability because it bridges two sister chromatids and is involved in the mitotic S-phase adhesins acetylation [40, 41]. As described in the con- text, current researchers consider ESCO2 an excellent therapeutic target as an oncogene that promotes can- cer development. However, current cancer research on ESCO2 is limited to a few cancers, such as lung, stomach, and colon cancers. No studies are focusing on ESCO2 in multiple cancers that can shed light on the similarities of ESCO2 in cancer. In this study, we identified that ESCO2 was a reliable biomarker for cancer patients and could
catalytic activity, acting on DNA
KIRP
helicase activity
-
ribonucleoprotein complex binding
KIRC
1
translation regulator activity
M
»
single-stranded DNA binding
-
-
-
1
-
LGG
ATP-dependent activity, acting on DNA
9
**
.
2
#
.
nucleocytoplasmic carrier activity
-
-
291
4
7
chromosomal region
.
13*
nuclear speck
39
a+
-
spindle
2419
-
14
ONTOLOGY
#
IF
m
Term
condensed chromosome
chromosome, centromeric region
BP
#
nuclear chromosome
CC
#
.
2
=
spliceosomal complex
*
-
condensed chromosome, centromeric region
MF
.
-
kinetochore
.
”
catalytic step 2 spliceosome
.
-
-
4
”
-
LIHC
ribonucleoprotein complex biogenesis
PAAD
-
N
RNA splicing
-
chromosome segregation
LUAD
nucleocytoplasmic transport
mitotic nuclear division
DNA replication RNA localization
C
D
sister chromatid segregation
regulation of chromosome organization
mitotic sister chromatid segregation
KIRC
MIRP
0
40
80
120
160
RFC4
88
Count
31
79
LGG
MCM5
88
305
CDC6
42
UBE2C
88
90
E
A498
sì-ESCO2-1
si-ESCO2-2
47
29
KIF2C
BIRC5
90
13
18
2
PX2
249
AURKĄ
94
96
si-NC
18
6
TTK
MCM4
98
LANG
20
25
MCM3
47
DLGAP5
CDC45
98
0.90
0.47
0.51
150
RRM2
98
15
16
102
FAAD
MCM2
102
KIF20A
CDK1
NCAPG
102
MAD2L1
108
CDCA8
112 €
0.72
0.45
0.46
LUND
ASPM
11
PLK1
120
NDC80
122
ESCO2
.
Size of each list
BUB1B
12
1497
CCNB2
30
TOP2A
134
748.5
KIF11
140
194
1810
140
ß-actin
to
CDC20
148
KIRG
CCNB1
150
KIRP
LGG
LING
LUAD
PAAD
BUB1
Number of elements: specific (1) or shared by 2, 3. … lists
CDK1
150
200
r
$
5
4
3
2
1
0
50
100
150
200
Number of adjacent nodes
F
mitotic cell cycle process
Cell Cycle, Mitotic
DNA metabolic process
Retinoblastoma gene in cancer
positive regulation of cell cycle process
meiotic cell cycle
DNA IR-damage and cellular response via ATR
cell cycle phase transition
PID AURORA B PATHWAY
PID PLK1 PATHWAY
Chromosome Maintenance
DNA Double-Strand Break Repair
G1/S-Specific Transcription
Gastric cancer network 1
chromosome localization
PID FANCONI PATHWAY
Mitotic Prophase
centrosome cycle
DNA mismatch repair
regulation of cytokinesis
accurately predict the cancer patient’s prognosis. Fur- thermore, ESCO2 participated in mitosis, the cell cycle, DNA damage repair, other processes, and tumor immune infiltration. Finally, we confirmed that ESCO2 is essential for the proliferation and invasion of human ccRCC and BLCA cells in vitro.
First, we evaluated the ESCO2’s mRNA expression value in 33 cancers by TCGA and GTEx databases. The analysis showed that ESCO2 expression was upregulated
in most cancer tissues except LAML, ACC and PCPG. Due to post-transcriptional processing, protein and mRNA gene expression levels may differ. Thus, we vali- dated ESCO2 protein levels in KIRC and BLCA clinical samples. ESCO2 protein values were considerably greater in malignant tissues than in para-cancerous ones, con- sistent with the mRNA analysis. These results confirmed that ESCO2 was upregulated in various cancers, suggest- ing a promising future for ESCO2 in cancer diagnosis.
A
ESCO2
B
log(TPM/10+1)
KIRC_GSE111360
ESCO2
CRC_EMTAB01OZ
0.02
0
·
5.01
1.5
34
CHC_GSE100212
001
2
003
4
CRC_OSE112065_mouse_201
.
Đ
·
D
0
0.00
.
1
Mast
-
24
CRC_GSE129909_move_PD1
0:02
03
0
0.05
0.00
0
4
13
RG_GSE122909_mouse_aPO faTIM3
.
.
0
₱
0.18
0.5
Tourw
@
1ª
CAC_GSE135194
@
4
·
· Treg
CBC_GSE139555
q
q
0.50
0
0.01
a
0
0.01
0
0
6
CRG_GSE146771_10X
0
·
.
·
·
0
0
.
CRC_GSE146771_Smartseu2
0.02
0.02
CRC_GSE106605
·
:46
·
0.06
0.01
0.02
0.02
0.13
KIRC_GSE121636
ESCO2
CAC_GSE179264
0.00
0.46
4.05
004
0.00
KICH GSE159115
0.06
0.01
.
a*
0.03
Cette jogosöneagel
KIRG_GSE111202
.
.
0
·
.
0.00
0
Đ
4
.
KIRG_GEL121635
@
0.41
Đ
D
.
0
Đ
· Mono Nisam
0
HIRC_GSE139555
0
3.41
0
D
D
0.02
@
:
KIRG_GSE145281_aPDL1
0
0.37
0
Đ
0
0
0
D
0.02
.
KIRG_GSE159115
Đ
0.01
·
KIRG_OSE171305
@
837
5
0.01
.
.
.
LHC_05E12542_OPPL1OCTLA
0.08
a
0
0:00
q
KIRC_GSE139555
ESCO2
LPG_GSE140220_10X
g
6.57
0.02
ĐỚI
9
0
0.02
WIMG_GSE140221_Smartseq2
-
@
001
·
0
0.02
an
-
UHG_GSE140115
.
0/02
0 02
Endohelst
.A
LOIC OSE 146409
0.01
K
LING_GSE105035
.
0.58
0
0:02
0.00
a
0.01
T prakt
5
y
LING_GSE90038
001
0.04
CD4TODAY
Mast Mono Macro WEGGAAN
Tpreid
CDSTex
#
$
Plasma
Endothelial
Fibroblasts
Epithelial
que
.
Pericytes
-
C
GSE156632
ESCO2
N
1
E
ESCO2
3
5
Tumor cell
Expression Level
Macrophage
2
Endothelial cell
Fibroblast
Y
TINK cell
1
Doublets
-S
-S
Dendritic cell
A
Prolifering T cell
B cell
-19
0
Mast cell
Tumor cell
Macrophage
Endothelial cell
Fibroblast
TINK cell
Doublets
Dendritic cell
Prolifering T cell
B cell
Mast cell
-19
..
UMAP_1
.
13
-$
UMAP_1
.
-
Additionally, OS, DSS, DFI, and PFI analyses all revealed that ESCO2 was significantly associated with cancer patient’s prognosis. Evaluated ESCO2 resulted in poorer prognosis in ACC, BLCA, BRCA, CESC, COAD, GBM, HNSC, KIRC, LGG, LIHC, LUAD, LUSC, and PAAD. However, in THYM, COAD and READ, ESCO2 was a favorable prognostic factor. ESCO2 has been shown to prevent cancer metastasis in a recent trial on colon can- cer, which is consistent with the results of our bioinfor- matics research [19]. According to the findings above, ESCO2 is crucial in determining a cancer patient’s prog- nosis and can serve as a reliable prognostic biomarker. To some extent, tumor grading and clinical staging can show how tumors progress. Our results show that ESCO2 expression was positively associated with tumor stage and size in LIHC, KIRC, KIRP and LUAD. Further- more, ESCO2 was crucial for the metastasis of COAD, KIRC, and LUAD. Our research identified that ESCO2 is a proto-oncogene that is linked to the development and progression of tumors.
Emerging research has confirmed that ESCO2 par- ticipated in apoptosis and cell proliferation-related pathways, such as the P53 and mTOR pathway [15, 17].
Furthermore, ESCO2 could stimulate aerobic glycolysis in LUAD cells by upregulating PKM2 and downregulat- ing PKM1 expression [16]. The GSEA results in our study were unexpectedly uniform among the 12 different can- cers, indicating the validity of our findings. The cell cycle and proliferation-related E2F targets, G2M checkpoint, mitotic spindle, and Mtorc1 signaling were concentrated in the genes upregulated in the ESCO2-high group. These ESCO2-high patients also displayed greatly enhanced DNA repair. In sum up, our bioinformatics analysis con- firmed ESCO2’s contribution to the control of cell prolif- eration and cycle. Therefore, we conducted experimental validation to verify the results of the bioinformatics anal- ysis. We knocked down ESCO2 in A498 and T24 cells, and ESCO2’S knockdown significantly inhibited the pro- liferation, invasion, and migration of A498 and T24 cells. This result demonstrated that ESCO2’s role in cancer might be unified, further indicating the possibility that it could be an effective therapeutic target. Furthermore, we investigated its potential regulatory mechanisms. We found the core of the ESCO2-CDK1 regulatory net- work. When ESCO2 was inhibited, CDK1 expression decreased, demonstrating that CDK1 is a downstream
A
si-NC
B
si-NC
si-ESCO2-1
si-ESCO2-1
si-ESCO2-2
si-ESCO2-2
2.0
Relative expression of ECSO2
1.5
si-NC
si-ESCO2-1
si-ESCO2-2
Cell viability(O.D)
1.5
1.0
1.0
0.64
0.32
0.31
ESCO2
0.5
0.5
ß-actin
0.0
**
0
24
48
72
0.0
Time(Hours)
C
DAPI
Edu
Merged
si-NC
si-ESCO2-1
si-NC
60
si-ESCO2-2
500 pm
500 pm
500 jum
si-ESCO2-1
Edu Positive Rate(%)
40
20
500 pm
5:00 pm
5.00 pm
0
si-ESCO2-2
500 peta
500 pm
500 pm
si-NC
D
0 h
24 h
72h
si-ESCO2-1
si-ESCO2-2
500μπ
si-NC
80
60
si-ESCO2-1
Wound Closure(%)
40
500um
20
si-ESCO2-2
0
500um
E
si-NC
si-ESCO2-1
si-ESCO2-2
F
si-NC
si-ESCO2-1
si-ESCO2-2
600
si-NC
si-NC
si-ESCO2-1
500
si-ESCO2-1
Cell number
si-ESCO2-2
Clolony number
400
si-ESCO2-2
400
300
200
200
100
0
0
A
si-NC
B
si-NC
si-ESCO2-1
si-ESCO2-1
si-ESCO2-2
si-ESCO2-2
1.5
4.0
Relative expression of ECSO2
si-NC
si-ESCO2-1
si-ESCO2-2
Cell viability(O.D)
3.0
I
1.0
0.84
0.46
0.47
2.0
ESCO2
0.5
1.0
ß-actin
0.0
0
24
48
72
0.0
Time(Hours)
C
DAPI
Edu
Merged
si-NC
si-NC
si-ESCO2-1
si-ESCO2-2
500 Jim
500 yım
500 Jim -
40
si-ESCO2-1
Edu Positive Rate(%)
30
20
$00 pm
500 pır
540 pm
10
si-ESCO2-2
0
500 pm
500 pm
590 um
-
D
0 h
24 h
72h
si-NC
si-ESCO2-1
500jam
si-NC
si-ESCO2-2
100
si-ESCO2-1
Wound Closure(%)
80
500um
60
40
si-ESCO2-2
500um
20
0
E
si-NC
si-ESCO2-1
si-ESCO2-2
F
si-NC
si-ESCO2-1
si-ESCO2-2
500 pra
600
si-NC
500
si-NC
si-ESCO2-1
si-ESCO2-1
Cell number
si-ESCO2-2
Clolony number
400
si-ESCO2-2
400
300
200
200
100
0
0
gene controlled by ESCO2. Cell cycle progression is the central event in all proliferating cells and is primarily reg- ulated by cell cycle-dependent kinases (CDK) [42].
CDK1 is the only CDK required for the G2-M transi- tion and regulates G1 progression and G1-S transition
[43]. In recent years, CDK1 has been suggested as a ther- apeutic target for cancer. CDK1 overexpression has been found in many cancers, including gastric cancer, ovarian cancer, oral squamous cell carcinoma, liver cancer, and breast cancer [44]. CDK1 inhibitors could be a potential
small-molecule drug. CDK1 inhibitor RO3306 could improve the efficacy of sorafenib treatment by targeting cancer stem cells in a preclinical model of hepatocel- lular carcinoma [45]. In addition, Several CDK1 inhibi- tors, including Rigosertib (phase II/III) and Zotaraciclib, have begun phase I clinical trials for treating pancreatic cancer and glioma [46, 47]. As a CDK1’s upstream reg- ulatory gene, ESCO2 took part in the control of the cell cycle. Hence, ESCO2 is likely to be a promising thera- peutic target in the future. Finally, single-cell analysis observed that ESCO2 was expressed not only in malig- nant cells but also in endothelial and immune cells, espe- cially proliferating T cells. The GSEA results also indicate that ESCO2 is involved in cancer inflammation control, including IL2 STAT5 signaling, which raises the possibil- ity that ESCO2 also controls the tumor immune micro- environment. Studies have shown that inhibition of cell cycle protein not only induce tumor cell cycle arrest, but also promote anti-tumor immunity. Inhibition of CDK4 /6, for example, increases levels of PD-L1 protein. More excitingly, combining CDK4/6 inhibitor with anti-PD-1 immunotherapy promotes tumor regression and signifi- cantly increases overall survival in mouse tumor models [48]. Therefore, research into ESCO2’s function in the tumor immune microenvironment is essential.
This study has the following limitations, though. First, this work does not apply any unique clinical cohorts to evaluate the diagnostic and prognostic significance of ESCO2 in cancer, despite its extensive use of sequencing data from public databases. Second, this study only per- formed ESCO2’s functional experiment in ccRCC cells; the upstream and downstream pathways of ESCO2 were not comprehensively examined, and the precise molecu- lar mechanism of ESCO2 regulation is still unknown. Third, we did not conduct experiments in vivo, which is an important issue. We should attempt to resolve this issue in the future.
In conclusion, ESCO2 is a potential biomarker and oncogene for pan-cancer that can accurately predict the cancer patient’s prognosis in ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, THYM, COAD, and READ. Furthermore, our bioinformatics results discov- ered that ESCO2 is involved in cell division and cell cycle regulation and verified that ESCO2 is essential for the proliferation and invasion of human ccRCC and BLCA cells in vitro. In a nutshell, ESCO2 is a potential and reli- able therapeutic target.
Abbreviations
| DEG | differentially expressed genes |
| ORR | objective response rate |
| QC | quality control |
| tSNE | t-Distributed Stochastic Neighbor Embedding |
| ROC | Receiver operating characteristic curve |
| GSEA | gene set enrichment analysis |
| KEGG | Kyoto Encyclopedia of Genes and Genomes |
TME tumor microenvironment
Supplementary Information
The online version contains supplementary material available at https://doi. org/10.1186/s12885-024-12213-w.
Supplementary Material 1
Supplementary Material 2
Supplementary Material 3 Supplementary Material 4
Acknowledgements
We thank Yubo Feng for assistance with the statistical methods.
Author contributions
Conceptualization: YZ, ZZ, YB, and JZ. Methodology: YH, DC, ZZ, QF, and BY. Software: DC, ZZ, QF, and YJ. Validation: YH, DC, YB, and YJ. Writing-original draft: YH and DC. Writing-review and editing: YZ, ZZ, YB, and JZ. Funding acquisition: ZZ, JZ. Supervision: YZ and ZZ. All authors reviewed and revised the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (grant numbers: 22176142 and 22076138), Young Elite Scientists Sponsorship Program by Tianjin (No. TJSQNTJ-2020-07).
Data availability
The data analyzed in this study can be downloaded from the GEO (GSE156632, https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal. gdc.cancer.gov/).
Declarations
Ethics approval and consent to participate
The authors confirm that all methods were conducted according to the principles of the Declaration of Helsinki and were approved by The Institutional Review Committee and the Medical Ethics Committee of the Second Hospital of Tianjin Medical University. Written informed consents have been obtained from all subjects.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1Tianjin Institute of Urology, The Second Hospital of Tianjin Medical
University, Tianjin, China
2Tianjin First Central Hospital Clinic Institute, Tianjin Medical University, Tianjin 300192, China
3Department of Hepatobiliary Surgery, Tianjin First Central Hospital,
School of Medicine, Nankai University, Tianjin 300192, China
Received: 4 April 2023 / Accepted: 1 April 2024
Published online: 11 April 2024
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