ESPL is elevated in Hepatocellular Carcinoma and Predicts Prognosis

doi:10.21203/rs.3.rs-1618197/v1

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ESPL is elevated in Hepatocellular Carcinoma and Predicts Prognosis

https://doi.org/10.21203/rs.3.rs-1618197/v1

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Background: Extra spindle pole bodies-like 1 (ESPL1) are associated with malignant biological behaviors in several tumors. Nevertheless, the correlation between hepatocellular carcinoma (HCC) and ESPL1 has not yet been confirmed. The objective of this paper is to analyze the molecular function together with the prognosis value of ESPL1 in HCC.

Methods: Firstly, we conducted next-generation sequencing on 121 HCC tissues and 119 normal adjacent normal tissues and collected clinicopathological and genetic data of HCC patients in The Cancer Genome Atlas (TCGA). Secondly, we verified the ESPL1 expression in another twenty pairs of HCC specimens by qRT-PCR. Subsequently, the prognostic value of ESPL1 was investigated through Cox univariate and multivariate regression analysis. Finally, weighted gene co-expression network analysis (WGCNA) was performed to define ESPL1-related co-expressed genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to analyze the process and pathway of ESPL1 in HCC. The prognostic value for the hub genes was explored through joint effect survival analysis.

Results: RNA-Seq and RT-qPCR data showed that raised ESPL1 in HCC tissues relative to normal tissues (P<0.001). Clinically, ESPL1 (HR=2.004, 95%CI=1.072-3.743, P=0.032), tumor size (HR=2.468, 95%CI=1.137-5.354, P=0.021) and advanced BCLC stage (HR=1.956, 95%CI=1.037-3.692, P=0.038) were independent prognostic factors for poor OS; ESPL1 ((HR=1.653, 95%CI=1.032-2.645, P=0.036) and advanced BCLC stage (HR=2.217, 95%CI=1.348-3.646, P=0.002) were independent prognostic factors for poor RFS. WGCNA showed the top 10 co-expressed genes associated with ESPL1 were GTSE1, KIF18B, BUB1B, GINS1, PRC1, KIF23, KIF18A, TOP2A, NEK2 and FANCD2. Enrichment analysis indicated that ESPL1 and its co-expression genes might be involved in the cell cycle and cell division of HCC. The joint effect survival analysis suggested that the mortality rate of HCC patients with all high expression of ESPL1, GTSE1, BUB1B, PRC1, KIF23, and TOP2A was approximately 3.37 times higher than that of patients with all low expression. Immune infiltration analysis demonstrated that infiltration by B cells, dendritic cells, and Treg cells were positively associated with ESPL1.

Conclusion: Our outcomes exhibit that ESPL1 might be associated with immunosuppression and probably is an effective prognostic indicator in HCC patients.

hepatocellular carcinoma

Extra spindle pole bodies-like 1

prognosis

According to Global Cancer Statistics 2020, the incidence rate of hepatocellular carcinoma (HCC) ranks sixth in the world's cancer, and the mortality rate is third [1]. Currently, surgery is considered to be the most effective treatment for patients with HCC, but clinicians have observed a high incidence of tumor relapse after surgery, which can affect posttreatment outcomes and survival[2]. In fact, early tumor recurrence (within 2 years after surgery) has been reported in 70% of all HCC patients across the world [3]. Furthermore, tumor heterogeneity in HCC patients contributes to increased resistance to conventional chemotherapy and targeted therapies [4]. Thus, it is imperative to establish new prognostic biomarkers and therapeutic targets in clinical treatment for HCC to improve patient survival rates.

ESPL1 is a Protein Coding gene, among its related pathways are Mitotic G1-G1/S phases and Cell Cycle, Mitotic. Additionally, ESPL1 is primarily involved in the anaphase process of sister chromatid cohesion release by cleaving the mucin subunit SCC1, which initiates the final separation of sister chromatids [5–7]. ESPL1 has also been found to be elevated in various types of cancers and to predict poor outcomes such as breast cancer [8], glioma [9], endometrial cancer [10]as well as bladder cancer. According to the Chinese Case-Control Study, the level of ESPL1 in the HCC group related to HBV is higher in comparison with the non-HBV-associated HCC group, serum ESPL1 can be applied as the biomarker for the monitoring of HCC recurrence related to HBV [11]. Nevertheless, genetic aspects and molecular functional analysis of ESPL1 in HCC have not yet been investigated.

In this study, we assessed the feasibility of applying ESPL1 as the latent HCC prognostic marker. Firstly, based on next-generation sequencing from the Guangxi cohort and TCGA database, we evaluated the expression level, prognostic value, biological function, and immune infiltration of the ESPL1 gene. Besides, Real-time quantitative-polymerase chain reaction (qRT-PCR) was used to detect mRNA expression in the HCC tissues and adjacent normal tissues. Our outcomes revealed that HCC patients with high expression of ESPL1 had a worse prognosis than patients with low expression of ESPL1. As a result, it can be speculated that ESPL1 is probably a promising biomarker of HCC and is conducive to the development of accurate treatment schemes.

2.1 Clinical tissue specimens

Tissue samples were gathered from 121 HCC patients who were admitted to the Hepatobiliary surgery, Guangxi Medical University Cancer Hospital from April 2018 to July 2019 for next-generation sequencing. We included patients who had received a pathological diagnosis of HCC and had undergone resection. There were no restrictions with respect to age and sex. We excluded HCC patients who were suffering from other known tumors, those who had very little tumor tissue, those who had received preoperative radiotherapy and chemotherapy, as well as those who refused to provide written informed consent. We further collected another 20 pairs of HCC tissues and adjacent normal tissues for qRT-PCR validation. All the specimens were maintained at -80℃ before use. This study was authorized through the Ethics Committee of the Cancer Hospital of Guangxi Medical University and carried out according to the principles of the Helsinki Declaration. All the patients received written informed consent for their anonymized data to be analyzed for research purposes.

2.2 RNA-seq

121 tumor tissues and 119 normal adjacent normal tissues were used for RNA sequencing. About 30 mg from the preserved frozen tissue samples and add them to EP tubes containing 1 mL of MagZol Reagent (Trizol lysis solution) to extract total RNA. First, use a 2100 Bioanalyser (Agilent) analyzer to detect the total RNA integrity (RNA integrity number, RIN), and the OD value of RNA in all tissue samples was detected by Epoch2 microplate spectrophotometer. In this study, the OD260/280 of the samples was between 1.9 and 2.0, and RIN ≥ 4 was set as the screening standard. Subsequently, using Ribo-Zero™ Magnetic Gold Kit (Human), the cytoplasmic and mitochondrial rRNA components in the total RNA of the tissue were removed, and the RNA was randomly cleaved into short template fragments of 200-500bp, reversed into cDNA, the cDNA library was successfully constructed. Then, the concentration of the successfully constructed sequencing library was measured by Qubit 2.0 dsDNA HS Assay (Thermo Fisher Scientific). Finally, the cDNA library samples were subjected to DNA analysis using an Agilent BioAnalyzer 2100 (Agilent) electrophoresis instrument. Agarose gel electrophoresis analysis was performed to detect the distribution of cDNA fragments. According to the 2 × 150 paired-end (PE) sequencing protocol provided by Illumina, the Illumina Hiseq2000 high-throughput sequencer was used to sequence the cDNA library to obtain the original sequencing data in FASTQ format.

2.3 RNA‑seq data quality control and analysis

In this study, the sequence alignment software Hisat was used to match the quality-controlled raw data with the human reference genome sequence. The differential exons of different genes were alternatively spliced to form the final RNA sequence, for each sample, the measured clean reads were compared with the annotation information of the human reference genome sequence (gencode v19), according to the number of reads that uniquely matched a gene and all reads that uniquely matched the reference genome sequence Quantity to determine the expression level of all genes matched on all samples. The number of reads aligned for a single gene was theoretically positively correlated with the expression of the gene. However, in the actual calculation process, the amount of gene expression was closely related to the sequencing depth and gene length. Therefore, a normalization method was usually used to calculate the actual expression level of a gene. The algorithm was FPKM. Subsequently, the differential expressed genes (DEGs) between tumor tissues and their neighboring normal tissues were screened using the “DESeq2” package of R software, and the |log 2 fold change| (|log₂FC|) value ≥ 0.6 and adjusted P < 0.05 regarded as the threshold.

2.4 qRT-PCR

TRIzol reagent (TaKaRa, Japan) was utilized for the extraction of total RNA from HCC and adjacent normal tissues, then reverse transcribe RNA into cDNA (Monad, China) according to the instructions, SYBR Green Master kit (Hieff, China) was applied to ESPL1 gene quantification, and DNA amplification was performed using the AriaMx Real-time PCR System (Agilent, America). The relative expression of the ESPL1 gene was determined using the 2- ∆∆ Ct method. for qRT-PCR, The reaction conditions were as below: 95℃ for 5 min, 40 cycles of 95℃ for 10s, 62℃ for 15s, and 72℃ for 20 seconds. β-actin was utilized as an internal reference: 5’-GTCATTCCAAATATGAGATGCGT-3’(F), GCTATCACCTCCCCTGTGTG-3’(R); The ESPL1 gene sequence: 5’-ATCTCTGTCAGTCGGACCTGCA-3’(F), 5’-CAGGTGGACCTTCTTCACAGAG-3’(R).

2.5 Genomic data mining

For the purpose of this study, we collected independent sets of genomic data and clinical parameters from public databases-The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.go; accessed on February 10, 2022) [12]. Employing the GEPIA (GEPIA; http://gepia.cancer-pku.cn/index.html; acquired on February 10, 2022) [13]website data, we compared gene expression patterns, tumor stages as well as prognostic survival analysis of ESPL1 in patients with HCC. In addition, ESPL1-expressed protein in HCC tissues and adjacent normal tissues was obtained from the Human Protein Atlas (HPA, https://www.proteinatlas.org/, obtained on March 1, 2022) [14].UALCAN database was applied to acquire the mutation frequency of the ESPL1 gene [15] (http://ualcan.path.uab.edu/index.html, obtained on March 1, 2022). Finally, the Gene Set Cancer Analysis (GSCA, https://www.proteinatlas.org/, acquired on March 2, 2022)[16] was employed to explore drug sensitivity and immune infiltration (Cancer Therapeutics Response Portal, CTRP; Genomics of Drug Sensitivity in Cancer, GDSC) of ESPL1 gene in HCC.

2.6 Weighted gene co‑expression network analysis

Based on the median cut-off value of the ESPL1 gene, the whole genome-wide expression data of HCC were classified as high and low expression groups. In order to identify genes expression differences between the two groups, “DESeq2” was used to screen DEGs and log2 fold change (log₂FC) > 1 or ≤-1, adjusted P < 0.05 were set as the threshold. Then, we conducted WGCNA to generate the gene co-expression network from the DEGs. First of all, we selected a soft threshold power with a scale-free R2 > 0.9 and slope near 1 to transform the adjacency matrix to a topological overlap matrix. With an aim of establishing networks and finding modules, the minimum size of the module, cutting height, and soft threshold power were set to 30, 0.25, and 3 respectively. The modules with high similarity were merged to create the compression network. Subsequently, we examined the correlation between modules and clinical parameters or the ESPL1 gene. In addition, we detected the genes in each module with high connectivity in the weighted co-expression network and calculated the Pearson correlation coefficients of the gene expression and the clinical parameters as gene significance (GS). Generally, when the values of modules were higher, the correlation between it and ESPL1 or clinical parameters was more powerful. The modules that correlate significantly with ESPL1 were considered as the interested modules, and modules were identified as having key genes when their |MM| > 0.8 and a |GS| > 0.2. Eventually, key genes in the interested modules were carried out to analyze biological processes and enrichment pathways using GO and KEGG.

2.7 Hub gene identification and prognostic analysis

Genes (weight > 0.2) in WGCNA modules, which significantly correlated with ESPL1 were inputted to Cytoscape software (version 3.8.2) to further analysis of PPI networks and hub gene selection [17]. Using the cytoHubba plugin in Cytoscape, the top 10 hub genes in the ESPL1 gene-related module were selected and visualized by the Degree method. Furthermore, We established a prognostic scoring model based on gene prognostic contribution coefficients(β) [18]. Meanwhile, the predictive prognostic ability of these genes was validated based on the TCGA dataset.

2.8 Statistical analysis

R 4.1.0 (https://www.r-project.org/) or SPSS 22.0 (IBM, Armonk, NY, USA) was applied for conducting statistical analyses. P-value less than 0.05 was viewed to reveal statistical significance. For each clinical parameter, we performed univariate and multivariate cox regression analyses. The paired or unpaired student's t-test was conducted for assessing the significance of differences between the groups. All graphics were visualized using PRISM8.3.0 (Graph Pad, San Diego, CA, USA) and R 4.1.0.

3.1 Comparison of baseline characteristics

According to the RNA-seq data, mRNA expression of the ESPL1 gene was evaluated in 121 tumors and 119 adjacent normal tissues from HCC patients in the Guangxi cohort. The median follow-up was 25 months, the mean age was 50.6 years old, 105 males (86.8%) and 16 females (13.2%). For validation, we download ESPL1 mRNA expression data and clinical parameter information from the TCGA dataset, including 374 tumor tissues and 50 adjacent normal tissues, after excluding patients whose survival time was 0 or unknown, we obtained 365 tumor samples and 50 adjacent normal tissue samples information. The median follow-up was 19.8 months, the mean age was 59.6 years old, 246 males (67.3%) and 119 females (32.7%).

3.2 ESPL1 is frequently upregulated in HCC

In our RNA-seq data, the ESPL1 expression in HCC tissues was remarkably higher in contrast to adjacent tissues and increased with the progression of BCLC stages (Fig. 1a-1c). For validation, ESPL1 expression was measured in 20 pairs of samples obtained from our hospital by qRT-PCR. Consistent with the RNA-seq data, the result implied that the expression level of ESPL1 was obviously elevated in HCC tissues compared to adjacent normal tissues (Fig. 1d). Besides, a similar result was also obtained in the TCGA cohort (Fig. 1e, 1f).

3.3 ESPL1 overexpression is associated with worse outcome

In our collection of HCC patients, the results of survival analysis suggested that high expression of ESPL1 in HCC was associated with worse outcomes than patients with low expression. (Fig. 2, Table 3). In addition, Multivariate Cox regression analysis suggested that ESPL1 gene (HR = 2.004, P = 0.032), tumor size (HR = 2.468, P = 0.021) progression BCLC stage (HR = 1.956, P = 0.038) were independent risk factors of poor OS (Table 2). ESPL1 gene (HR = 1.653, P = 0.036) and progression BCLC stage (HR = 2.217, P = 0.002) were independent risk factors of poor RFS. Analogously, high expression of ESPL1 (HR = 1.757, P = 0.005) was an independent prognostic factor of OS in the TCGA cohort. (Table S2).

Table 1

Clinical baseline characteristics of HCC patients in our cohort
Variable	N (%)
Sex
Male Female	105(86.8%) 16(13.2%)
Age (years)
≤ 65 > 65	108(89.3%) 13(10.7%)
BCLC stage
0-A B-C	62(51.2%) 59 (48.8%)
Edmondson grade
I-II	60(49.6%)
III-IV	57(47.1%)
Missing*	4(3.3%)
Tumor size
≤ 5cm	39 (32.2%)
>5cm	82 (67.8%)
Cirrhosis
Positive	49 (40.5%)
Negative	64 (52.9%)
Missing^#	8 (6.6%)
HbsAg
Positive	101 (83.5%)
Negative	20 (16.5%)
AFP(ng/mL)
≤ 400	75 (62.0%)
> 400	46(38.0%)
Note: Missing, information of Cirrhosis was unknown in 4 patients;Missing^#, information of Cirrhosis was unknown in 8 patients; BCLC stage, Barcelona Clinic Liver Cancer; AFP, Alpha-fetoprotein; ESPL1*: extra spindle pole bodies-like 1

Table 2

Univariate and multivariate analysis of OS and RFS in HCC patients based on our cohort
Variable	OS				RFS
	Univariate		Multivariate		Univariate		Multivariate
	HR(95%CI)	P	HR(95%CI)	P	HR(95%CI)	P	HR(95%CI)	P
ESPL1(high)	2.105(1.141–3.882)	0.017	2.004(1.072–3.743)	0.032	1.692(1.063–2.693)	0.027	1.653(1.032–2.645)	0.036
Sex(Male)	1.341(0.529–3.398)	0.537			1.063(0.529–2.138)	0.863
Age (>65years)	0.525(0.162–1.694)	0.281			0.529(0.213–1.315)	0.170
Tumor size(>5cm)	2.628(1.223–5.644)	0.013	2.468(1.137–5.354)	0.021	1.552(0.933–2.582)	0.091
BCLC Stage(B-C)	2.424(1.302–4.504)	0.005	1.956(1.037–3.692)	0.038	2.468(1.528–3.987)	< 0.001	2.217(1.348–3.646)	0.002
Cirrhosis(Positive)	0.809(0.432–1.516)	0.509			0.971(0.603–1.563)	0.903
HbsAg(Positive)	1.326(0.561–3.133)	0.521			1.395(0.716–2.720)	0.328
AFP(> 400ng/mL)	1.504(0.837–2.701)	0.172			1.610(1.014–2.557)	0.044	1.347(0.833–2.177)	0.225
Abbreviation: 95% CI, 95% confidence interval; HR, hazards ratio. OS, overall survival; RFS, recurrence-free survival; BCLC stage, Barcelona Clinic Liver Cancer; AFP, Alpha-fetoprotein

Table 3

**Prognostic values of top 10** ESPL1 **co-expressed genes in HCC based on our cohort**
Gene	Patients	OS
Gene	(n = 121)	NO. of events	HR (95%CI)	Adjusted P^*
ESPL1				0.033
Low	61	16	1
High	60	29	2.019(1.058–3.852)
GTSE1				0.004
Low	61	13	1
High	60	32	2.732(1.367–5.459)
KIF18B				0.055
Low	61	15	1
High	60	30	1.918(0.987–3.729)
BUB1B				0.034
Low	61	15	1
High	60	30	2.009(1.053–3.833)
GINS1				0.097
Low	61	16	1
High	60	29	1.723(0.907–3.273)
PRC1				0.039
Low	61	15	1
High	60	30	1.982(1.034–3.802)
KIF23				0.027
Low	61	14	1
High	60	31	2.119(1.090–4.120)
KIF18A				0.116
Low	61	16	1
High	60	29	1.687(0.879–3.237)
TOP2A				0.001
Low	61	12	1
High	60	33	3.124(1.547–6.308)
NEK2				0.136
Low	61	16	1
High	60	29	1.626(0.858–3.080)
Notes: Adjusted P^*, adjustment for BCLC stage, Edmondson grade, Age, Tumor size and AFP.

3.4 ESPL1 related co-expression analysis and prognostic analysis

To further explore the co-expression network of ESPL1 genes, we performed a WGCNA analysis, Firstly, A weighted gene network was constructed, and 3 was selected as the threshold β of a suitable adjacency matrix, and the obtained threshold satisfies the criterion that the network is close to scale-free. (Fig. 3a), Afterwards, a total of 5 modules were identified via the Merged dynamic (Fig. 3b), The association of modules with ESPL1 was drawn through a heatmap, the results suggested that the blue model was most positively associated with ESPL1 gene expression (r = 0.89, P < 0.001), while the green, brown and red modules were negatively correlated with ESPL1 gene expression (Fig. 4a, 4b). In the blue model, GO and KEGG ontologies and pathways were significantly enriched in cell cycle and cell division pathways (Fig. 5). Finally, the TOP10 hub genes of the blue model were identified, including GTSE1, KIF18B, BUB1B, GINS1, PRC1, KIF23, KIF18A, TOP2A, NEK2, and FANCD2 (Fig. 4c).

We also investigated the prognostic value of the top 10 ESPL1-related co-expressed hub genes in HCC. The multivariate survival analysis included the BCLC stage, Edmondson grade, Age, Tumor size and AFP as adjusted prognostic factors. After adjustment, it could be observed that the high G-2 and S-phase expressed 1(GTSE1)(Adjusted HR = 2.732, 95%CI = 1.367–5.459, Adjusted P = 0.033), BUB1B Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B) (Adjusted HR = 2.009, 95%CI = 1.053–3.833, Adjusted P = 0.034), protein regulator of cytokinesis 1(PRC1) (Adjusted HR = 1.982, 95%CI = 1.034–3.802, Adjusted P = 0.039), kinesin family member 23(KIF23) (Adjusted HR = 2.119, 95%CI = 1.090–4.120, Adjusted P = 0.027) and DNA Topoisomerase II Alpha (TOP2A) (Adjusted HR = 3.124, 95%CI = 1.547–6.308, Adjusted P = 0.001) levels were evidently associated with worse OS (Fig. 6b-6f. Table 3). The Joint effect prognosis analysis indicated that the mortality rate of HCC patients with all high expression of ESPL1, GTSE1, BUB1B, PRC1, KIF23 and TOP2A was approximately 3.376 times higher than that of patients with low expression. (Fig. 6g Table 4).

Table 4

Joint effect survival analysis
Gene	Patients	OS
Gene	(n = 121)	HR (95%CI)	Crude P	HR (95%CI)	Adjusted P^*
Low ESPL1&
Low GTSE1& Low BUB1B&	37	1		1
Low PRC1&
Low KIF23&
Low TOP2A
Not all high or low	46	1.285(0.532–3.103)	0.577	1.384(0.561–3.414)	0.481
High ESPL1&
High GTSE1& High BUB1B&
High PRC1&	38	3.742(1.676–8.358)	< 0.001	3.376(1.463–7.790)	0.004
High KIF23&
High TOP2A
Notes: Adjusted P^*, adjustment for BCLC stage, Edmondson grade, Age, Tumor size and AFP

ESPL is elevated in Hepatocellular Carcinoma and Predicts Prognosis

Construction of risk scoring model: risk score = ESPL1* -0.646 + GTSE1* -0.027 + BUB1B* 0.163 + PRC1* 0.575 + KIF23* 0.016 + TOP2A* 0.049. Based on the median risk score, 121 patients with HCC were divided into high- and low-risk groups. Kaplan–Meier survival analysis indicated that HCC patients with high-risk score had worse OS than patients with the low-risk score. (Fig. 7b, P = 0.0039). Furthermore, the time-dependent ROC curve results showed that the 1-, 2-, and 3-year AUCs were 0.71, 0.72 and 0.72, respectively. (Fig. 7c) Simultaneously, the prognostic model was also validated in TCGA dataset, survival analysis manifested that patients in the high-risk group had worse OS than the low-risk group(Fig. 7e, P = 0.013), moreover, the time-dependent ROC curve suggested that the 1-, 2-, and 3-year AUCs were 0.74, 0.67 and 0.64 (Fig. 7d-7f).

3.5 Relationship between ESPL1 and immune infiltration and drug sensitivity

To determine the kinds of the immune cells that were in relation with expression of ESPL1 in HCC, we obtained the data from the GSCA platform, the outcomes reflected that ESPL1 was observably positively associated with B cells, CD8 naïve cells, dendritic cells (DCs), effector memory cells, Tr1, iTreg and nTreg cells. However, the opposite trend was observed in the cells of CD4_naive, Cytotoxic, MAIT, Macrophage, Monocyte, NK, Tfh, Th17 (Fig. 8a). Moreover, the mutation rate of ESPL1 in HCC was 7% (Fig. 8b). Additionally, adjacent normal tissues possessed a lower ESPL1 protein expression than the HCC tissues (Fig. 8c, 8d). Finally, we observed that ESPL1 gene expression was positively correlated with 17-AAG, RDEA119, and Trametinib drugs sensitivity (Fig. 9).

HCC is considered to be one of the most prevalent malignant tumors that threatens human health, in recent years, the age of onset tends to be younger, mainly concentrated between 40–60, in our data, the mean age of patients with HCC was 50.6 years old, the younger the age at onset of HCC, the worse the prognosis[19, 20].

Using samples from HCC patients admitted to Guangxi Medical University Cancer Hospital, we performed next-generation sequencing and validated our findings using qRT-PCR and the TCGA database. Consistently, in both the Guangxi cohort and the TCGA-validated cohort, ESPL1 was highly expressed in tumor tissues compared to adjacent tissues. As described in previous studies, it showed that overexpression of ESPL1 was associated with tumor progression, such as endometrial cancer[10], glioma[9], chronic myeloid leukemia[21], and breast cancer[8]. However, the opposite trend was reported in gastric carcinoma [22]. It demonstrated that the expression of ESPL1 varies among different tumors, but was frequently expressed in most tumor tissues. Clinically, ESPL1 overexpression is correlated with poor outcomes and advanced tumor stages, suggesting that ESPL1 could be used in predicting tumor malignancy. In addition, the results of multivariate and univariate survival analysis exhibited that the ESPL1 was probably a promising biomarker for the prognosis of HCC.

The enriched analysis of co-expressed genes associated with the ESPL1 gene showed that they might be involved in the cell cycle pathways, mitotic sister chromatid segregation, and cell division of HCC. Cancer cells often have upregulated levels of CDKs for progression through the cell cycle[23], therefore, the development of drugs inhibiting CDKs has become a novel strategy for treating the tumors[23, 24]. After selecting the top 10 co-expression genes, prognostic analyses were performed, and the results suggested that ESPL1, GTSE1, BUB1B, PRC1, KIF23 and TOP2A genes might be unfavorable for the prognosis of HCC. GTSE1 is expressed specifically during the G2 and S phases of the cell cycle [25], A previous study has identified that GTSE1 is a key regulator involved in mitosis [26, 27]. Lai et al found that the GTSE1 promoted cell proliferation by upregulating FOXM1 expression in prostate cancer and was associated with an unfavorable prognosis.[28] The mechanism of the GTSE1 gene has also been reported in bladder cancer, according to Liu et al, high GTSE1 levels may induce the transfer of p53 to the cytoplasm, thus regulating FOXM1/CCNB1 expression. [29]. As for breast cancer and hepatocellular carcinoma, GTSE1 is associated with poor prognosis, study also suggested that it can improve the ability of proliferative and metastatic through upregulating the expression of EMT markers.[30–32]. Another research has shown that knockdown GTSE1 increases the sensitivity of non–small-cell lung cancer to radiation through a DNA damage repair pathway [33]. BUB1B, a member of the BUB1 proteins, plays a critical role in chromosome segregation[34], and aberrant expression of BUB1B had been reported in various cancers including lung adenocarcinoma[35], glioblastoma[36] and ductal breast carcinoma[37]. Tang et al indicated that the expression of BUB1B increased in multiple myeloma patients, and was closely correlated with poor outcomes [38]. Additionally, BUB1B can upregulate the mTORC1 signaling pathway and participate in proliferation, migration, invasion, EMT, and metastases in HCC [39]. Additionally, Jiao et al. reported that BUB1B promotes cholangiocarcinoma proliferation and invasion via JNK-c-Jun signaling [40]. PPRC1, a microtubule-associated protein, is involved in cytokinesis. It has already been documented that PRC1 is overexpressed in different cancers, including pancreatic cancer [41], breast cancer[42] and bladder cancer[43]. A study of hepatocellular carcinoma has been reported that PRC1 exerts an oncogenic effect by promoting cancer proliferation, stemness, metastasis and oncogenesis. Mechanistically, PRC1 can reinforce Wnt signaling pathway to promote early HCC recurrence [44]. Zhan et al. also demonstrate a similar description that PRC1 facilitates the progression of lung cancer through the activation of Wnt signaling pathway [45]. As for oral squamous cell carcinoma, PRC1 is strikingly related to low differentiation, metastasis and high-clinical stage. The data indicated that it can significantly promote cell cycle transition and diffusion via the signaling pathway of p53/PRC1/EGFR [46]. KIF23 is a type of nuclear protein that exerts a critical effect in modulating cytokinesis [47, 48] and its dysregulated expression has been observed in diverse cancers, which leads to poor outcomes, such as Glioma[49], gastric cancer[50], Hepatocellular carcinoma[51, 52], renal cell carcinoma[53], pancreatic ductal adenocarcinoma[54], lung cancer[55], colorectal cancer[56], ovarian cancer[57, 58]and breast cancer[59, 60], these studies demonstrated that KIF23 overexpression is widely involved in tumor progression. TOP2A is an essential nuclear enzyme that regulates the state of DNA during transcription and is participated in the processes of chromatid separation [61]. Increasingly researches have been suggested that TOP2A is evidently expressed in tumor tissues. Kim et al confirmed that elevated expression of TOP2A was indicative of progression and a high rate of recurrence in non-muscle-invasive bladder cancer [62]. In addition, TOP2A is also involved in many other important biological behaviors, such as proliferation and invasion, which play pivotal roles in tumor initiation and progression[63], including bladder urothelial carcinoma[64], pancreatic cancer[65], ovarian cancer[66] and hepatocellular carcinoma[67, 68]. In general, the prognostic model established by six genes has a good prediction potential for the overall survival of patients with HCC.

More and more reports suggest that the interaction and signaling between liver malignant cells and the immune microenvironment contribute to tumorigenesis and progression. This immunosuppressive microenvironment can induce tolerance and promote tumor proliferation, invasion as well as metastasis [69–73]. In this study, we also assessed the correlation between ESPL1 expression and immune cell infiltration in HCC, The results showed that ESPL1 gene expression was positively correlated with B cells, CD8 naïve cells, dendritic cells (DCs), effector memory cells, Tr1, iTreg and nTreg cells. Numerous studies have revealed that TME in cancer patients is associated with a raised Treg cells number in circulation [74, 75], increased FOXP3-Tregs have been found in concordance with cancers prognosis and progression in ovarian cancer[76] and uveal melanoma[77].

In conclusion, this research evaluated the ESPL1 expression, prognostic value and its correlation with the infiltration of immune cells in HCC. Even so, we must consider our results in the light of certain limitations, the data utilized in this study were based only on the genetic perspective and lacked validation at the protein level. In addition, increasingly in vitro and in vivo experiments should be considered to demonstrate the molecular function of ESPL1 and further mechanistic exploration.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

SR participated in data curation, formal analysis and original draft. HJT was involved in experiments and data curation. YCL was involved in editing. LYK was involved in data curation and formal analysis. ZGH participated in data curation. XBD reviewed, revised the manuscript, and approved the submitted version.

FUNDING

This work was supported by grants from the National Natural Science Foundation of China (81960450), National Major Special Science and Technology Project (2017ZX10203207), High-level Innovation Team and Outstanding Scholar Program of Guangxi Colleges and Universities, “139” Projects for Training of High-level Medical Science Talents from Guangxi, Key Research and Development Project of Guangxi (AA18221001, AB18050020, 2020AB34006), Key Laboratory of Early Prevention and Treatment for Regional High-Frequency Tumors, Ministry of Education of Guangxi, Independent Research Projects (GKE2017-ZZ02, GKE2018-KF02, GKE2019-ZZ07), and Development and Application of Medical and Health-appropriate Technology in Guangxi (S2019039).

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ESPL is elevated in Hepatocellular Carcinoma and Predicts Prognosis

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Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Clinical tissue specimens

2.2 RNA-seq

2.3 RNA‑seq data quality control and analysis

2.4 qRT-PCR

2.5 Genomic data mining

2.6 Weighted gene co‑expression network analysis

2.7 Hub gene identification and prognostic analysis

2.8 Statistical analysis

3 Results

3.1 Comparison of baseline characteristics

3.2 ESPL1 is frequently upregulated in HCC

3.3 ESPL1 overexpression is associated with worse outcome

3.4 ESPL1 related co-expression analysis and prognostic analysis

3.5 Relationship between ESPL1 and immune infiltration and drug sensitivity

4 Discussion

Declarations

References

Supplementary Files

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