Introduction
Hepatocellular carcinoma (HCC) accounts for 75–85% of primary liver cancers and is associated with high morbidity and mortality.1 Due to the high propensity for recurrence and metastasis, the overall survival of patients with advanced HCC is extremely poor. Every year, more than 500,000 deaths are attributable to HCC worldwide.2,3 Efforts have been made to understand the mechanism of the development, progression, and metastasis of HCC. Use of serum markers and advanced medical imaging techniques can facilitate early diagnosis of HCC.4–6 Besides, molecular targeted therapy, chemotherapy, immunotherapy, and surgical resection are effective treatment methods for HCC.7,8 However, the molecular characteristics of HCC are not well characterized. The lack of specific markers for tumor type or disease stage poses a significant challenge in the understanding and treatment of HCC.
Mitosis, the basic form of cell division, is a common biological process in eukaryotes. During mitosis, the spindle ensures the division of chromosomes into two equal groups of sister chromatids.9 The spindle and kinetochore-associated (SKA) complex is composed of SKA1/2/3 proteins and maintains the attachment of intermediate spindle microtubules to the centromere, thus ensuring the completion of mitosis.10,11 Dysfunction of the SKA complex leads to chromosomal congression failure and subsequent cell death.12 Recent studies have shown that abnormal cell cycle and uncontrolled cell proliferation can be attributed to the overexpression of SKAs; moreover, studies have also suggested a potential role of SKAs in the genesis, progression, and chemotherapeutic resistance of various types of tumors.13–18 In an in vitro study, G2/M blockade of pancreatic cancer was inhibited by upregulating the SKA1 gene, which enhanced pancreatic cancer aggressiveness and malignancy.15 SKA2 expression was found to be significantly upregulated in esophageal squamous cell carcinoma (ESCC) samples and inhibition of SKA2 mRNA level suppressed ESCC cell proliferation and migration.19 SKA3 overexpression accelerated the cell cycle by activating the PI3K-Akt signaling, thereby promoting cervical cancer cell migration and proliferation.14 However, the expression profile, molecular biological function, and prognostic significance of SKA in HCC have not been fully elucidated.
In our research, we carried out comprehensive bioinformatics analysis of the expression patterns of SKA family genes in HCC, investigated their association with immune infiltration, assessed their diagnostic and prognostic relevance in HCC. We observed markedly high expression of SKA family genes in HCC, which simultaneously showed good diagnostic significance for HCC. Interaction analysis (i.e. protein interaction and gene interaction) and enrichment analysis showed that SKA family members were mainly related to microtubule motor activity, mitosis, and cell cycle. Comprehensive immune infiltration analysis showed that all SKA family members were correlated with immune cell subsets, especially Th2 cells. We further demonstrated an inverse association between increased expression of SKA family members and the prognosis of HCC patients and identified SKA1 as an independent predictor of HCC patients. Further functional assays indicated that knockdown of SKA1 decreased the proliferation and invasion of HCC cells. Our research aimed to elucidate the specific functions and mechanisms of SKA family genes in the development of HCC from a new perspective; our findings may facilitate a better understanding of the pathogenetic mechanism of HCC and offer insights for the development of SKA-targeted tumor immunotherapy.
Methods
Data resource
We downloaded level three data of liver hepatocellular carcinoma (LIHC) patients from The Cancer Genome Atlas (TCGA) database (cancergenome.nih.gov/), which contains data of 374 samples of HCC and 50 samples of para-carcinoma tissue. The workflow type we chose was HTSeq-FPKM. Corresponding clinical information was also obtained from the TCGA data portal. For the sake of subsequent analysis, we converted the HTSeq-FPKM data into transcripts per million (TPM) reads.
Gene expression profiling interactive analysis (GEPIA)
GEPIA is an online tool for analyses and visualization of RNA sequencing data of 9,700 tumors and 8,500 normal tissues.20 In this study, transcriptional levels of SKA family genes in 20 distinct cancer samples and nonneoplastic control samples were acquired from GEPIA. Besides, we used a similar gene detection function in the “expression analysis module” to identify the most similar genes of SKA family genes.
Integrative Molecular Database of Hepatocellular Carcinoma (HCCDB)
HCCDB is a specialized database that integrates multiple databases including Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), etc. It contains expression spectrum analysis of more than 3,000 HCC samples (lifeome.net/database/hccdb/).21 We applied this comprehensive database to identify the expression of SKA family genes in liver cancer.
ONCOMINE
ONCOMINE database (oncomine.org) is an online data-mining tool that provides an integrated analysis of genome-wide expression in a wide variety of tumor samples and normal control samples.22 We compared the transcription levels of SKA family members in different HCC samples and normal adjacent tissues. Statistical significance was considered at p-values <0.05. The fold change in our study was set to 2, and thresholds for statistical significance were set at 10%.
STRING
STRING (string-db.org/) is a search tool that predicts a network of genes and proteins that may have an interacting association.23 A protein-protein interaction (PPI) network analysis of SKAs and their most similar genes were conducted using the STRING and further processed using the visualization tool Cytoscape.
GeneMANIA
GeneMANIA (genemania.org) is a visual database tool with highly accurate prediction algorithms providing information on physical interactions, coexpression, genetic interactions, and co-localization of query genes.24 We used GeneMANIA to measure the predictive value of SKA family members.
Functional enrichment analysis
To identify the gene ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways where SKAs and similar genes were enriched, functional enrichment analysis was carried out using the R package cluster profiler.25 Enrichment analysis predicts the functional roles of SKA family genes and their similar genes based on three aspects, i.e., biological process, cellular component, and molecular function, while KEGG analysis defines the related pathways of SKA family genes and their similar genes.
Immune infiltration analysis
The infiltration of 24 tumor-infiltrating immune cells in HCC samples was quantified by single sample gene set enrichment analysis (ssGSEA) methods using the GSVA R package. We scored the enrichment of every immunocyte based on gene signatures unique to 24 tumor-infiltrating lymphocytes (TILs), including B cells, T cells, macrophages, neutrophils, etc. Spearman correlation analysis was used to evaluate the correlation between SKAs and the tumor immune infiltration.
Kaplan-Meier plotter
Kaplan-Meier plotter is an online tool for survival analysis of cancer patients, and it was used to assess the prognostic significance of the expression of SKA family gene mRNA in HCC patients.26,27 Survival outcomes included overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). The optimal cutoff value was set through the KM plotter algorithm. P-values < 0.05 were considered significant.
Cell culture and transfection
Normal human liver cell line (L-02 cells) and HCC cell lines (SMMC-7721, LM3, and Hep3) were used in our experimental study. They were purchased from the China Cell Bank (ATCC, Shanghai, China). All cell lines were grown in Dulbecco’s Modified Eagle Medium (Gibco, Carlsbad, CA, USA) with 10% fetal bovine serum (FBS; Ausbian, Sydney, Australia) and 1% penicillin-streptomycin (Gibco, Carlsbad, CA, USA). Cells were maintained in an incubator at a constant temperature of 37°C with 5% CO2. siRNA against SKA1 (5′-GCAUGUCAAGGAGCACCACAATTUUGUGGUGCUCCUUGACAUGCTT-3′) and short interfering noncoding oligonucleotides (5′-UUCUCCGAACGUGUCACGUTTACGUGACACGUUCGGAGAATT-3′)
were synthesized by Sangon Biotech (Sangon, Shanghai, China) and transfected into cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) following the product instructions. After 3 days of transfection, the cells were collected to determine the knockdown effects.
RNA extraction and quantitative real-time PCR
Total RNA was isolated from cultured cells by Trizol reagent (Takara, Dalian, China). SYBR Premix Ex Taq II in a PCR detection system (Bio-Rad, Hercules, CA, USA) was used to assay the expression of target genes. cDNA was synthesized using PrimeScript RT reagent kits (Takara Dalian, China). The transcriptional levels were normalized to those of the internal control gene GAPDH. The sequences of all target genes are shown in Supplementary Table 5.
Western blotting
The protein concentration of cells lysates were measured with a Bradford protein assay. Total proteins were isolated by 8% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes. After being blocked with 5% bovine serum albumin at room temperature for 3 h, the membranes were incubated overnight at 4°C with anti-GAPDH (1:1,000; Proteintech, Chicago, IL, USA) and anti-SKA1 (1:1,000; Affinity, Cincinnati, OH, USA) primary antibodies. The membrane was then washed with phosphate buffered saline Tween and incubated with secondary antibodies (1:4,000) for 2 h The protein bands were visualized with an enhanced chemiluminescence detection kit (Biosharp, Hefei, China).
Cell proliferation and invasion assays
Cell Counting Kit-8 (CCK-8) and colony formation assays were performed to assess cell proliferation. Cells maintained in 96-well plates were incubated with 10 µL CCK-8 solution (Dojindo, Kumamoto, Japan) at 37°C for 2.5 h. Cell viability was then determined by at 450 nm absorbance. To assay colony formation, about 2,500 HCC cells were seeded in six-well plates and cultured for 11 days. The colonies were then fixed in 4% paraformaldehyde and stained with crystal violet. Transwell assays were carried out to assess cell invasion. Cell migration was assayed in about 6×103 transfected HCC cells that were inoculated in serum-free medium on the Matrigel-coated upper surface of an 8 µm Transwell chamber (Corning, NY, USA). The culture medium in the lower chamber was supplemented with 350 µL of medium containing 10% FBS. The cells that had migrated through the membrane were stained with 0.5% crystal violet and counted by light microscopy.
HCC tissue microarray and immunohistochemical (IHC) staining
Human HCC tissue microarrays (Cat No. IWLT-N-64LV41) containing 10 pairs of HCC and adjacent normal tissues were obtained from Wuhan Saiweier Biotechnology Co; Wuhan, China), and CDT1 expression was assayed by IHC staining using an anti-SKA1 antibody (1:250; Affinity, Cincinnati, OH, USA). And following the kit manufacturer’s instructions. The H-score method was used to assess the expression of SKA1 protein. Positivity and H-scores (0–300) were reported as (1×% of cells with weak staining intensity) + (2×% of cells with moderate staining intensity) + (3×% of cells with strong staining intensity).
Statistical analysis
The statistical analysis was performed with R version 3.6.3 (www.r-project.org ). One-way analysis of variance was used to compare differences among the three groups, and between-group differences were compared using t-tests. Correlations of SKA expression and the clinical parameters of HCC patients were evaluated by the Wilcoxon signed-rank test. Univariate and multivariate Cox analysis was used to evaluate the prognostic significance of SKAs levels in terms of OS and DSS. Correlations of SKA family genes were assessed with Spearman’s correlation coefficient. The performance of differentially expressed SKA family genes in distinguishing between HCC samples and normal liver samples was assessed by receiver operating characteristic (ROC) curve analysis using the pROC package (version 1.16.2).28 An area under the ROC curve (AUC) >0.7 and from 0.5–0.7 indicated good and poor accuracy, respectively. The H-scores of HCC and adjacent healthy tissues were compared using paired t-tests. Statistical significance was defined as p <0.05.
Results
Transcription of SKAs in HCC patients
There is accumulating evidence that SKAs are novel tumor biomarkers.29,30 However, transcriptional analysis and prognostic significance of SKA genes in human HCC have not been well elucidated. Therefore, GEPIA was used to compare the transcription levels of SKA family genes between normal and tumor samples of 33 different cancers. The mRNA levels of SKA family genes were significantly increased compared with normal tissues in the analysis of HCC tissues (p<0.001, Supplementary Fig. 1).
We further compared the transcriptional levels of SKAs between HCC samples and normal control samples in the HCCDB database. The results of the ICGC GEO dataset (GSE22058, GSE22097, GSE54236, GSE64041, GSE25097, and ICGC-LIRI-JP) analysis all suggested abnormally high expression of SKA1/2/3 in HCC (Fig. 1). We also compared the differences of transcription levels of SKAs between HCC cancer samples and normal samples in the TCGA database. The mRNA levels of SKAs were markedly increased in HCC samples relative to normal liver samples, which was consistent with our previous result (Fig. 2A). The observation was also verified in paired HCC and normal tissues (Fig. 2B). We also compared the transcriptional levels of SKA family members between tumor tissues and normal control tissues in ONCOMINE databases. Specifically, the results from Chen datasets showed upregulation of SKA1 in HCC samples compared with that in normal samples, with fold changes of 1.212–2.534.31–33 In Wurmbach datasets, HCC tissues showed a 1.266–1.331-fold increase in the mRNA expressions of SKA2 and SKA3 (Fig. 2C).33 Finally, differences in the expression of SKAs were validated by qRT-PCR in normal human liver cell line and HCC cell lines (Fig. 2D).
Correlation and molecular interactions of SKAs in HCC
Correlation between the expression levels of SKA family members in HCC was assessed using Spearman correlation analysis. Scatter plot results indicated a strong correlation between the transcriptional levels of SKA1/3 (r=0.890). Besides, there was a moderate correlation between SKA1 and SKA2 (r=0.590) and between SKA2 and SKA3 (r=0.640, Fig. 3A–C).
Through GEPIA, we identified genes whose expression patterns were similar to those of differentially expressed SKAs in HCC patients. The results of similar gene detection are presented in Supplementary Table 1. To elucidate the potential interactions among SKAs and their most similar genes, we further built a PPI network by STRING dataset analysis and Cytoscape visualization (Fig. 3D). The SKA family genes and their similar genes were associated with microtubule binding, microtubule motor activity, cytoskeletal protein binding, and the cell cycle. The gene-gene interaction network through GeneMANIA also revealed that SKAs and their associated genes (e.g., NUDT5, SPC24, DSN1, NDC80, CENPE, and BUB1) were primarily related to chromosome segregation, mitosis, nuclear division, and microtubule polymerization or depolymerization (Fig. 3E).
GO and KEGG enrichment analyses of SKA family members and their most similar genes
To further investigate the potential mechanisms of SKAs in HCC, GO, and KEGG enrichment analyses were carried out to probe the functions and pathways of SKA family proteins and their most similar genes using the “ClusterProfiler” package in R software. The biological processes for these genes were predominantly enriched in mitotic nuclear division, nuclear division, organelle fission, and microtubule cytoskeleton organization. The molecular functions were mainly microtubule motor activity, motor activity, microtubule binding, and ATP-dependent microtubule motor activity. In terms of the cellular component category, the overexpressed SKA family members and their similar molecules were mainly associated with spindle, chromosome (centromeric region), mitotic spindle, and chromosomal region (Fig. 3F–H). The results of KEGG enrichment revealed several major KEGG pathways of SKAs and their similar genes, and included the p53 signaling pathway, cell cycle, and cell senescence (Fig. 3I). All the enriched pathways were tightly associated with the occurrence and progression of malignant tumors (Supplementary Table 2).34,35
Correlation between SKAs levels and immune infiltrates
The complexity of immune cells in the tumor microenvironment influences the biological behavior of the tumor, prognosis, and outcomes of immunotherapy.36,37 Therefore, we investigated the correlation between various immune cells infiltrating in the HCC microenvironment and SKA family members. All members of the SKA family showed a positive correlation with T helper 2 (Th2) cells [SKA1 (r=0.752, p<0.001), SKA2 (r=0.438, p<0.001), SKA3 (r=0.718, p<0.001)], T follicular helper cells (Tfhs) [SKA1 (r=0.235, p<0.001), SKA2 (r=0.126, p=0.015), SKA3 (r=0.218, p<0.001)], and T helper (Th) cells [SKA1 (r=0.203, p<0.001), SKA2 (r=0.178, p<0.001), SKA3 (r=0.261, p<0.001)], as well as a negative correlation with neutrophils [SKA1 (r=−0.308, p<0.001), SKA2 (r=0.224, p=0.015), SKA3 (r=0.343, p<0.001)] and dendritic cells (DCs) [SKA1 (r=−0.272, p<0.001), SKA2 (r=−0.326, p<0.001), SKA3 (r=−0.328, p<0.001)]. The correlation with Th2 was the most significant (Fig. 4A–C). We further compared the enrichment score of Th2 cells of HCC samples in SKA1-high and SKA1-low groups. Consistently, the results demonstrated that high SKA1 expression samples had a higher enrichment score of Th2 cells than those in low SKA1 expression samples (Fig. 4D–F). All the above analyses indicated the significant correlation between SKA family members and immune cell subsets, especially Th2 cells.
Relationship of mRNA levels of SKAs and clinicopathological features of HCC patients
We further assessed the correlation between transcriptional levels of SKA genes and clinicopathologic features [such as histologic grade, T stage, and α-fetoprotein (AFP)] of HCC patients. The tissues obtained from HCC patients tended to express higher levels of SKAs mRNA as cancer stages advanced [SKA1, (G4 vs. G1, p<0.001), SKA2, (G3 vs. G2, p<0.001), SKA3, (G4 vs. G1, p<0.001)]. The highest mRNA levels of SKA family genes were predominantly found in grade 3 and grade 4 HCC (Fig. 5A–C). T stage has an important prognostic significance. Patients with high-T stage tumors tended to express higher mRNA levels of SKA1/2/3 according to the pathological T stage criterion (p<0.05) (Fig. 5D–F). AFP is a specific diagnostic biomarker for liver cancer. Therefore, we assessed the correlation between SKAs expression and AFP level. As expected, there were significant differences between the high-AFP group and the low-AFP group based on these SKAs expression (p<0.001, Fig. 5G–I). We further analyzed the association of SKAs expression with other clinical characteristics (e.g., age, sex, TNM stage, AFP, Child-Pugh grade, fibrosis Ishak score, vascular invasion, albumin, and prothrombin time) in HCC patients (Supplementary Table 3). High SKA1 expression was significantly linked to T stage, AFP, and prothrombin time; high SKA2 expression was significantly linked to age, T stage, AFP, and prothrombin time; high SKA3 expression was significantly linked to T stage and AFP (p<0.05). The findings indicated the potential prognostic significance of the expression of SKA family members in patients with HCC.
Diagnostic and prognostic value of SKAs expression in HCC patients
To identify the diagnostic role of mRNA expression of SKAs for HCC, the R statistics pROC package was used to construct ROC curves based on transcriptome sequencing and clinical data derived from the TCGA database. The diagnostic potential of mRNA expression of these SKAs for HCC was assessed based on AUC (Fig. 6A–C). The AUCs, optimal cutoff values sensitivity, specificity and Youden index for predicting HCC are shown in Supplementary Table 4. Statistical analysis of the differences in the AUCs of SKAs suggested that the AUC of SKA1 (0.982, 95% CI: 0.970–0.994) was the largest, followed by SKA3 (0.973, 95% CI: 0.957–0.989) and SKA2 (0.887, 95% CI: 0.852–0.922). We also assessed the diagnostic value of SKA expression for multiple clinical features of HCC patients, such as the pathological stage and TNM stage (Supplementary Fig. 2). The results showed that SKA expression had a certain significance in the diagnosis of TNM staging and pathological staging, which implied that the transcriptional levels of SKAs had a relatively greater sensitivity and specificity for the diagnosis of HCC. Furthermore, we analyzed the influence of the expressions of SKAs on the OS, PFS, and DSS to assess the prognostic significance of SKAs in HCC patients by Kaplan-Meier plotter analysis. The OS of patients with high expressions of SKA1/2/3 was remarkably lower than that of patients with low expression of the corresponding SKAs (p<0.01, Fig. 6D). In addition, increased mRNA expression of SKA1/2/3 were associated with poor PFS and DSS (p<0.05, Fig. 6E–F). The findings indicated the potential prognostic significance of SKA mRNA levels in HCC patients. We further assessed the prognostic value of SKA1/2/3 using univariate and multivariate Cox proportional hazards regression analysis (Table 1). RNA sequencing and clinical data of 374 HCC patients were Level 3 data of the LIHC project in the TCGA database. Univariate analysis showed an association of expression of SKA1/3 and pathologic stage with poor OS and DSS. The Child-Pugh grade was also associated with poor DSS. In multivariate analysis, high transcriptional levels of SKA1 (HR=2.047, 95% CI: 1.211–3.459, p=0.007) and high pathologic stage (HR=1.920, 95% CI: 1.308–2.818, and p<0.001) were independent predictors of a significantly shorter OS of HCC patients. The expression of SKA1 mRNA, Child-Pugh grade, pathologic stage, and race were independent predictors of DSS. To sum up, we identified transcriptional levels of SKA1 as an independent predictor of OS and DSS of HCC patients.
Table 1Cox regression analyses of variables for OS and DSS in LIHC patients
| Characteristics | OS
| DSS
|
|---|
Univariate analysis
| Multivariate analysis
| Univariate analysis
| Multivariate analysis
|
|---|
| Hazard ratio (95% CI) | p-value | Hazard ratio (95% CI) | p-value | Hazard ratio (95% CI) | p-value | Hazard ratio (95% CI) | p-value |
|---|
| Age (>60 vs. <=60) | 1.205 (0.850–1.708) | 0.295 | | | 0.846 (0.543–1.317) | 0.458 | | |
| Gender (Male vs. Female) | 0.793 (0.557–1.130) | 0.200 | | | 0.813 (0.516–1.281) | 0.373 | | |
| Race (White vs. Asian and Black or African American) | 1.265 (0.881–1.816) | 0.203 | | | 1.547 (0.964–2.481) | 0.071 | 2.465 (1.113–5.458) | 0.026 |
| Pathologic stage (Stage II and Stage III and Stage IV vs. Stage I) | 2.090 (1.429–3.055) | <0.001 | 1.920 (1.308–2.818) | <0.001 | 2.909 (1.718–4.925) | <0.001 | 2.101 (1.043–4.233) | 0.038 |
| AFP (ng/ml) (>400 vs. <=400) | 1.075 (0.658–1.759) | 0.772 | | | 0.867 (0.450–1.668) | 0.668 | | |
| Fibrosis Ishak score (1/2 and ¾ and 5/6 vs. 0) | 0.772 (0.465–1.281) | 0.316 | | | 0.913 (0.474–1.757) | 0.784 | | |
| Vascular invasion (Yes vs. No) | 1.344 (0.887–2.035) | 0.163 | | | 1.277 (0.707–2.306) | 0.418 | | |
| Child-Pugh grade (B and C vs. A) | 1.643 (0.811–3.330) | 0.168 | | | 2.560 (1.123–5.834) | 0.025 | 3.090 (1.273–7.499) | 0.013 |
| SKA1 (High vs. Low) | 1.911 (1.341–2.724) | <0.001 | 2.047 (1.211–3.459) | 0.007 | 2.505 (1.566–4.008) | <0.001 | 2.872 (1.266–6.513) | 0.012 |
| SKA2 (High vs. Low) | 1.241 (0.878–1.752) | 0.221 | | | 1.489 (0.954–2.325) | 0.080 | 0.534 (0.240–1.189) | 0.124 |
| SKA3 (High vs. Low) | 1.545 (1.090–2.188) | 0.014 | 0.900 (0.542–1.494) | 0.683 | 1.778 (1.132–2.795) | 0.013 | 1.283 (0.536–3.071) | 0.576 |
SKA1 knockdown inhibited the in vitro tumorigenicity of HCC cells
In the previous analysis, SKA1 was confirmed as an independent prognostic factor for HCC. To further validate the role of CDT1 in HCC, we performed IHC staining on HCC tissue microarrays. The results of the scatter plot analysis by paired t-tests revealed that SKA1 expression in HCC tissues was significantly higher than that in para-carcinoma tissues (Fig. 7A). The functional assays were also performed in LM3 and Hep3B cell lines. Western blot detection showed that SKA1 was markedly knocked down by SKA1 siRNA (Fig. 7B). CCK-8 and colony formation assays demonstrated that CDT1 knockdown significantly inhibited the proliferation and invasiveness of LM3 and Hep3B cells (Fig. 7C, E). CDT1 knockdown also significantly inhibited the invasiveness of HCC cells in Transwell assays (Fig. 7D). The results indicated that SKA1 knockdown suppressed the proliferation and invasiveness of HCC cells.
Discussion
HCC is associated with high mortality and morbidity rates and accounts for approximately 75–85% of all primary liver cancers worldwide. The poor 5-year survival makes it the second most common cause of death after lung cancer.1 There are several risk factors for the occurrence and progression of HCC, including hepatitis B or C virus infection, aflatoxin exposure, contamination of drinking water, and excessive alcohol consumption.38–40 Recent next-generation sequencing studies have demonstrated different evolutionary patterns of liver cancer and confirmed the high molecular heterogeneity in HCC;1,41 the high incidence of metastasis and recurrence of liver cancer may be attributable to its high molecular heterogeneity. Although numerous studies have probed the molecular mechanisms that contribute to the occurrence, progression, and recurrence of HCC, there are limited therapeutic options to delay tumor progression and improve survival outcomes. Besides, the high metastasis and recurrence rates of HCC are a significant impediment to favorable treatment outcomes. The underlying mechanisms of the clinical characteristics of HCC are not well characterized. SKA family proteins, as essential mediators of mitosis and participate in the coordination of cell cycle and proliferation in eukaryotic cells. Several studies have demonstrated a close association between abnormal expression of SKAs and the occurrence or progression of tumors such as lung carcinoma,29 invasive breast carcinoma,17 prostatic intraepithelial neoplasia,42 and other malignant tumors. However, the molecular mechanisms and functions of different SKA family members in HCC remain unclear. In this study, we used bioinformatics analysis to comprehensively and systematically describe the expression levels, diagnostic and prognostic significance, gene mutation patterns, transcriptional regulation, and immune cell infiltration levels of SKA family genes in HCC.
We found significantly higher expression levels of SKA1/2/3 in HCC tissues relative to nonneoplastic tissues. Significant upregulation of SKA1 has been demonstrated in a variety of tumors, such as non-small cell lung cancer,43 prostate cancer,42 and pancreatic ductal adenocarcinoma.30 An in vitro study found that the transcriptional levels of SKA1 were significantly elevated in human non-small cell lung cancer tissues and positively correlated with the proliferation, invasion, and metastatic ability of lung cancer cell lines.43 Li et al. reported that the overexpression of SKA1 induced centrosomal expansion of human prostatic epithelial cells by inducing centriole overduplication, which ultimately leads to spontaneous tumor formation in transgenic mouse models.42 SKA1 has also been shown to be highly expressed in pancreatic ductal adenocarcinoma tissues compared with noncancer tissues. Survival analysis found that SKA1 expression was an independent prognostic factor for pancreatic cancer but was not related to relapse-free survival.15 In our study, SKA1 expression was greater in HCC tissues than that in non-tumor tissue. We further explored the transcriptional levels of SKA1 in HCC and assessed their association with clinicopathological characteristics. The results showed a marked correlation of SKA1 expression with histologic grade, T stage, and AFP in HCC patients. Notably, ROC curve analysis showed that the expression of SKA1 had a good diagnostic value for liver cancer. Moreover, high SKA1 expression was linked with unfavorable outcomes in HCC patients. Through Cox proportional hazards regression analysis, we identified transcriptional expression of SKA1 as an independent predictor of poor OS and DSS of HCC patients. To further validate the role of SKA1 in HCC, we performed IHC staining of HCC tissue microarray and found that SKA1 expression in HCC tissues was markedly higher than that in para-carcinoma tissues. In vitro functional assays indicated that SKA1 knockdown significantly suppressed the proliferation and invasion abilities of HCC cells. To summarize, we identified SKA1 as an independent marker of survival outcomes in HCC and confirmed its important role in tumor cell malignant behavior.
Multiple studies have also focused on the oncogenic role of SKA2 in a variety of tumors. Wang et al. found significant upregulation of the transcriptional levels of SKA2 in breast cancer and that SKA2 knockdown inhibited the proliferation, invasion, and migration of breast cancer cells.17 In another study, expression of SKA2 mRNA was higher in human ESCC tissues relative to nonneoplastic tissues, and SKA2 overexpression contributed to both proliferation and migration of ESCC cells via activation of Akt signaling in vitro.19 A few studies have investigated the expression pattern, function, and mechanism of SKA2 in liver cancer. SKA2 expression was positively correlated with the expression of β-catenin in liver cancer cells in vitro and in vivo, and SKA2 knockdown inhibited tumor formation and growth in nude mice.44 However, further studies are required to investigate the underlying molecular mechanisms, biological functions, and clinical applications of SKA2 in HCC. In the present study, HCC tissues showed higher expression of SKA2 compared with normal tissues, which was significantly linked with unfavorable histologic grade, T stage, and AFP levels. The results of ROC curve analysis suggested that SKA2 may be a potential diagnostic marker in HCC. Moreover, high SKA2 expression in HCC patients was correlated with unfavorable OS, PFS, and DSS. The results indicate the oncogenic role of SKA2 in HCC and its potential prognostic significance.
Similar to SKA1 and SKA2, the carcinogenic role of SKA3 has been demonstrated in multiple human malignancies, such as cervical cancer14 and laryngeal squamous cell carcinoma.45 An IHC study of 100 cervical cancer patients revealed overexpression of protein levels of SKA3 in advanced cervical cancer. High SKA3 expression was found to promote cervical cancer cell proliferation and invasion via the PI3K/Akt-dependent signaling.14 In laryngeal squamous cell carcinoma, Li et al. initially revealed a role of SKA3 in regulating tumor proliferation through metabolic reprogramming and demonstrated that targeting SKA3 inhibited the chemotherapy and proliferation resistance of tumor cells by inhibiting glycolysis mediated by the PLK1-Akt axis.45 The transcriptional levels of SKA3 were found to be significantly higher in pancreatic ductal adenocarcinoma tissues relative to para-carcinoma tissues and had a marked association with unfavorable prognosis and immune infiltration.30 Consistent with the results of SKA1 and SKA2, our study revealed a higher transcriptional level of SKA3 in HCC samples compared with that in adjacent nonneoplastic tissues, and SKA3 expression showed a strong correlation with clinicopathological characteristics, such as histologic grade, T stage, and AFP levels. Early diagnosis of cancer is crucial for tracking the progression of the disease and early use of antitumor therapy. On ROC curve analysis, transcriptional levels of SKA3 were found to be highly sensitive and specific for the diagnosis of HCC. Predictably, HCC patients with high SKA3 expression showed markedly unfavorable OS, DFS, and DSS. The results revealed that SKA3 may be partially involved in the carcinogenic mechanism of HCC.
To further verify the possible oncogenic function of SKA family genes, we constructed a gene-gene interaction network of SKA family genes through GeneMANIA and a PPI network of SKAs and their most similar genes based on the STRING dataset. The two networks revealed that SKAs were primarily related to microtubule motor activity, cytoskeletal protein binding, and cell cycle, all of which were closely associated with abnormal proliferation of malignant tumors.
Subsequently, we explored the function of highly expressed SKAs and their similar genes through GO and KEGG enrichment analysis. As expected, the results showed that they were significantly related to cell cycle, cellular senescence, and the p53 signaling pathway. It is widely accepted that impaired regulation of the cell cycle leads to uncontrolled growth of normal cells and induces their transformation into tumor cells.46,47 The p53 signaling pathway, which regulates the initiation of the cell cycle, is the most closely related pathway found in human tumors.48,49 Cellular senescence is a universal biological phenomenon, and recent studies have suggested that cellular senescence may contribute to the occurrence and development of tumors by inducing chronic inflammation.50,51 Therefore, our results demonstrate the participation of SKA family genes in the carcinogenic mechanism of HCC and their potential value as new therapeutic targets.
As an important component of the tumor microenvironment, immune cell infiltration can have a significant impact on tumor progression as well as recurrence and is considered as a key determinant of responses to immunotherapy and clinical outcomes of cancer patients.52,53 Recent studies have proven the SKA1 expression associated with an abundance of tumor-infiltrating immune cells in adrenocortical carcinoma was closely related with poor prognosis. It has also been reported that SKA1 and SKA3 have a vital role in the recruitment and regulation of immune-infiltrating cells in pancreas ductal adenocarcinoma, which eventually influence overall patient survival. Based on literature research and existing research basis, we analyzed the association of SKA and immune infiltration using the ssGSEA algorithm. We found that the transcriptional levels of SKA family genes were correlated with the infiltration of Th2 cells, Tfhs, and Th cells, neutrophils, and dendritic cells (DCs), especially Th2 cells. Helper T cells, both Th1 and Th2 cells, are important immune regulatory cells, and normally there is a dynamic equilibrium between Th1 and Th2 subtypes.54 Increased secretion of Th2 cytokines in patients with malignant tumors induces Th1/Th2 drift, resulting in the imbalance of Th1/Th2.55 Many tumors, including lung cancer, liver cancer, and gastric cancer, have a Th1/Th2 balance shift that is often dominated by Th2 cells in the body, which may be related to the immune escape of tumors.56 Consistent with the above assumptions, we found a positive association of SKAs expression with Th2 cell infiltration in this study. We observed a significant positive correlation between mRNA levels of SKAs and markers of Th2 cells, thus revealing the underlying mechanism by which SKAs influence the immune microenvironment through altering the infiltration state of Th2 cells.
While our study revealed some important findings, there are some limitations. Most of the experimental results in this study are based on online bioinformatics data analysis, and further experiments are required to explore the underlying mechanisms. In addition, most of the analyses were based on transcriptome data. We did not explore the association between SKA family protein expression and the clinical outcomes of HCC patients. Finally, the specific mechanism by which SKA promotes the progression of HCC was not discussed in this review. Further study is required to address these issues.