Introduction
Pancreatic ductal adenocarcinoma (PDAC) is the most common cause of death from digestive system malignancies. It has been reported that the mortality rate of PDAC reached 93.95% in 2020 [
1]. The median 5-year survival for stage 4 PDAC is 9% [
2]. PDAC has a poor prognosis partly due to its rapid progression and the lack of diagnostic and therapeutic targets [
3]. Although a lot of work has been done to reveal the pathogenesis of PDAC, there are still many unclear areas. Ectopically expressed genes involved in the cell cycle, development, cell differentiation/proliferation, and energy metabolism are among the factors involved in PDAC pathogenesis [
4]. The key to developing more effective diagnostic and therapeutic strategies is to identify novel genes or specific targets of PDAC and clarify their roles [
5].
Recently, gene chips and gene profiles have been widely used to screen differentially expressed genes (DEGs), and using these data, we can obtain new insights into the mechanism and treatment of PDAC [
6]. Many bioinformatic studies of PDAC have been proven to be effective and reliable [
7]. Because of the complex tumor heterogeneity and complicated molecular regulatory mechanism of PDAC, current studies may be insufficient or inconsistent. An integrated bioinformatics analysis could not only assist with exploring the biomarkers and the mechanisms underlying the tumorigenesis and progression of cancer but could also help to find novel and potential treatment options for the disease [
8].
Nucleolar and spindle-associated protein 1 (NUSAP1) has recently been recognized as a cell cycle-regulating protein that binds microtubules and controls mitotic progression, spindle formation, and stability. The structural integrity of the spindle ensures the equal division of chromosomes, which can be a prerequisite for cell division. Abnormal spindle structure can result in incorrect chromosome separation (also known as chromosomal instability), which will lead to tumorigenesis [
9]. Increased expression of NUSAP1 has been reported in prostate cancer, breast cancer, oral squamous cell carcinoma, and cervical cancer and is closely associated with tumor development and a poor prognosis [
10]. However, studies on NUSAP1 in PDAC are rare, and its role in the mechanism of occurrence and development of PDAC remains unclear.
In this study, three original gene expression profiles (GSE15471, GSE16515, and GSE71989) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs were screened, protein-protein interaction (PPI) network was constructed and hub genes were identified. Then, we experimentally identified the function of NUSAP1 in the carcinogenesis of PDAC. Our results will help to develop novel therapeutic strategies to improve clinical outcomes and provide new insights into PDAC biology.
Materials and methods
Data acquisition from the Gene expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA)
In this study, we collected three microarray datasets (GSE15471, GSE16515 and GSE71989) from the GEO database (
https://www.ncbi.nlm.nih.gov/geo/), which is a public repository of high-throughput gene expression genomics datasets. The inclusion criteria for the above gene expression profiles were set as follows: (1) the dataset included tissue samples obtained from human PDAC tissues and normal tissues; (2) the number of samples in each dataset was more than 8; (3) the platform was GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array). The miRNA transcriptome data for 183 pancreatic-related samples (179 tumor tissues and 4 normal tissues) and the corresponding clinical information were also downloaded from the TCGA database (
https://portal.gdc.cancer.gov/) on June 1, 2021.
Identification of differentially expressed genes (DEGs)
Perturbation effects of the hub genes on PDAC cell lines
The Cancer Dependency Map (DepMap) database (
https://depmap.org/portal/) was used to explore the perturbation effects of the ten hub genes on 44 different PDAC cell lines. The CERES dependency score, which is based on data from a cell depletion assay, was used to evaluate the effect. A low CERES score indicates a higher likelihood that the gene of interest is essential in a given cell line. A score of 0 indicates that a gene is not essential, and a score of -1 is comparable to the median of all pan-essential genes.
Survival analysis and tissue expression analysis of NUSAP1
Gene Expression Profiling Interactive Analysis (GEPIA) (
http://gepia.cancer-pku.cn/) was utilized to show the expression of NUSAP1 in PDAC tumor tissue and normal tissue. Furthermore, survival analysis was performed via GEPIA using data from the TCGA and Genotype-Tissue Expression (GTEx) (
https://commonfund.nih.gov/gtex) databases.
Associations between NUSAP1 expression and genome heterogeneity
To identify the regulatory role of NUSAP1 expression in PDAC, we integrated NUSAP1 gene expression data in the TCGA with data for other variables. Tumor mutation burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), and neoantigen were used to evaluate the relationship between NUSAP1 expression, tumor mutation load and treatment sensitivity [
12]. Mutant-allele tumor heterogeneity (MATH), purity, ploidy, and loss of heterozygosity (LOH) were used to assess the association between NUSAP1 expression and tumor heterogeneity [
13].
Correlation of immune cell infiltration and NUSAP1 expression
Immune cell infiltration in PDAC was estimated from RNA-sequencing data using CIBERSORT (
https://cibersortx.stanford.edu/), a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). A violin plot was drawn in R software Version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria;
https://www.r-project.org/) [
14].
DEG analysis
Analysis of DEGs between the high NUSAP1 expression group and the low NUSAP1 expression group of PDAC samples in the TCGA database was performed in R software, and an adjusted
p value of < 0.05 and |logFC|> 2.0 were employed as the cutoff criteria. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were performed by Metascape (
http://metascape.org).
PDAC tissue microarray (TMA) and immunohistochemistry (IHC)
TMAs were generated from samples from PDAC patients who were clearly diagnosed through surgical pathology at the Fudan University Shanghai Cancer Center (FUSCC) between February and September 2017. Patients who received medical treatment (e.g., radiotherapy or chemotherapy) before sampling or had a coexisting secondary tumor were excluded. The corresponding clinical data of the patients, including age, sex, tumor location, tumor size, lymph node status, pathologic diagnosis, TNM stage, survival time and outcomes, were recorded. All procedures were performed after obtaining approval from the Clinical Research Ethics Committee of FUSCC, and informed consent was obtained from each patient prior to the analyses. Two independent pathologists conducted the strict pathological diagnoses and postoperative follow-ups. IHC staining with antibodies against NUSAP1 was performed to detect protein expression levels using standard procedures. Protein expression levels were calculated by multiplying the positivity (0, < 5% of the total cells; 1, 5–25%; 2, 25–50%; 3, 50–75%; and 4, > 75%) and intensity scores (0, no coloration; 1, pale yellow; 2, yellow; and 3, clay bank) and were classified as follows: negative (0, −); weakly positive (1–3, +); moderately positive (4–6, ++); and strongly positive (> 6, +++). Then, we divided the patients into two groups (−/+, low expression and ++/+++, high expression) and performed survival analyses [
15].
Cell lines and cell culture
The PANC-1 and CAPAN-1 cell lines were purchased from The Cell Bank of Type Culture Collection of the Chinese Academy of Sciences. PANC-1 cells were maintained at 37 °C with 5% CO2 and cultured in DMEM supplemented with 10% FBS (both Gibco; Thermo Fisher Scientific, Inc.). CAPAN-1 cells were maintained at 37 °C with 5% CO2 and cultured in IMDM supplemented with 10% FBS (both Gibco; Thermo Fisher Scientific, Inc.).
Transfection
The siRNA targeting NUSAP1 and scrambled negative control siRNA (si-NC) were purchased from Santa Cruz Biotechnology, Inc. (cat. nos. sc-93,396 and sc-37,007). Transfection was performed using standard protocols for Lipofectamine® 3000 (Invitrogen; Thermo Fisher Scientific, Inc.). Lipofectamine 3000 reagent and siRNAs were diluted separately with OPTI-MEM (Gibco; Thermo Fisher Scientific, Inc.) in a centrifuge tube, and then Lipofectamine 3000 and siRNAs were mixed and incubated for 15 min at room temperature. Subsequently, the complex was added to the cells and incubated for 48 h at 37 °C [
16]. Lentiviral vectors containing NUSAP1 shRNA, NC shRNA and NUSAP1 OE plasmids were obtained from HanBio (China) and transfected into cells according to the manufacturer’s instructions. Stable cells transfected with lentivirus were selected with puromycin (2 µg/mL).
Quantitative real-time PCR
Quantitative real-time PCR was performed as described previously [
16]. All reactions were run in triplicate. RNA was extracted from the cell line preserved in RNAlater using the SteadyPure Universal RNA Extraction Kit (AG21017).
Western blot analysis
Western blotting was performed as described in our previous study [
17,
18]. The antibodies used in the present study were against NUSAP1 (1:3000; Proteintech), GAPDH (1:5000; Abcam), E-cadherin (1:1000; CST), ZEB1 (1:1000; CST), Claudin 1 (1:1000; CST), Snail (1:1000; CST), Zo 1 (1:1000; CST), AMPKα (1:1000; CST) and Phospho-AMPKα (1:1000; CST). The membranes of western blotting were cut to a suitable size prior to binding with antibodies.
CCK-8 assay
The cells were seeded into 96-well plates at the logarithmic growth stage. After 24 h, 10 µL CCK-8 (Beyotime, Shanghai, China) solution was added to each well, and the cells were further cultured for 2 h. The absorbance value at 450 nm was detected under a microplate reader.
EdU assay
An EdU incorporation assay was conducted using the BeyoClick EdU Cell Proliferation Kit (cat #C0078S; Beyotime Biotechnology) following the instructions. Briefly, the cells were cultured with 10 µM EdU for 4 h at 37 °C with 5% CO2. The cells were then fixed and permeabilized. After washing with PBS three times, the cells were incubated with Click Additive Solution for 30 min at room temperature. Finally, fluorescent images were obtained by a confocal laser scanning microscope.
For colony formation assays, 1000 cells were seeded in 6-well plates. After 14 days, colonies were fixed with 4% paraformaldehyde and stained with crystal violet staining solution (cat #C0121; Beyotime Biotechnology, Shanghai, China). Images and colony counts were obtained using a colony counting machine (Gel Count; Oxford Optronix, UK) [
15].
Flow cytometry
Cells were stained by using a FITC Annexin V Apoptosis Detection Kit (BD, La Jolla, CA, USA) complied with the manufacturer’s instructions and counted using a FACSCalibur flow cytometer to detect apoptotic rate.
Cell migration and invasion assay
Wound healing assays was used to assess the migration ability of PDAC cells. The cell line was seeded in six-well plates and scratched with a sterile pipette tip, and then the cells were washed with PBS. DMEM or IMDM containing 2% FBS was added to each well. The images were sequentially captured at 0, 12, and 24 h of cultivation.
The Transwell assay was used to assess the migration and the invasion ability of PDAC cells. For migration assays, 6 × 104 cells suspended in 200 µL serum-free medium were added to the upper chamber of the Transwell chamber, each of which included a Tewksbury multiporous polycarbonate membrane (8-mm pore size) insert, and medium containing 10% FBS as a chemical attractant was placed in the bottom chamber. According to the manufacturer’s instructions, cell invasion was detected using a Transwell chamber coated with Matrigel (1:100 in DMEM; BD Biosciences, USA). A total of 2 × 105 cells in serum-free medium were added to the upper chamber, and the lower chamber contained 500 µL of 20% FBS-supplemented medium. After culturing the cells for 24 h, they were fixed with 4% paraformaldehyde for 30 min. The migrated cells were stained with crystal violet for 20 min and washed with PBS three times.
Animal studies
Four- to five-week-old female nude mice were obtained from Shanghai SLAC Laboratory (Shanghai, China). Ten mice were randomly divided into two groups (5 mice/group): the NC group and the KD group, approximately 5 × 106 cells in 200ul PBS were subcutaneously inoculated on right flank of the mice. Following the formation of palpable tumors, we tested the tumor size every 4 days and calculated the tumor volume following the formula: length × width2 × 0.5. At 4 weeks post implantation, the tumor specimens were surgically dissected, fixed with paraformaldehyde and then subjected to immunohistochemical staining. NUSAP1, Ki-67, BAX, and Cleaved caspase-3 were evaluated, and the calculation methods for protein expression levels were the same as those used for TMAs. The protocol was approved by the Committee on the Ethics of Animal Experiments of Fudan University, and the study is reported in accordance with ARRIVE guidelines.
Statistics
Statistical analyses were conducted using SPSS Statistics Version 25.0.0 (IBM Inc., Chicago, IL, USA;
https://www.ibm.com/docs/en/spss-statistics/25.0.0). Statistical significance was determined by Student’s t test, chi-square test, log-rank test, and Pearson correlation analysis. Differences with
P < 0.05 were considered to be statistically significant.
Discussion
Although great progress in surgical and medical treatment has been made for PDAC, it still has a dismal prognosis, highlighted by a low survival rate and unfavorable therapeutic efficacy [
25]. PDAC-related death is mainly attributed to a lack of early detection methods, the high risk for metastasis, and chemotherapy resistance. Therefore, exploring reliable biomarkers and precise molecular mechanisms for the early diagnosis, treatment, and prognosis evaluation of PDAC is urgent [
26]. NUSAP1 was reported to be a potential biomarker for PDAC diagnosis and prognosis evaluation in previous studies [
27]. It has been reported to be associated with mitosis, which is an integral cell process that requires great accuracy to ensure correct and stable chromosome replication [
28]. NUSAP1 participates in regulating the Wnt/β-catenin signaling pathway and is expressed at higher levels in breast cancer and liver cancer tissues than in corresponding normal tissues [
29,
30]. There are only a few reports on the role of NUSAP1 in the occurrence and progression of pancreatic cancer. In our study, we found that NUSAP1 promoted the proliferation, epithelial mesenchymal transition, migration and invasion of cancer cells in vitro. TMA analysis revealed high expression of NUSAP1 in tumor tissues compared with normal tissues, and this upregulation was associated with a poor prognosis. Our results confirmed that NUSAP1 plays a role as an oncogene in PDAC.
Novel methods are being continually established for the identification of the molecular mechanisms of cancer. RNA sequencing and cDNA microarray are both high-throughput screening (HTS) techniques and are widely used to explore the mechanisms involved in carcinogenesis and tumor progression [
31]. A large amount of corresponding data from HTS techniques is stored in several public databases, such as the TCGA, International Cancer Genome Consortium (ICGC) (
https://dcc.icgc.org/) and GEO. Compared to analysis of individual HTS datasets, integration of multiple HTS datasets (cDNA microarray and RNA sequencing) is considered to increase the reliability of results [
32]. In the present study, three GEO datasets, namely, GSE15471, GSE16515 and GSE71989, were selected to screen DEGs. PPI network and module analyses were used to identify the top 10 high-scoring hub genes (CCNB1, CCNA2, CDK1, MAD2L1, DLGAP5, NDC80, NUSAP1, MELK, TOP2A and ASPM), which confirmed that NUSAP1 may play an important role in the development of PDAC. The cBioPortal for Cancer Genomics, an open-access resource for interactive exploration of multidimensional cancer genomics datasets that provides access to data from more than 5,000 tumor samples [
33], was used to show mutation information of the hub genes. DepMap is an accessible website based on large-scale multiomics screening projects, including the Cancer Cell Line Encyclopedia (CCLE), the PRISM Repurposing dataset and the Achilles Project [
34]. CERES (CRISPR) scores, representing the effect of each gene in a given set, were determined by screening experiments. Simply, the score evaluates the effect size of knocking out or knocking down a gene while normalizing expression against the distribution of pan-essential and nonessential genes. A negative score indicates that the cell line grows slower after experimental manipulation, while a positive score indicates that the cell line grows faster [
35]. The tumor immune microenvironment (TIME) has gradually attracted attention, and analysis of the TIME will contribute to the improvement of immunotherapy [
36]. Some researchers revealed that the TIME could be used as a main prognostic indicator and could enhance the potential of precision treatments [
37]. We also found that NUSAP1 can cause changes in the TIME of PDAC, which may be one of the ways in which this gene plays its role. Bioinformatics analysis could assist with exploring the biomarkers and the mechanisms underlying the tumorigenesis and progression of cancer and may play an increasingly important role in future research [
8].
As an energy sensor, AMPK is activated when the intracellular ATP level is insufficient and triggers a series of downstream responses that inhibit rapid cell proliferation and has been frequently identified as a potential target in anticancer treatment [
38]. In the context of metabolic regulation, AMPKα is a key molecule responsible for energy regulation. The AMPKα-Sirt1-FGF21 cascade has a strong correlation to metabolic energy modulation. AMPK activation can increase glucose uptake and decrease hyperglycaemia by promoting energy expenditure, together with the increase in insulin sensitivity to attenuate metabolic stresses [
39]. Although there is evidence suggesting that AMPK might help cancer cells survive under certain circumstances, there is more support in the literature for the notion that AMPK acts as a tumor suppressor by leading to cell growth inhibition and cell cycle arrest [
40]. Song’s study revealed that Hernandezine activates autophagy and induces autophagic cell death in PDAC cells by promoting ROS generation, activating the AMPK signaling pathway and inhibiting the mTOR/p70S6K signaling pathway [
41]. Notably, activation of AMPK is dependent on the phosphorylation of AMPKα at Thr-172. Our study firstly found that NUSAP1 affects the proliferation and metastasis of PDAC through inhibition AMPK signaling pathway, which provides a new idea for targeting the NUSAP1/AMPK axis in PDAC research and treatment.
Taken together, the current study demonstrated very interesting and strong evidence that NUSAP1 is a key gene in PDAC and may be an effective novel target for treatment. Therefore, considering the crucial roles of NUSAP1 identified in this study and previous studies mentioned above, further research procedure such as RNA sequencing may be needed and focused on exploring the gene’s precise mechanisms in PDAC for only simple experiments were performed in this study. However, this study provides novel information regarding the role of NUSAP1 in PDAC.
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