The human LUAD cell line, H1299, and the normal bronchial epithelial BEAS-2B cell line, were purchased from the Cell Bank of the Chinese Academy of Sciences. H1299 cells were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.) and maintained at 37°C in an incubator containing 5% CO₂, with 100 U/ml penicillin and 100 mg/ml streptomycin. BEAS-2B cells were maintained in Dulbecco's modified Eagle's medium (DMEM) (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% FBS and maintained at 37°C in an incubator containing 5% CO₂, with 100 U/ml penicillin and 100 mg/ml streptomycin.

H1299 and BEAS-2B cells were lysed in RIPA buffer (Beyotime Institute of Biotechnology). Protein concentrations were then determined using a BCA Protein assay kit (Beyotime Institute of Biotechnology). The protein samples (25 µg/sample) were subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE) on a 12% gel before being transferred to polyvinylidene difluoride membranes. To block non-specific protein binding, the membranes were incubated in 5% BSA for 1.5 h at room temperature with gentle agitation. Next, the membranes were incubated with GAPDH (1:1,000; cat. no. ab8245; Abcam) and HJURP (1:2,000; cat. no. ab233541; Abcam) antibodies at 4°C for 15–18 h, washed three times with TBST (Tris-buffered saline containing 0.1% Tween-20), and incubated with HRP-conjugated secondary antibodies [1:1,000; cat. nos. A0208 (goat anti-rabbit IgG) and A0216 (goat anti-mouse IgG); Beyotime Institute of Biotechnology] for 1 h at room temperature. Finally, the membranes were washed again with TBST and BeyoECL Plus [Ultra Sensitive ECL Chemiluminescence kit (cat. no. P0018S; Beyotime Institute of Biotechnolog]) was used to visualize the protein bands on a ChemiDoc Touch (Bio-Rad Laboratories, Inc.). The bands were quantified using Quantity One 1-D analysis software version 4.6.8 (Bio-Rad Laboratories, Inc.) (18). GAPDH immunoreactivity was used as the loading control for each protein.

Total RNA was extracted from cells using TRIzol^® (Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Using the PrimeScript RT reagent kit (Takara Bio, Inc.), RNA was reverse-transcribed to synthesize first-strand cDNA, which was then quantified using an SYBR Premix Ex Taq kit (Takara Bio, Inc.), according to the manufacturer's instructions. Primers used in this study were as follows: HJURP forward, 5′-AGTGCCTTTATGTATTGGAG-3′ and reverse, 5′-AAGTGAGGGTCTGGATTTA-3′; and GAPDH forward, 5′-GAACATCATCCCTGCCTCTACT-3′ and reverse, 5′-ATTTGGCAGGTTTTTCTAGACG-3′. qPCR was performed with the following thermocycling conditions: 95°C for 15 min, followed by 40 cycles of 95°C for 10 sec, 60°C for 20 sec and 72°C for 10 sec. Fluorescence was detected using a Corbett Research RG-6000 Real-Time PCR Machine (Corbett Life Science, Sydney, NSW, Australia). Each sample was run in triplicate and was compared with GAPDH as the internal control. Results were obtained using the 2^−ΔΔCq method (19).

Gene expression (HJURP) profiling data of 519 LUAD samples and 54 normal tissue samples were downloaded from the publicly available TCGA database (https://gdc.cancer.gov/). Another transcriptome profiling dataset, GSE116959 (20), of 57 LUAD samples and 11 peritumoral normal lung tissue samples was obtained from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/) to verify the expression level of HJURP in LUAD cases. Log₂FC>2 indicates that gene expression in tumor samples is upregulated 4 times compared with that of adjacent samples, log₂FC<-2 indicates that gene expression in tumor samples is downregulated 4 times compared with that of adjacent samples. HJURP protein expression profiling data were obtained from the UALCAN database (http://ualcan.path.uab.edu/index.html). Relevant clinicopathological information, including age, sex, T stage, N status, M grade, stage (21) and overall survival (OS) time were also extracted from the TCGA database. A total of 480 patients with LUAD with complete follow-up data were included, whose details were recorded prior to November 1, 2019. The clinical end point was OS, defined as the time from surgery to death. In addition, patients who were alive at the last follow-up were considered to be censored observations.

GSEA is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (22). In the present study, the GSEA first generated an ordered list of all genes according to their correlation with HJURP expression; tumor samples were divided into high expression group and low expression group according to the median value (4.3) of HJURP expression level. GSEA was then performed to elucidate the significant survival differences observed between the high and low expression HJURP groups. Gene set permutations were performed 1,000 times for each analysis. The level of HJURP expression was used as a phenotype label. The nominal P-value and normalized enrichment score (NES) were used to sort the pathways enriched in each phenotype.

It is well known that interactions between a tumor and the immune system play a crucial role in cancer initiation, progression and response to treatment. The integrated repository portal for tumor-immune system interactions (http://cis.hku.hk/TISIDB/index.php) (22) was used to examine tumor and immune system interactions in 28 types of tumor-infiltrating lymphocytes (TILs) seen across different human cancer types. The relative abundance of TILs was inferred by using gene set variation analysis based on the HJURP expression profile. Spearman's test was applied to measure correlations between HJURP and TILs; P<0.05 was considered to indicate a significant difference for all tests.

Scatter plots and paired plots were used to show differences in HJURP expression between normal and tumor samples. The cut-off value of HJURP expression was determined by the optimal cutoff values determined by X-tile software (https://medicine.yale.edu/lab/rimm/research/software/). The Wilcoxon rank-sum test or Kruskal-Wallis test was used to assess the association between expression levels and clinicopathological characteristics. The Kaplan-Meier method and log-rank test were used to estimate associations between HJURP expression and OS. Univariate and multivariable Cox proportional hazards regression models were used to evaluate the impact of HJURP expression on OS in the presence of other known risk factors. Two-sided P<0.05 was considered to indicate a statistically significant difference. All analyses were performed using R (v.3.5.2; http://cran.r-project.org/bin/windows/base/old/3.5.2/).

As shown in Table 1, 480 primary tumors with both clinical and gene expression data were downloaded from the TCGA database during November 2019. The study cohort included 221 (46.04%) males, with average patient age being 66 years. In the cohort, the T-stage distribution of LUAD was as follows: T1, 164 patients (34.17%); T2, 254 patients (52.92%); T3, 40 patients (8.33%); and T4, 19 patients (3.96%). The N status distribution of LUAD was as follows: N0, 310 patients (64.58%); N1, 87 patients (18.13%); N2, 69 patients (14.38%); and N3, 2 patients (0.42%). The cancer type distribution was as follows: M0, 316 patients (65.83%) and M1, 25 patients (5.21%). Stage I disease was found in 260 patients (54.16%), stage II in 107 patients (22.29%); stage III in 79 patients (16.46%); and stage IV in 26 patients (5.42%). The median follow-up time for patients alive at their last contact was 18.42 months (range, 0–227.07 months).

Compared with HJURP expression in normal lung tissues (n=54), HJURP expression was significantly higher in LUAD tissues (n=519) (P<0.05). The scatter plot (Fig. 1A) and paired plot (Fig. 1B) show the differences in HJURP expression between normal and tumor samples. Expression profiling data were also obtained from the GSE116959 dataset (including 57 LUAD samples and 11 normal lung tissue samples), after data preprocessing and quality assessment using R software. According to the cut-off criteria set (P<0.05 and |log2FC|>2.0), a total of 329 differentially expressed genes were obtained, including 85 upregulated genes and 244 downregulated genes. To further investigate HJURP protein expression in patients with LUAD, the levels of HJURP proteomic expression were quantified in normal lung tissues (n=102) and primary LUAD tissues (n=111). The results showed that the expression of HJURP was significantly higher in primary LUAD (median, 0) than in normal lung tissues (median, −1.016) (P<0.01; Fig. 1C). Significant differences were also found in HJURP protein expression with regard to LUAD grades compared with the normal group: Grade 2 (median, −0.28; P<0.01) and grade 3 (median, 0.421; P<0.01). However, the protein expression of HJURP may not be associated with grade 1 LUAD (P=0.202) (Fig. 1D). Significant differences in HJURP expression were observed with regard to the T grade (P=0.012), N grade (P=0.012) and stage (P=0.001) of LUAD (Fig. 2A, B and D). However, the HJURP expression level was not associated with M grade (P=0.101; Fig. 2C).

As presented in Fig. 3, Kaplan-Meier survival analysis showed that LUAD with high expression of HJURP was associated with a worse prognosis compared with LUAD with low expression of HJURP (P<0.001). The univariate analysis revealed that high expression of HJURP was significantly associated with a poor OS [hazard ratio (HR), 1.06; 95% confidence interval (CI), 1.03-1.09; P<0.001]. Other clinicopathological variables associated with poor survival included T grade, N grade and stage (Table II). The multivariate analysis showed that HJURP remained independently associated with OS, with an HR of 1.32 (95% CI, 1.09-1.60; P=0.004), along with stage (HR, 1.90; 95% CI, 1.19-3.03; P=0.007).

To identify signaling pathways that are differentially activated in LUAD, GSEA was conducted between low and high HJURP expression data sets. GSEA was used to identify significant differences in signaling pathways from the MSigDB Collection (c2.cp.kegg.v7.4.symbols.gmt). False discovery rate <0.05 and nominal P<0.05 were used as thresholds to determine significantly enriched signaling pathways. The most significantly enriched signaling pathways were selected based on their NES (Fig. 4). Fig. 4 shows that ‘basal transcription factors’, the ‘cell cycle’, ‘homologous recombination’, ‘non-small cell lung cancer’ (NSCLC), ‘oocyte meiosis’, the ‘p53 signaling pathway’, ‘pathways in cancer’, ‘RNA degradation’ and ‘spliceosome’ are differentially enriched in the high HJURP expression phenotype.

After identifying the HJURP-related signaling pathways, a correlation analysis was performed to explore the relationship between HJURP expression and immune infiltration level in patients with LUAD. Significant correlations were found between HJURP expression and 28 types of TILs across different human cancer types (Fig. 5A). Significant results were found for HJURP expression with the abundance of activated CD4 T cells (ρ=0.591; P<0.001) were notably correlated.

The relationships between three types of immunomodulators, immune-inhibitors (Fig. 6A-E), immunostimulators (Fig. 6F-R) and major histocompatibility complex molecules (Fig. 6S and T), and the expression of HJURP were examined. Significant results were observed using Spearman's correlation test (Fig. 6); however, only HJURP expression and poliovirus receptor (an immunostimulator) exhibited a coefficient indicating that the variables were notably correlated.

The distribution of HJURP expression across immune and molecular subtypes was also explored. Fig. 7A shows the associations between HJURP expression and immune subtypes across different human cancer types. The violin plot shows the LUAD distribution across the following subtypes: C1, wound healing; C2, IFN-γ dominant; C3, inflammatory; C4, lymphocyte-depleted; C5, immunologically quiet; and C6, TGF-β dominant (Fig. 7B). In addition, western blotting and qPCR results from a LUAD cell line (H1299) and a normal bronchial epithelial cell line (BEAS-2B) confirmed that HJURP was significantly increased in NSCLC (Fig. 8A-C). The research design of the study is shown in Fig. 9.