Prognostic value of Holliday junction‑recognizing protein and its correlation with immune infiltrates in lung adenocarcinoma
- Authors:
- Published online on: May 27, 2022 https://doi.org/10.3892/ol.2022.13353
- Article Number: 232
Abstract
Introduction
Lung cancer is the first and second most common cause of cancer morbidity among males and females in China, respectively, and is also a leading cause of cancer-related mortality worldwide (1,2). Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer, and its incidence has been increasing in recent years (3). Current treatments for LUAD include surgical resection, radiotherapy, chemotherapy, targeted therapy and immunotherapy (4). Although multimodal therapies have been used to treat LUAD, survival outcomes remain unsatisfactory, ranging from 15 to 20% (2). Surgery and drugs used for chemotherapy can lead to complications, such as infections (Staphylococcus aureus, Escherichia coli, Streptococcus spp and Fusarium spp), which can become severe, and even fatal infections of the surgical site while in the hospital environment (5–7). Therefore, there is an urgent need to explore potential molecular biomarkers that can help determine patient prognosis and be used to prescribe effective treatments for LUAD.
Holliday junction-recognizing protein (HJURP) is a protein that has recently been shown to be required for centromere protein-A (CENP-A) loading in the centromeric chromatin and for the assembly of functional kinetochores (8–10). In humans, HJURP has been demonstrated to be a critical regulator of DNA binding and phosphorylation, and is involved in the regulation of chromosomal segregation and cell division (11,12). Emerging evidence has revealed that HJURP expression is significantly upregulated following DNA damage, that it collaborates with components of the DNA repair machinery and that it plays a role in homologous recombination (10,13). In addition, the upregulation of HJURP, which has now been reported in hepatocellular carcinoma, glioblastoma, breast cancer and ovarian carcinoma, has been correlated with a poor prognosis (14–17). However, there remains limited understanding as to whether HJURP expression can act as a prognostic biomarker for LUAD, despite continuing reports of the role HJURP plays in carcinogenesis.
Thus, the objective of the current study was to evaluate the prognostic value of HJURP expression in cases of human LUAD, based on data obtained from The Cancer Genome Atlas (TCGA). To gain further insights into the biological pathways involved in LUAD pathogenesis related to the HJURP regulatory network, gene set enrichment analysis (GSEA) was also performed.
Materials and methods
Cell lines
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.
Western blotting
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.
Reverse transcription-quantitative PCR (RT-qPCR)
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).
Collection of publicly available data from TCGA and Gene Expression Omnibus (GEO) databases
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
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.
Immune infiltration analysis
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.
Statistical analyses
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/).
Results
Patient characteristics
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).
HJURP expression and its association with clinicopathological variables
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).
HJURP expression is associated with the survival rate of patients with LUAD
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).
 | Table II.Associations between overall survival time and clinicopathological characteristics in patients from The Cancer Genome Atlas according to univariate and multivariate Cox regression analysis. |
Molecular mechanisms of HJURP in LUAD
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.
Correlation of HJURP expression with immune infiltration level
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.
Discussion
In the present study, an RNA sequencing dataset of HJURP and relevant clinical parameters of 480 patients with LUAD from the TCGA database were analyzed. The study found that the high expression of HJURP could be considered to be an independent prognostic factor in patients with LUAD, regardless of other clinicopathological variables. HJURP may be a potentially useful prognostic molecular biomarker of poor survival in LUAD cases. Further experiments should be performed to elucidate the biological effects of HJURP.
An increasing number of studies have found that HJURP may be exploited as a potentially effective biomarker in the diagnosis of and determination of progression and prognosis of cancer (14–17). Hu et al (14) identified that high levels of HJURP expression could predict a poorer prognosis in hepatocellular carcinoma (HCC) and may promote HCC progression by accelerating HCC cell proliferation. A study by Valente et al (15) found that HJURP plays an important role in the maintenance of extremely proliferative cells of high-grade gliomas and pointed to HJURP as a potential therapeutic target for the development of novel treatments for patients with glioma. Montes de Oca et al (16) identified HJURP as the first biomarker that can be used to differentiate good and poor prognoses in patients with luminal A breast cancer; the study also noted that HJURP can support the integration of selected chromatin regulators in the clinical setting to help guide treatment plans and improve the overall management of patients with breast cancer. Recently, Li et al (17) revealed that increased expression of HJURP could act as an independent negative prognostic biomarker for patients with advanced serous ovarian cancer. These studies suggested that HJURP has potentially useful clinical implications in improving prognostic predictions for cancer. However, there remains a limited understanding on whether HJURP expression is a prognostic biomarker in LUAD.
In the present study, bioinformatics analysis using high-throughput RNA-sequencing data from TCGA demonstrated that the upregulation of HJURP in LUAD was associated with advanced clinicopathological characteristics (T grade, N grade and stage), survival time and a poor prognosis. To further investigate the functions of HJURP in LUAD, GSEA was performed using TCGA data. This GSEA showed that ‘basal transcription factors’, the ‘cell cycle’, ‘homologous recombination’, ‘non-small cell lung cancer’, ‘oocyte meiosis’, the ‘p53 signaling pathway’, ‘pathways in cancer’, ‘RNA degradation’ and ‘spliceosome’ are enriched in the high HJURP expression phenotype. This suggests that HJURP may serve as a potential biomarker of prognosis and a therapeutic target in LUAD.
The present findings are in agreement with those previously reported for HJURP upregulation in lung tumors compared with HJURP expression in normal lung tissue samples (23), as well as with the increased HJURP levels seen in plasma sediments from patients with lung cancer (24). Zhou et al (25) focused on plasma mRNA as a novel non-invasive biomarker for diagnosing lung cancer. Blood specimens were collected from 47 patients with primary lung cancer and 14 healthy individuals. Circulating HJURP and ADAMTS8 mRNAs with superior sensitivity and specificity were revealed, and these molecules were proposed as promising non-invasive biomarkers for the diagnosis of lung cancer. Recently, Wei et al (24) confirmed that the increased expression of HJURP was associated with advanced stage and a poor prognosis, based on a small sample size of 74 patients with NSCLC. Additionally, the study provided clues regarding the role of HJURP as a tumor promoter in NSCLC via the activation of the Wnt/β-catenin pathway.
In the present study, using GSEA, it was observed that the HJURP high expression phenotype was associated with ‘basal transcription factors’, the ‘cell cycle’, ‘homologous recombination’, ‘non-small cell lung cancer’, ‘oocyte meiosis’, the ‘p53 signaling pathway’, ‘pathways in cancer’, ‘RNA degradation’ and ‘spliceosome’. Significant correlations were also found between HJURP expression and immunomodulators, immune subtype and several tumor-infiltrating immune cells, such as activated CD4 T cells. There have been many reports on the molecular genetic alterations of p53 in lung cancer. Dhieb et al (26) found that abnormal immunostaining of p53 was detected in 56.16% of patients with LUAD. Abnormal p53 expression was slightly increased in European compared with Asian populations. It has been reported that lung cancer is strongly influenced by mutations of p53 (27). The role played by immune infiltration in LUAD has been highlighted by certain studies. Varn et al (28) determined that naive B-cell and CD8+ T-cell infiltration was associated with prolonged prognosis, while myeloid cell infiltration was associated with shorter survival times. Wang et al (29) found that increased TTC21A expression was correlated with an increased proportion of immune cells, such as B cells, neutrophils, mast cells and T cells, in patients with LUAD. Previous studies have also found that the expression levels of HJURP mRNA are linked with the regulation of the cell cycle (11,14). Several proteins have been reported to interact with HJURP, including proteins affecting HJURP function and downstream proteins regulated by HJURP. The most well-known molecule regulated by HJURP is the histone H3 variant, centromere-specific protein (CENP)-A. The cooperation between CENP-A and its chaperon HJURP mediates a normal cell cycle, whereas ectopic activation of HJURP is involved in the chromosomal stability and immortality of cancer cells (11). The associations between HJURP expression and ‘basal transcription factors’, ‘homologous recombination’, ‘oocyte meiosis’, ‘RNA degradation’ and ‘spliceosomes’ were the first noted in the present study, although the regulatory mechanisms remain to be further elucidated.
The present study found that the expression of HJURP was significantly increased in patients with LUAD and associated with several clinical features and immune infiltrations. HJURP may be a potentially useful prognostic molecular biomarker of poor survival in LUAD cases. The bioinformatics results were confirmed with RT-qPCR and western blotting analyses in the normal bronchial epithelium (BEAS-2B) and human NSCLC (H1299) cell lines. HJURP mRNA and protein levels were significantly increased in the H1299 cells compared with the levels in the BEAS-2B cells. These findings were consistent with those of a previous study (24) and also demonstrated that higher expression of HJURP was associated with advanced stage, distant metastasis and a poor prognosis in cases of NSCLC. Similarly, higher HJURP levels may be associated with early stage lung cancer (11,25), and HJURP activation seems to play a pivotal role in the immortality of cancer cells (11). Therefore, higher HJURP levels promote a poor prognosis in NSCLC; a precise mechanism for this showing that HJURP promotes tumor cell proliferation, migration and invasion through the Wnt/β-catenin signaling pathway has been reported (24), and the present study has provided further support for this mechanism.
In conclusion, the present study demonstrated that high levels of HJURP expression are correlated with a poor prognosis in patients with LUAD. HJURP may be a promising therapeutic target for the development of anticancer drugs and may also act as a biomarker for LUAD diagnosis.
Acknowledgements
Not applicable.
Funding
This study was supported by Scientific Research Start Plan of Shunde Hospital, Southern Medical University (grant no. SRSP2019013) and The National Natural Scientific Foundation of China (grant no. 81770148).
Availability of data and materials
The dataset analyzed during the present study can be downloaded from TCGA (https://cancergenome.nih.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), UALCAN (http://ualcan.path.uab.edu/index.html) and TISIDB (http://cis.hku.hk/TISIDB/index.php) databases. The remaining data (including PCR and WB experiments) are available from the corresponding author on reasonable request.
Authors' contributions
LC and JY designed the experiments and wrote the manuscript. LC and CZe performed the experiments. LY and WL analyzed and interpreted the results. LC performed the statistical analysis. LC, CZe and CZh assembled the data. JY contributed to every process as a supervisor. LC, CZe, WL, LY, CZh and JY confirm the authenticity of all the raw data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
CENP-A |
centromere protein-A |
|
GSEA |
gene set enrichment analysis |
|
HCC |
hepatocellular carcinoma |
|
HJURP |
Holliday junction-recognizing protein |
|
LUAD |
lung adenocarcinoma |
|
NES |
normalized enrichment score |
|
NSCLC |
non-small cell lung cancer |
|
OS |
overall survival |
|
TIL |
tumor-infiltrating lymphocyte |
References
|
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Shi J, Hua X, Zhu B, Ravichandran S, Wang M, Nguyen C, Brodie SA, Palleschi A, Alloisio M, Pariscenti G, et al: Somatic genomics and clinical features of lung adenocarcinoma: A retrospective study. PLoS Med. 13:e10021622016. View Article : Google Scholar : PubMed/NCBI | |
|
Shah DR and Masters GA: Precision medicine in lung cancer treatment. Surg Oncol Clin N Am. 29:15–21. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Ungureanu A, Zlatian O, Mitroi G, Drocaş A, Ţîrcă T, Călina D, Dehelean C, Docea AO, Izotov BN, Rakitskii VN, et al: Staphylococcus aureus colonisation in patients from a primary regional hospital. Mol Med Rep. 16:8771–8780. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Zlatian O, Balasoiu AT, Balasoiu M, Cristea O, Docea AO, Mitrut R, Spandidos DA, Tsatsakis AM, Bancescu G and Calina D: Antimicrobial resistance in bacterial pathogens among hospitalised patients with severe invasive infections. Exp Ther Med. 16:4499–4510. 2018.PubMed/NCBI | |
|
Tanase A, Colita A, Ianosi G, Neagoe D, Branisteanu DE, Calina D, Docea AO, Tsatsakis A and Ianosi SL: Rare case of disseminated fusariosis in a young patient with graft vs. host disease following an allogeneic transplant. Exp Ther Med. 12:2078–2082. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Foltz DR, Jansen LE, Bailey AO, Yates JR 3rd, Bassett EA, Wood S, Black BE and Cleveland DW: Centromere-specific assembly of CENP-a nucleosomes is mediated by HJURP. Cell. 137:472–484. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Dunleavy EM, Roche D, Tagami H, Lacoste N, Ray-Gallet D, Nakamura Y, Daigo Y, Nakatani Y and Almouzni-Pettinotti G: HJURP is a cell-cycle-dependent maintenance and deposition factor of CENP-A at centromeres. Cell. 137:485–497. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Barnhart MC, Kuich PH, Stellfox ME, Ward JA, Bassett EA, Black BE and Foltz DR: HJURP is a CENP-A chromatin assembly factor sufficient to form a functional de novo kinetochore. J Cell Biol. 194:229–243. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Kato T, Sato N, Hayama S, Yamabuki T, Ito T, Miyamoto M, Kondo S, Nakamura Y and Daigo Y: Activation of Holliday junction recognizing protein involved in the chromosomal stability and immortality of cancer cells. Cancer Res. 67:8544–8553. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Mishra PK, Au WC, Choy JS, Kuich PH, Baker RE, Foltz DR and Basrai MA: Misregulation of Scm3p/HJURP causes chromosome instability in Saccharomyces cerevisiae and human cells. PLoS Genet. 7:e10023032011. View Article : Google Scholar : PubMed/NCBI | |
|
Shuaib M, Ouararhni K, Dimitrov S and Hamiche A: HJURP binds CENP-A via a highly conserved N-terminal domain and mediates its deposition at centromeres. Proc Natl Acad Sci U S A. 107:1349–1354. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Hu B, Wang Q, Wang Y, Chen J, Li P and Han M: Holliday junction-recognizing protein promotes cell proliferation and correlates with unfavorable clinical outcome of hepatocellular carcinoma. Onco Targets Ther. 10:2601–2607. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Valente V, Serafim RB, de Oliveira LC, Adorni FS, Torrieri R, Tirapelli DP, Espreafico EM, Oba-Shinjo SM, Marie SK, Paçó-Larson ML and Carlotti CG Jr: Modulation of HJURP (Holliday junction-recognizing protein) levels is correlated with glioblastoma cells survival. PLoS One. 8:e622002013. View Article : Google Scholar : PubMed/NCBI | |
|
Montes de Oca R, Gurard-Levin ZA, Berger F, Rehman H, Martel E, Corpet A, de Koning L, Vassias I, Wilson LO, Meseure D, et al: The histone chaperone HJURP is a new independent prognostic marker for luminal A breast carcinoma. Mol Oncol. 9:657–674. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Li L, Li X, Meng Q, Khan AQ and Chen X: Increased expression of Holliday junction-recognizing protein (HJURP) as an independent prognostic biomarker in advanced-stage serous ovarian carcinoma. Med Sci Monit. 24:3050–3055. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Askar N, Cirpan T, Toprak E, Karabulut B, Selvi N, Terek MC, Uslu R, Sanli UA and Goker E: Arsenic trioxide exposure to ovarian carcinoma cells leads to decreased level of topoisomerase II and cytotoxicity. Int J Gynecol Cancer. 16:1552–1556. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Zeng C, Shao Z, Wei Z, Yao J, Wang W, Yin L, YangOu H and Xiong D: The NOTCH-HES-1 axis is involved in promoting Th22 cell differentiation. Cell Mol Biol Lett. 26:72021. View Article : Google Scholar : PubMed/NCBI | |
|
Moreno Leon L, Gautier M, Allan R, Ilié M, Nottet N, Pons N, Paquet A, Lebrigand K, Truchi M, Fassy J, et al: The nuclear hypoxia-regulated NLUCAT1 long non-coding RNA contributes to an aggressive phenotype in lung adenocarcinoma through regulation of oxidative stress. Oncogene. 38:7146–7165. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Ramos M, Franch P, Zaforteza M, Artero J and Durán M: Completeness of T, N, M and stage grouping for all cancers in the mallorca cancer registry. BMC Cancer. 15:8472015. View Article : Google Scholar : PubMed/NCBI | |
|
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Ru B, Wong CN, Tong Y, Zhong JY, Zhong SS, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, et al: TISIDB: An integrated repository portal for tumor-immune system interactions. Bioinformatics. 35:4200–4202. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Wei Y, Ouyang GL, Yao WX, Zhu YJ, Li X, Huang LX, Yang XW and Jiang WJ: Knockdown of HJURP inhibits non-small cell lung cancer cell proliferation, migration, and invasion by repressing Wnt/β-catenin signaling. Eur Rev Med Pharmacol Sci. 23:3847–3856. 2019.PubMed/NCBI | |
|
Zhou D, Tang W, Liu X, An HX and Zhang Y: Clinical verification of plasma messenger RNA as novel noninvasive biomarker identified through bioinformatics analysis for lung cancer. Oncotarget. 8:43978–43989. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Dhieb D, Belguith I, Capelli L, Chiadini E, Canale M, Bravaccini S, Yangui I, Boudawara O, Jlidi R, Boudawara T, et al: Analysis of genetic alterations in tunisian patients with lung adenocarcinoma. Cells. 8:5142019. View Article : Google Scholar : PubMed/NCBI | |
|
Zheng MM, Li YS, Jiang BY, Tu HY, Tang WF, Yang JJ, Zhang XC, Ye JY, Yan HH, Su J, et al: Clinical utility of cerebrospinal fluid cell-free DNA as liquid biopsy for leptomeningeal metastases in ALK-rearranged NSCLC. J Thorac Oncol. 14:924–932. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Varn FS, Tafe LJ, Amos CI and Cheng C: Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy. Oncoimmunology. 7:e14310842018. View Article : Google Scholar : PubMed/NCBI | |
|
Wang W, Ren S, Wang Z, Zhang C and Huang J: Increased expression of TTC21A in lung adenocarcinoma infers favorable prognosis and high immune infiltrating level. Int Immunopharmacol. 78:1060772020. View Article : Google Scholar : PubMed/NCBI |
