The expression of tyrosine kinase and threonine promotes the progression of lung adenocarcinoma and is linked to a negative prognosis

Abstract

Threonine and tyrosine kinase (TTK) is a crucial element of the spindle assembly checkpoint. This study aimed to investigate the prognostic value of TTK in LUAD and its correlation with immunity. Data from The Cancer Genome Atlas was used in the study to examine TTK expression in both lung adenocarcinoma (LUAD) and normal tissue samples. To investigate the correlation between TTK expression and overall survival, multivariable analysis and Kaplan–Meier survival curves were utilized in this study. The biological function of TTK in LUAD was investigated using LinkedOmics and the STRING database for interacting genes/proteins. Furthermore, examine the relationship between TTK expression and its DNA methylation. Evaluated the correlation between the expression of the TTK gene and the infiltrating levels of 24 different types of immune cells. Additionally, cell culture validation was conducted in A549 cells and BEAS-2B cells. Expression levels of TTK were significantly increased in LUAD samples from the The Cancer Genome Atlas compared to normal tissue samples. TTK plays a role in cell cycle checkpoints, meiotic recombination, and DNA replication. TTK demonstrates a strong functional correlation with 10 partner genes. Furthermore, there is an inverse relationship between TTK DNA methylation and both TTK expression and overall survival rates among LUAD patients. TTK expression has also been found to be associated with immune cell presence, potentially impacting immune cell infiltration and serving as a predictive factor for patient prognosis. Cell culture investigations revealed significantly elevated levels of TTK mRNA and protein expression in A549 cells compared to BEAS-2B cells. TTK expression in LUAD is correlated with disease advancement, unfavorable prognosis, and alterations in the immune cell composition. TTK levels could offer valuable insights into the progression and prognosis of LUAD, as well as potential implications for immunotherapy strategies.

1. Introduction

Lung cancer is a common and serious ailment that contributes significantly to morbidity and mortality rates on a global scale.^[1,2] Lung cancer is generally classified into 2 main subgroups based on histological characteristics: non-small cell lung cancer (NSCLC) and small cell lung cancer, comprising 85% and 15% of cases, respectively.^[3] Notably, among all NSCLC subtypes, lung adenocarcinoma (LUAD) is the most commonly observed histological type. Lung cancer not only affects the physical and mental health of patients, but also brings them huge medical expenses and economic pressure, which has become an important public health issue faced by countries around the world. Therefore, seeking new biomarkers is extremely important for the treatment of LUAD.

Threonine and tyrosine kinases (TTK) play a crucial role in the spindle assembly checkpoint, a critical mechanism that oversees chromosome segregation.^[4] The spindle assembly checkpoint serves as a vital surveillance system, halting the start of mitosis until all chromosomes are properly aligned on spindle microtubules to prevent incorrect chromosome segregation.^[5] Increased levels of TTK expression could be a factor in the formation of subsequent solid tumors.^[6] It has been found in recent studies that TTK is present in various malignant tumors, where it plays a role as an oncogene in glioma, breast cancer, and pancreatic cancer.^[7-9] Because of its critical role in the process of cell division, TTK has the potential to be used as a fresh focus for treatment and a marker for LUAD.

At present, The Cancer Genome Atlas (TCGA) stands as the most extensive repository of cancer genetic details, encompassing clinical specimens and a range of datasets including gene expression information, copy number alterations, DNA methylation patterns, and data on single nucleotide polymorphisms.^[10] This research employed the TCGA database to examine the connection between TTK expression levels and prognosis, along with immune cell infiltration, through the analysis of TCGA sequencing data. The main objective of the study was to recognize TTK as a potential biomarker for predicting the responsiveness to immunotherapy in individuals diagnosed with LUAD, thus providing beneficial insights for their treatment strategies.

2. Methods and materials

2.1. Data collection

The TCGA (https://portal.gdc.cancer.gov) serves as an open data platform for extensive cancer genome projects, offering clinical pathology data for 33 distinct cancer types. For this research, data from clinical records and RNA sequencing (RNA-seq) analysis of patients with LUAD were obtained from the TCGA repository, including information on 539 instances of healthy tissue and 59 instances of neighboring tissue. To facilitate further investigation, HTSeq-FPKM data and clinical information were transformed into TPM format. Access to the database is open to the public and does not require permission from an ethics committee. The research methodology and workflow are visually represented in Figure 1.

Figure 1.

The flowchart outlines the process of identifying the TTK function in LUAD. DNA= deoxyribonucleic acid, GSEA = Gene Set Enrichment Analysis, LUAD = lung adenocarcinoma, TTK = threonine and tyrosine kinases.

2.2. The GEO and HPA databases

The Gene Expression Omnibus (GEO) database, maintained by the National Center for Biotechnology Information, serves as a comprehensive gene expression resource. This study utilized 3 GEO datasets, namely GSE33532 (80 tumor samples, 20 normal samples, on GPL570 platforms), GSE140797 (7 tumor samples, 7 normal samples, utilizing GPL13497 platforms), and GSE136043 (5 tumor samples, 5 normal samples, using GPL13497 platforms). The datasets were utilized to verify the mRNA expression levels of TTK in both LUAD and healthy tissues.

Genes that showed differential expression were pinpointed by applying filtering standards of |Log FC| > 2 and P < .05 within the GEO datasets. The outcomes were visually demonstrated through volcano plots. Through the utilization of principal component analysis plots, the clustering characteristics between tumor and normal samples were assessed.

The R software (Auckland, New Zealand) packages “GEOquery,” “limma,” and “ggplot2” were employed for data analysis.

The Human Protein Atlas (HPA) provides comprehensive information on the transcriptome and proteome of different human samples such as tissues, cells, and pathological conditions. The study analyzed TTK protein expression in LUAD and normal samples by examining IHC staining images from the HPA database, including protein immunohistochemistry details in both tumor and healthy tissues samples.

2.3. Clinical prognosis analysis, model construction and evaluation

Data from patients in the TCGA were analyzed on the Xiantao platform’s (https://www.xiantaozi.com/) clinical significance module to study prognostic factors like overall survival (OS), disease specific survival (DSS), and progress free interval. Cox regression and Kaplan–Meier methods were used for the analyses. Levels of gene expression were grouped as low and high based on the median value of TTK. The association between TTK gene expression and clinicopathological characteristics was assessed using the Wilcoxon signed rank test and logistic regression. The impact of TTK gene expression on survival and other clinical factors was examined using multivariable Cox regression models. An analysis threshold of P < .05 was established. Survival probabilities at 1, 5, and 10 years were predicted using results from the Cox regression model and independent prognostic variables identified through multivariable analysis. Calibration curves were utilized to compare predicted probabilities to actual outcomes.

2.4. TTK-related genetic analysis

The LinkedOmics database (http://www.linkedomics.org/login.php) contains a wide range of multi-omics data for various cancer types from the TCGA and CPTAC cohorts. For this study, the focus was specifically on the LUAD cohort to investigate the differentially expressed genes associated with TTK using the “LinkFinder” module. Through the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the “Function” module, insights were gained into the potential biological functions and pathways influenced by TTK in LUAD. Additionally, the use of the STRING (https://string-db.org/) network platform allowed for the integration of comprehensive protein–protein interaction data. By importing TTK expression data into the STRING platform, a detailed protein–protein interaction network diagram was constructed with a significance threshold set at a confidence score above 0.9. This network analysis further elucidated the potential interactions and relationships of TTK with other proteins within the context of LUAD.

Perform least absolute shrinkage and selection operator (LASSO) regression analysis on TTK-related functional partner genes, and further analyze the filtered variables through LASSO regression analysis. The analysis utilized 1000-fold cross-validation to prevent overfitting. The optimal value of the penalty parameter λ is set according to the minimum criterion. A risk score for each patient is generated based on a unified algorithm. Risk value = 1 risk gene expression × (1coef) + 2 risk gene expression × (2coef) +… + n risk gene expression × (ncoef). In the TCGA data, the LUAD cohort was split into high-risk and low-risk categories based on the median risk score. To generate the prognostic LASSO variable path, the R package “glmnet” was employed, while the risk factor map was visualized using the “ggplot2” package.

2.5. TTK function analysis

Analysis of GO enrichment was conducted on TTK gene expression, focusing on 3 main categories: biological processes, cellular components, and molecular functions. The KEGG database was employed to investigate biological functions and pathways, and both GO and KEGG analyses were completed using the clusterProfiler program in R (version 3.6.3).

Gene Set Enrichment Analysis (GSEA) is a technique used to assess the impact of genes on traits by examining patterns in gene and trait relationships within a specific gene group. In each investigation, the genome underwent 1000 rounds of sequencing via GSEA to enhance the related pathway, with a specific focus on TTK gene expression. Significant statistical discoveries in GSEA analysis were pinpointed by considering an adjusted P-value of <0.05 and a false discovery rate of <0.25. The results of the enrichment analysis were determined using normalized enrichment scores and adjusted P-values. Utilizing the Cluster Profiler tool, the GSEA was conducted and the results were visually depicted.

2.6. DNA methylation analysis

Analysis of TTK promoter methylation levels in LUAD was conducted using the TCGA analysis in UALCAN (https://ualcan.path.uab.edu/index.html) and cBioPortal (https://www.cbioportal.org/) portal. This analysis allowed for an in-depth examination of the methylation patterns in this specific region of the genome. Moreover, the MethSurv (https://biit.cs.ut.ee/methsurv/) tool was used to explore the relationship between specific probes, changes in methylation, and the likelihood of survival in patients with LUAD. This comprehensive approach provided valuable insights into the potential impact of TTK promoter methylation on patient outcomes in LUAD.

2.7. Immune cell infiltration analysis

The ssGSEA method uses 24 different immune cell types to analyze tumor infiltration. The research analyzed Spearman correlations to assess the levels of immune cell infiltration in subgroups with high and low TTK gene expression. Additionally, it measured the strength of correlation between TTK gene expression and the infiltration levels of 24 different immune cell types. TISIDB (http://cis.hku.hk/TISIDB/) is a virtual tool created to explore the relationship between tumors and the immune system. The use of TISIDB in the “subtype” section allowed for the analysis of TTK expression distribution among various immune and molecular subtypes. The immune infiltration results were used to analyze the association between TTK gene expression and immune infiltration in the “Xiantao Tool” module, as well as the link between the level of immune cell infiltration and the values observed in each TTK gene expression subgroup. A statistically significant outcome was determined with a P-value below .05.

2.8. Cell culture

The study utilized Human BEAS-2B and A549 cells obtained from Procell Life Science Technology Co, Ltd. The Human BEAS-2B cells were labeled with batch number CL-0496, and the A549 cells with batch number CL-0016. The cells were cultured in MEM medium with 10% fetal bovine serum and 1% penicillin-streptomycin. Cell culture medium was altered every 2 days, and subculturing occurred every 3 to 4 days to maintain cell survival and growth.

2.9. Quantitative Real‐Time PCR

The expression levels of TTK mRNA in BEAS-2B and A549 cells were assessed through RNA extraction from cell cultures. Around 1 gram of total RNA was converted into cDNA through reverse transcription using a kit manufactured by Vazyme. Quantitative real-time PCR (QRT-PCR) was conducted using a Quantitative SYBR Green PCR Kit on a SLAN-96S Detection System. The process of polymerase chain reaction (PCR) commenced with a denaturation step lasting 30 seconds at a temperature of 95°C. This was succeeded by 40 cycles of 10 seconds at 95°C and 30 seconds at 60°C. The primers used were as follows: TTK forward, 5′-GCCCGAAAA GTTAATACAGAGAAAC-3′ and reverse, 5′-TGACAAGCAGGTGGAAAGTCA-3′; GAPDH forward, 5′-CATGTTGCAACCG GGAAGGA-3′ and reverse, 5′-GCCCAATACGACCAAATCAGAGA-3′. Normalization of mRNA expression was performed relative to GAPDH using the 2^−ΔΔCt method, and all assays were conducted in triplicate.

2.10. Western blot

TTK protein expression was identified in the study. Proteins from cells were isolated with RIPA lysate and measured using the BCA technique. The extracted proteins were then subjected to electrophoresis on an SDS-PAGE gel and later transferred onto a PVDF membrane. Subsequently, a 5% skim milk solution was used to block the membrane, followed by an overnight incubation with primary antibodies (TTK, GAPDH) at 4°C. The primary antibodies were then washed off the following day, and a secondary antibody was applied and incubated for 2 hours. Chemiluminescence detection was performed using the ECL Immobilon Western Kit, and Image J software was employed for quantifying grayscale intensities.

2.11. Statistical analysis

SPSS 19.0 software was used for statistical analysis, with data shown as mean ± standard deviation. To compare differences between 2 groups, the t-test was conducted. In order to evaluate differences in protein expression among different groups, the chi-square test was utilized. Statistical analysis and data processing were conducted using the R software (version 3.6.3) and its associated packages. The “survival” software packages were utilized for conducting the Kaplan–Meier method, log rank test, and multivariate Cox regression model. The calculation of the area under the curve was achieved through the receiver operating characteristics (ROC) function within the “survival” package in R software. Statistical significance was determined at a threshold of P < .05.

3. Results

3.1. TTK expression is increased in tumor tissues in comparison to normal tissues

A study of TCGA data compared TTK expression levels in normal tissues and different types of cancers. The findings revealed a significant rise in TTK expression in cancers including BLCA, BRCA, CESC, CHOL, LUAD, LUSC, among others, as illustrated in Figure 2A, B, using both unpaired and paired sample analysis.

Figure 2.

TTK mRNA expression analysis in 34 cancer types in the TCGA database. (A) Unpaired analysis; (B) paired analysis. ACC = adrenocortical carcinoma, BLCA = bladder urothelial carcinoma, BRCA = breast invasive carcinoma, CESC = cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL = cholangiocarcinoma, COAD = colon adenocarcinoma, DLBC = lymphoid neoplasm diffuse large B-cell lymphoma, ESCA = esophageal carcinoma, GBM = glioblastoma multiforme, HNSC = head and neck squamous cell carcinoma, KICH = kidney chromophobe, KIRC = kidney renal clear cell carcinoma, KIRP = kidney renal papillary cell carcinoma, LAML = acute myeloid leukemia, LGG = brain lower grade glioma, LIHC = liver hepatocellular carcinoma, LUSC = lung squamous cell carcinoma, MESO = mesothelioma, mRNA = messenger ribonucleic acid, OV = ovarian serous cystadenocarcinoma, PAAD = pancreatic adenocarcinoma, PCPG = pheochromocytoma and paraganglioma, PRAD = prostate adenocarcinoma, READ = rectum adenocarcinoma, SARC = sarcoma, SKCM = skin cutaneous melanoma, STAD = stomach adenocarcinoma, TCGA = The Cancer Genome Atlas, TGCT = testicular germ cell tumors, THCA = thyroid carcinoma, THYM = thymoma, TTK = threonine and tyrosine kinases, UCEC = uterine corpus endometrial carcinoma, UVM = uveal melanoma.

We utilized the TCGA database to predict the expression levels of TTK messenger RNA (mRNA) in 59 paracancerous tissues and 539 normal tissue samples as depicted in Figure S1A, Supplemental Digital Content, https://links.lww.com/MD/R485. An analysis revealed that levels of TTK expression were elevated in LUAD tissues when compared to normal tissues in the paired samples, as depicted in Figure S1B, Supplemental Digital Content, https://links.lww.com/MD/R485. Additionally, we conducted a comparison of TTK expression between normal tissue samples (data sourced from GTEx) and paracancerous tissues, revealing an upregulation of TTK expression in LUAD tissues (P < .001), as illustrated in Figure S1C, Supplemental Digital Content, https://links.lww.com/MD/R485. TTK expression was positively correlated with the clinical stage of LUAD (r = 0.17, P = .000119; Fig. S1D, Supplemental Digital Content, https://links.lww.com/MD/R485). Afterward, we created a ROC curve to assess the diagnostic importance of TTK expression through the comparison of TTK levels in nearby LUAD tissue and normal tissue samples (GTEx). The analysis resulted in an area under the curve value of 0.965 (CI = 0.950–0.979), indicating that TTK exhibits high diagnostic and prognostic accuracy in LUAD, as shown in Figure S1E, Supplemental Digital Content, https://links.lww.com/MD/R485.

To validate the heightened expression of TTK in LUAD tissues obtained from the TCGA database, we utilized 3 external datasets (GSE33532, GSE140797, and GSE136043) from the GEO database to compare mRNA levels of TTK in normal and cancerous tissues. The differential genes in these datasets were analyzed based on differential gene screening criteria, revealing both up-regulated and down-regulated genes across all 3 datasets (Fig. S2A–C, Supplemental Digital Content, https://links.lww.com/MD/R485). The principal component analysis plots (Fig. S2D–F, Supplemental Digital Content, https://links.lww.com/MD/R485) illustrate the distribution of PC1 and PC2, with common proportions of 32.3% in GSE33532, 52.4% in GSE140797, and 59.2% in GSE136043. These proportions suggest abundant differential gene expression between datasets, highlighting significant differences between tumor and normal tissue groups. Figure S2H–J, Supplemental Digital Content, https://links.lww.com/MD/R485 reveal increased TTK mRNA levels in cancerous tissues and decreased levels in healthy tissues, in alignment with data from the TCGA database. The integrated examination of both TCGA and GEO databases provides evidence that TTK is significantly upregulated in tumor tissues, suggesting its possible role as a biomarker for LUAD.

Immunohistochemistry analysis was conducted to investigate TTK expression in both LUAD and normal tissues. The results showed a marked decrease in TTK protein levels in normal tissues in contrast to a substantial increase in LUAD tissues (Fig. S3A, B, Supplemental Digital Content, https://links.lww.com/MD/R485).

3.2. Relationship between TTK expression and clinical pathology in patients with LUAD

Examining the relationship between TTK levels in LUAD and clinical pathological features, TTK expression was evaluated in various categories including different pathologic T and M stages, primary therapy results, gender, N stages, and smoking status (Fig. 4A–F, Supplemental Digital Content, https://links.lww.com/MD/R485). The results indicate that TTK expression is elevated in T2 stage, M1 stage, PD group, male group, patients aged≤65 years old, and smokers.

3.3. The relationship between TTK expression and the prognosis of LUAD

The study investigated the correlation between TTK expression and prognostic markers (OS, DSS, FPI) by analyzing data from the TCGA database. The findings revealed that reduced TTK expression was linked to more favorable outcomes in terms of OS (HR = 1.61 [1.20–2.15], P = .001), DSS (HR = 1.81 [1.25–2.62], P = .002), and progress free interval (HR = 1.62 [1.25–2.12], P < .001). Specifically, higher TTK expression was associated with increased survival rates in patients diagnosed with LUAD. These findings indicate that the levels of TTK expression could potentially be used as a prognostic tool for LUAD patients, underscoring the need for additional investigation into the specific impact of TTK on the progression of lung cancer and the outcomes of patients. Further analysis by subgroups indicated that within different clinical parameters such as smoking status, pathologic stage, pathologic T stage, N stage, gender, and age categories, patients with low expression of TTK in LUAD had a superior OS outcome compared to those with high expression. These results are graphically presented in Figure S5A–C, Supplemental Digital Content, https://links.lww.com/MD/R485 and Figure S6A–F, Supplemental Digital Content, https://links.lww.com/MD/R485.

Following this, a series of univariate and multivariate Cox regression analyses were carried out to investigate the relationship between clinical features and OS with respect to TTK expression. The results presented in Table 1 indicated noteworthy connections between OS length and T2, T3, and T4 phases, N1 and N2 phases, M1 phase, as well as the degree of TTK expression as determined by the univariate Cox regression analysis. Furthermore, the multivariable Cox regression analysis established that T3 and T4 phases, N1 and N2 stages, and the degree of TTK expression were significantly associated with OS duration. These results suggest that T phase, N phase, and TTK expression could be deemed as distinct predictive factors for OS period in individuals with LUAD.

Table 1.

Univariate and multivariate Cox regression analyses were performed to assess the impact of clinicopathological characteristics and TTK expression on overall survival time.

Characteristics	Total (N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	P-value	Hazard ratio (95% CI)	P-value
Pathologic T stage	527
T1	176	Reference		Reference
T2	285	1.507 (1.059–2.146)	.023	1.437 (0.912–2.265)	.118
T3	47	2.964 (1.762–4.986)	<.001	3.671 (1.947–6.925)	<.001
T4	19	3.357 (1.767–6.376)	<.001	2.191 (1.039–4.619)	.039
Pathologic N stage	514
N0	345	Reference		Reference
N1	96	2.293 (1.632–3.221)	<.001	2.010 (1.362–2.968)	<.001
N2	71	3.133 (2.154–4.559)	<.001	2.747 (1.790–4.218)	<.001
N3	2	0.000 (0.000–∞)	.994	0.000 (0.000–∞)	.993
Pathologic M stage	381
M0	356	Reference		Reference
M1	25	2.176 (1.272–3.722)	.005	1.693 (0.919–3.116)	.091
Gender	530
Female	283	Reference
Male	247	1.087 (0.816–1.448)	.569
Age	520
≤65	257	Reference
>65	263	1.216 (0.910–1.625)	.186
TTK	530
Low	266	Reference		Reference
High	264	1.609 (1.204–2.149)	.001	1.532 (1.090–2.152)	.014

CI = confidence interval, TTK = threonine and tyrosine kinases.

A prognostic risk score for LUAD was created based on TNM stage, gender, age, and TTK expression (Fig. 3A). The impact of pathological stage on OS time in LUAD patients is crucial. The order of importance is T stage, M stage, and TTK expression level. Calibration plots were employed to evaluate the accuracy of model predictions (Fig. 3B). TTK expression is a valuable tool for predicting survival rates among patients with LUAD. Specifically, it has been shown to be particularly effective in predicting both 1-year and 5-year survival rates with a high degree of accuracy. In conclusion, TTK expression levels can be used as a reliable prognostic indicator in patients diagnosed with LUAD.

Figure 3.

Prognostic analysis of TTK expression. (A) Nomogram model for predicting 1, 5, and 10-year OS in LUAD patients. The length of the lines in the nomogram represents the impact of the corresponding variable on OS time. Adding up the scores of every factor can determine the likelihood of OS occurring within 1, 5, or 10 years. (B) Calibration plot showing model prediction accuracy determined using multifactor Cox regression analysis. LUAD = lung adenocarcinoma, OS = overall survival, TTK = threonine and tyrosine kinases.

3.4. The GSEA analysis of TTK1 in LUAD

The clinical importance of TTK in LUAD has prompted further investigation into the regulatory mechanisms linked to this protein. Through GSEA enrichment analysis, it was revealed that TTK primarily influences the Retinoblastoma gene in cancer, cell cycle checkpoints, meiotic recombination, DNA replication, and meiosis signaling pathways, as depicted in Figure S7, Supplemental Digital Content, https://links.lww.com/MD/R485.

3.5. Analysis of TTK-related genes

To further investigate the biological function of TTK in LUAD, we employed the “LinkFinder” tool in the LinkedOmics platform to discover genes that are expressed differently and linked to TTK in the TCGA-LUAD dataset. Figure 4A illustrates the results of the Spearman test, revealing 3341 genes positively correlated with TTK (marked by dark red dots) and 4025 genes negatively correlated (dark green dots) at a threshold false discovery rate < 0.0001. Heatmaps in Figure 4B, C display the top 50 positively and negatively correlated genes.

Figure 4.

GOKEGG enrichment analysis and PPI interaction network analysis of TTK-related genes. (A) Volcano plot of correlation between TTK and other gene expression levels; (B) TTK-related up-regulated gene correlation heat map; (C) TTK-related down-regulated gene correlation heat map, red represents positively correlated genes, and blue represents negatively correlated genes; (D) BP histogram; (E) CC histogram; (F) MF histogram; (G) KEGG histogram; (H) PPI interaction structure diagram of TTK; (I) GOKEGG enrichment analysis bubble chart of 10 key genes related to TTK function. BP = biological process, CC = cellular component, GOKEGG = gene ontology Kyoto encyclopedia of genes and genomes, KEGG = Kyoto Encyclopedia of Genes and Genomes, MF = molecular function, PPI = protein–protein interaction, TTK = threonine and tyrosine kinases.

GO term annotation (Fig. 4D–F) revealed that the differential expression of TTK-related genes is associated with processes such as chromosome segregation, mitotic cell cycle phase transition, chromosomal region, and catalytic activity acting on DNA. Furthermore, KEGG pathway analysis indicated that these differentially expressed genes are primarily involved in cell cycle, spliceosome, and RNA transport (Fig. 4G).

Protein network interaction analysis was conducted on TTK (Fig. 4H), revealing the top 10 functional partner genes with strong correlation. These genes, including BUB1, MAD2L1, NDC80, BUB1B, CDC20, NUF2, MAD1L1, KIF11, DLGAP5, and NCAPG (Table 2), are likely implicated in the pathogenesis of LUAD alongside TTK. Furthermore, GO/KEGG enrichment analysis indicated that these TTK-related genes are predominantly associated with processes such as cell cycle and mitotic nuclear division, as illustrated in Figure 4I.

Table 2.

TTK-related gene details.

Gene symbol	Annotation	Score
BUB1	Budding uninhibited by benzimidazole 1	0.999
MAD2L1	Mitotic arrest defcient 2 like 1	0.995
NDC80	Nuclear division cycle 80	0.992
BUB1B	BUB1 mitotic checkpoint serine/threonine kinase B	0.991
CDC20	Cell division cycle 20	0.990
NUF2	Ndc80 kinetochore complex component	0.989
MAD1L1	Mitotic arrest defcient 1 like 1	0.987
KIF11	Condensate I complex subunit G	0.984
DLGAP5	Large homolog associated protein 5	0.977
NCAPG	Non-SMC condensate complex subunit G	0.973

TTK = threonine and tyrosine kinases.

To assess the prognostic significance of 10 genes associated with TTK function in LUAD, a prognostic model was developed using LASSO regression analysis (Fig. 5A). The LASSO regression results were analyzed to determine variable coefficient values, lambda logarithmic values, and L1 regularization values. The data was visualized to create a LASSO variable trajectory plot, where the x-axis represents the lambda logarithm (log(λ)) and the y-axis represents the variables. The trajectories of the coefficients of 10 genetic variables were depicted by 10 curves. As the parameters increased, the coefficients of DLGAP5, NUF2, and CDC20 eventually converged to 0, indicating their importance (Fig. 5B). Following this, a risk map was created utilizing the aforementioned trio of genes, categorizing individuals with LUAD into high-risk and low-risk cohorts based on median risk scores. Analysis of survival unveiled a clear correlation between risk score and patient fatality, indicating that elevated risk scores were associated with increased mortality rates. The expression levels of DLGAP5, NUF2, and CDC20 in high and low-risk groups were positively associated with risk scores, suggesting that higher gene expression was linked to increased risk scores and mortality rates (Fig. 5C).

Figure 5.

(A) TTK function-related genes prognostic model using LASSO regression; (B) trajectory diagram of variables in the prognostic model using LASSO regression; (C) diagram of risk factors. LASSO = least absolute shrinkage and selection operator, TTK = threonine and tyrosine kinases.

3.6. TTK DNA methylation is associated with survival in LUAD patients

Based on data from the cBioPortal database, there is an inverse relationship noted between TTK levels of expression and TTK methylation levels (Fig. S8A, Supplemental Digital Content, https://links.lww.com/MD/R485; r = −0.110, P = .0226). Analysis on the UALCAN portal revealed that patients with normal TTK1 promoter methylation levels were significantly higher compared to those with tumors (Fig. S8B, Supplemental Digital Content, https://links.lww.com/MD/R485, P = 1e−12). Additional analysis of TTK DNA methylation levels and the prognostic significance of each distinct CpG site was carried out using the MethSurv tool, revealing that cg19323245 exhibited the greatest DNA methylation levels in Figure S8C, Supplemental Digital Content, https://links.lww.com/MD/R485. These findings suggest an inverse correlation between DNA methylation, TTK expression, and OS in LUAD patients.

3.7. TTK gene expression to global gene expression pattern

Studying the TTK gene expression profile can improve our comprehension of its biological function in LUAD. The results suggested a link between the levels of up-regulated and down-regulated genes and the expression of the TTK gene (log FC > 1 and P < .05; Fig. 6A). Moreover, a heat map displayed the top 21 genes with abnormal expression (Fig. 6B), showing varying z-scores of these 21 genes in the TTK high and low expression groups. GO enrichment analysis based on TTK gene expression revealed significant connections with histone deacetylase binding, hormone activity, GABA-A receptor activity, dendrite membrane, GABA receptor complex, neuron projection membrane, and others. Regarding KEGG pathway enrichment, the focus was mainly on neuroactive ligand-receptor interaction, Alcoholism, and systemic lupus erythematosus pathways (Fig. 6C).

Figure 6.

Differential expression and enrichment analysis of TTK genes. (A) Volcano plot based on TTK expression pattern, genes that are up-regulated are represented in red, while genes that are down-regulated are represented in blue; (B) expression levels of TTK genes were used to generate a heat map showing 21 up- or down-regulated genes; (C) the GO and KEGG enrichment results of differentially expressed genes filtered according to TTK gene expression were analyzed by using Metascape database. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, TTK = threonine and tyrosine kinases.

3.8. The correlation between TTK gene expression and the infiltration of immune cells

In examining the relationship between TTK gene expression and immune cells in LUAD, notable variations are observed in TTK gene expression levels across different infiltrating immune cells. For B cells, CD8 cells, DC, macrophages, mast cells, T helper cells, T cells, and NK cells, a pattern emerges where lower TTK expression levels correspond to higher immune cell enrichment scores, while no distinction was found among Neutrophils (Fig. 7A). LUAD was classified into 5 kinds of immune subtype, C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), C6 (TGF-b dominant), TTK Expression was significantly correlated with C1, C2, C3, C4 and C6 isoforms (P = 2.49e−44), Figure 7B. The analysis encompassed 24 immune cell subtypes, revealing a positive correlation between TTK gene expression and Th2 cells, Tgd, T helper cells, and a negative association with mast cells, Eosinophils, iDC, Th17 cells (Fig. 7C–I), suggesting a linkage between TTK expression and immune responses.

Figure 7.

Correlation between TTK gene expression and immune cells. (A) Assessing the variance in immune cell subpopulation enrichment between high-expression and low-expression TTK gene groups; (B) investigating the association of TTK gene expression with immune cell infiltration; (C) examining the connection between TTK expression and molecular subtypes; (D–I) analyzing the correlation of TTK gene expression with tumor microenvironment. TTK = threonine and tyrosine kinases.

3.9. qRT-PCR and western blot verification

The presence of messenger RNA (mRNA) for TTK was validated in both A549 and BEAS-2B cells. A549 cells exhibited considerably higher mRNA levels of TTK compared to BEAS-2B cells. In line with this, the protein expression of TTK in A549 cells was notably increased relative to BEAS-2B cells. The results illustrated in Figure 8, imply that TTK could potentially act as a predisposing factor for LUAD.

Figure 8.

The mRNA and protein levels of TTK in cells. GAPDH = glyceraldehyde-3-phosphate dehydrogenase, mRNA = messenger ribonucleic acid, TTK = threonine and tyrosine kinases.

4. Discussion

Lung cancer is the deadliest tumor with the highest incidence and death rate globally. Since he onset of lung cancer is insidious and the early symptoms are atypical, patients are usually diagnosed at a late stage, which greatly reduce patients’ survival time. LUAD is the most common kind of NSCLC. At present, therapy options for NSCLC encompass standard chemotherapy and radiation, as well as targeted and immune therapies. The application of immunotherapy in clinical settings has significantly increased survival rates and improved the overall well-being of advanced NSCLC patients. Tumor immunotherapy primarily targets tumor cells by mobilizing the body’s natural antitumor immune response, offering long-lasting therapeutic benefits and minimal side effects.^[11]

TTK protein kinase, also referred to as unipolar spindle 1 or Mps1, plays a crucial role as a dual specificity kinase in forming mitotic checkpoint complexes. These complexes are responsible for regulating cytokinesis, coordinating responses to DNA damage, and facilitating correct alignment of chromosomes during cell division.^[12] Studies have confirmed that TTK gene polymorphisms may affect the risk of gastric cancer and adenocarcinoma development.^[13] A study conducted in a lab environment demonstrated that reducing TTK levels resulted in decreased growth, invasion, and movement of prostate cancer cells, while also stimulating apoptosis. Follow-up experiments conducted in living organisms revealed that reducing TTK levels suppressed the development of tumors in mice injected with prostate cancer cells.^[14] The TTK gene expression in LUAD was found to significantly increase compared to normal tissue based on the analysis of the TCGA database and validation in the GEO database. The expression of TTK was found to be associated with clinical survival prognosis and characteristics. ROC curve demonstrated that TTK has high diagnostic and prognostic accuracy in LUAD and can serve as an independent predictor. Cell experiments confirmed these findings, showing significantly higher mRNA and protein expression of TTK in tumor cells compared to normal cells, consistent with the database analysis results. These results are consistent with our findings, so TTK genes are expected to become important therapeutic targets, and TTK is a potential new biomarker for LUAD.

TTK has a higher correlation with BUB1, MAD2L1, NDC80, BUB1B, CDC20, NUF2, MAD1L1, KIF11, DLGAP5, and NCAPG genes. LASSO regression analysis revealed that DLGAP5, NUF2, and CDC20 are particularly important. Higher gene expression of these genes is associated with increased mortality rates in LUAD patients. Previous research has shown that the expression levels of DLGAP5, NUF2, and CDC20 in LUAD are significantly linked to patient survival rates. DLGAP5 is a protein known for its role in regulating microtubule dynamics and cell mitosis. In LUAD, increased expression of DLGAP5 is closely linked to tumor cell proliferation, invasiveness, and disease prognosis. DLGAP5 facilitates cell cycle progression by activating the PLK1 signaling pathway, leading to accelerated entry into mitosis, ultimately promoting tumor proliferation and enhancing survival rates. Furthermore, elevated levels of DLGAP5 are also correlated with chemotherapy resistance, suggesting its potential impact on tumor cell response to treatment.^[15] NUF2, a significant centromere protein involved in cell division, plays a crucial role in the accurate docking and separation of chromosomes. Elevated NUF2 expression in LUAD has been linked to a negative patient prognosis. Research indicates that NUF2 indirectly contributes to tumor genetic heterogeneity and malignancy by influencing chromosome stability and mitotic precision. Consequently, NUF2 serves not only as a biomarker for the progression of LUAD but also as a promising therapeutic target.^[16] CDC20 is an activator of the actin complex and plays a critical role in regulating the cell cycle, particularly in the termination of mitosis. Elevated levels of CDC20 are linked to increased tumor grade and a worse prognosis in LUAD. By driving the rapid advancement of the tumor cell cycle, CDC20 enhances the proliferation and division of tumor cells, while also decreasing their responsiveness to chemotherapy. As a result, CDC20 presents itself as a potential target for therapy in managing LUAD.^[17] The findings of this research align with those of prior investigations.

GSEA enrichment analysis found that Retinoblastoma gene in cancer, cell cylce checkpoints, meiotic recombination, DNA replication, and meiosis signaling pathways in LUAD. Cell cycle checkpoints are a central component of the DNA monitoring system throughout the entire cell division process. They play a crucial role in preventing the buildup and propagation of genetic mutations.^[18] Tumors are characterized by disruptions in cell cycle regulatory mechanisms,^[19] research indicates that the majority of cell cycle control functions play a crucial role in the survival of cancer cells.^[20] Research has indicated that cell cycle checkpoint-related genes may have prognostic significance in different types of cancer, making the targeting of cell cycle checkpoints a promising approach.^[21,22] DNA replication starts at multiple origins that become active in each cell cycle. Replication of DNA is necessary for the activation of pluripotency genes.^[23] The enriched pathways involved in GO and KEGG in this study have been found in other tumors. GO is mainly enriched in histone deacetylase binding, hormone activity, GABA-A receptor activity, dendrite membrane, GABA receptor complex, neuron projection membrane, among others. The KEGG pathway is mainly enriched in neuroactive ligand-receptor interaction, Alcoholism, systemic lupus erythematosus. Histone deacetylase (HDAC), also known as histone deacetylase, initiates the removal of acetyl groups from lysine residues located in the N-terminal tails of nucleosome core histones. This enzyme plays a role in repressing gene expression by facilitating chromatin condensation.^[24,25] HDAC, plays a crucial role in controlling gene transcription and is implicated in regulating cell cycle, differentiation, development, and cancer in humans.^[26,27] The neuroactive ligand-receptor interaction pathway is a crucial signaling pathway in colon adenocarcinoma, playing a significant role in immune suppression. Analysis of the immune-related functions of this pathway indicates that its activation is linked to decreased infiltration of CD8+ T cells, suggesting that excessive activation of the pathway could hinder the infiltration of these cells. CD8+ T cells typically infiltrate the tumor microenvironment (TME) in colon cancer, contributing to immune evasion. Patients with heightened activation of this pathway exhibit a downregulation of genes associated with immune checkpoint blockade.^[28]

Tumor-infiltrating immune cells are key factors in maintaining the balance of the TME, greatly affecting tumor progression and clinical outcomes. In the TME, infiltrating immune cells account for a large proportion, which drives the biological behavior of tumors by releasing inflammatory cytokines and chemokines and interacting with tumor cells. Research indicates that tumor-associated macrophages play a role in tumor advancement, angiogenesis, survival, and immune suppression against tumors.^[29] Macrophages have great flexibility and can react variably to various environmental cues. For instance, the activation of macrophages by IFN and Toll-like receptor ligands can enable them to eliminate pathogens and, in certain situations, cancer cells.^[30] Multiple studies have established that M1 macrophage and M2 macrophage have the characteristics of resisting tumor and promoting tumor growth respectively. M1 macrophages, known as classically activated macrophages, have the ability to function as antigen-presenting cells, presenting tumor-associated antigens to effector T cells. Furthermore, they can secrete immunostimulatory factors, support the proliferation of NK cells and T cells, and exhibit potent antitumor capabilities. On the other hand, M2 macrophages, once activated, can promote tumor growth and progression. It is noteworthy that M1 macrophages have the potential to transition into M2 macrophages when exposed to stimuli such as IL-10, TGF-β, among others.^[31,32] Mast cells, as a type of innate immune cell, play a significant role in inducing the growth and metastasis of different tumors. Furthermore, they have the ability to promote T lymphocyte proliferation by secreting TNF-α. This reciprocal interaction between mast cells and T lymphocytes, mediated by TNF-α, creates a positive feedback mechanism that contributes to antitumor effects.^[33] Furthermore, mast cells have the ability to release proteases via degranulation, which helps break down the tumor matrix and dismantle the extracellular matrix and cell structure of the tumor, ultimately impeding tumor growth. In this study, TTK has different correlations with different immune cells, TTK expression is correlated with wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, TGF-b dominant cell subtypes. The level of TTK expression shows a positive correlation with the expression of important genes that play a role in regulating various immune cell subsets, such as Th2 cells, Tgd cells, T helper cells, and others. On the contrary, TTK is negatively correlated with the infiltration level of immune cells such as mast cells, Eosinophils, iDC, Th17 cells, and macrophages. This indicates that TTK could be involved in controlling the infiltration of immune cells and might be a promising target for therapy in the tumor microenvironment of patients with LUAD.

Although this study provides new ideas for LUAD therapeutic targets, it also has certain limitations: First, in terms of sample size, we did not include many GEOs and need to expand the sample size for verification. Second, our study lacks animal model validation, and the specific mechanism of TTK in LUAD needs further verification.

5. Conclusion

In summary, analysis of data from multiple databases and online platforms shows that TTK expression is abnormal in LUAD. TTK expression is correlated with the prognosis of LUAD patients. The higher the TTK expression level, the worse the survival prognosis of patients. TTK has different correlations with different immune cells and can be used as a biomarker for the efficacy of LUAD immunotherapy, providing effective guidance for immunotherapy in LUAD patients. In the future, the specific mechanism of TTK’s high expression in LUAD will be studied in animal models.

Author contributions

Conceptualization: Xiaoling Zhu, Lingling Ye.

Data curation: Shengdan Li.

Funding acquisition: Lingling Ye.

Methodology: Lingling Ye.

Writing – original draft: Xiaoling Zhu, Lingling Ye.

Writing – review & editing: Xiaoling Zhu, Lingling Ye.