GNA14 may be a potential prognostic biomarker in nasopharyngeal carcinoma

doi:10.21203/rs.3.rs-4912812/v1

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GNA14 may be a potential prognostic biomarker in nasopharyngeal carcinoma

https://doi.org/10.21203/rs.3.rs-4912812/v1

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Background:

Nasopharyngeal carcinoma (NPC) is a highly invasive malignant tumor. Recurrence and distant metastasis represent the primary causes of treatment failure. This study aimed to identify biomarkers highly associated with NPC and investigate its roles in tumor progression.

Methods:

Transcriptome sequencing (RNA-seq) data of NPC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) database. By analyzing the RNA-seq data, we found that G Protein Subunit Alpha 14 (GNA14) is closely associated with the diagnosis and prognosis of NPC. Immunohistochemistry (IHC) was used to detect the expression of GNA14 in tumor tissues of 165 NPC patients, and we analyzed the relationship between GNA14 expression and patient prognosis. The potential mechanisms by which GNA14 affects tumor prognosis were preliminarily analyzed using bioinformatics analysis.

Results:

Analysis of RNA-seq data and IHC showed that GNA14 expression was downregulated in NPC (p < 0.001, p < 0.01, respectively), and low expression of GNA14 was closely associated with poor prognosis. IHC analysis showed that patients with low GNA14 expression had significantly shorter progression-free survival (PFS) and distant metastasis-free survival (DMFS) than those with high GNA14 expression (p = 0.023, p = 0.008, respectively). Multivariate analysis indicated that the GNA14 expression was an independent risk factor for DMFS (p= 0.030). The nomogram included GNA14 expression, EBV DNA, and N stage as prognostic factors and the concordance index (C-index) of the DMFS nomogram was 0.73. Bioinformatics analysis indicated that NPC patients with low GNA14 expression might represent lower levels of immune cell infiltration and poorer drug sensitivity.

Conclusion:

Low GNA14 expression may be a risk factor for poor prognosis in NPC.

Nasopharyngeal carcinoma

Biomarker

GNA14

EBV

Prognosis

Immunohistochemistry

Bioinformatics analysis

Nasopharyngeal carcinoma (NPC), a type of head and neck squamous cell carcinoma (HNSCC), originates from nasopharyngeal epithelial tissues(1). NPC is characterized by an uneven geographical distribution, particularly prevalent in East and Southeast Asia(2) The etiology of the disease is related to racial susceptibility, genetic factors, environmental factors, and Epstein-Barr virus (EBV) infection(1). In terms of treatment, NPC is addressed through a combined regimen of radiotherapy and chemotherapy. This regimen has achieved significant success, with a marked increase in the survival rate for patients(3). However, nasopharyngeal recurrence and distant metastases remain the leading causes of death for NPC patients(4). Therefore, identifying the biomarkers that influence the prognosis of NPC is particularly important. Identifying patients at high risk of recurrence and metastasis before treatment can help oncologists develop individualized treatment plans, which are essential for improving outcomes and prolonging the survival of NPC patients.

Identifying biomarkers closely associated with NPC and exploring their value in the diagnosis and treatment of cancer is currently a major research focus(5, 6). RNA-seq technology has been extensively applied in NPC research to elucidate the association between biomarkers and disease mechanisms, tumor biology, and prognosis(7). Against this backdrop, machine learning algorithms and bioinformatics technologies have played a pivotal role. Advances in bioinformatics enable comprehensive analysis of RNA-seq data(8), and the integration of machine learning algorithms has facilitated the identification of biomarkers highly associated with tumors(9). Identifying key biomarkers in tumor is crucial for developing personalized treatment strategies and understanding the molecular mechanisms of NPC. For instance, in the treatment of NPC, outcomes are often influenced by the unique immune evasion mechanisms of the tumor. Recent studies have identified that certain key biomarkers influence the number and proportion of immune cells in the tumor microenvironment (TME), thereby affecting the therapeutic outcomes for patients(10, 11). By analyzing the characteristics of immune infiltration associated with these biomarkers, new avenues for targeted therapies can be identified(12).

The prognosis of NPC primarily depends on the extent of tumor infiltration. However, in addition to the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging, various other prognostic factors, including age, gender, smoking history, EBV DNA load, and gene expression levels(13–15), have been observed. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) provide a wealth of RNA-seq data and corresponding clinical information(16, 17), which play a crucial role in the discovery of new functional genes and understanding of the pathogenesis of tumors. In this study, NPC-related RNA-seq data were analyzed using bioinformatics methods, identifying that GNA14 was strongly associated with the prognosis of NPC. Immunohistochemistry was used to verify the prognostic value of GNA14 in clinical samples. The intrinsic association between GNA14 and NPC was investigated by functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and other methods.

Raw data acquisition and preprocessing

RNA-seq datasets related to NPC (GSE12452, GSE53819, GSE64634, GSE61218, GSE102349) were sourced from the GEO database. For biomarker screening, datasets GSE12452 and GSE53819 were combined to create a new training set, while datasets GSE64634 and GSE61218 were utilized to establish the validation set. The dataset GSE102349, which contains NPC clinical information, was utilized for survival analysis and subsequent bioinformatics analysis. RNA-seq data for 33 solid tumors and corresponding normal tissues from the TCGA database. The raw data were subjected to probe annotation, normalization, and batch effect removal using the “sva” package(18). The characteristics of the GEO datasets are summarized in Supplementary Table 1.

Differential expression analysis and machine learning algorithms

Differential expression analysis was performed on the training set using the “limma” package, with |log2FC| > 1 and false discovery rate (FDR) < 0.05 as screening criteria for identifying differentially expressed genes (DEGs)(19). The DEGs were further screened utilizing Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. RF was performed using the “randomForest” package, genes with a score > 1 were considered as key genes based on the “importance” function. The LASSO regression model was constructed utilizing the “glmnet” package(20) for variable selection, and a cross-validation method was used to determine the optimal lambda value corresponding to the number of key genes. SVM-RFE was performed using the “e1071” package to conduct 10-fold cross-validation and feature elimination and identified a set of key genes based on the principle of error minimization.

Selection of the key biomarker

The overlapped genes identified by the three algorithms were considered highly related to NPC. The diagnostic value of these genes was assessed by receiver operating characteristic curve (ROC) analysis and validated using the validation set. 88 patients from GSE102349 with complete progression-free survival (PFS) information were used for survival analysis. The RNA-seq data and PFS information from the TCGA were used to conduct a pan-cancer survival analysis. Grouping for survival analysis was based on median expression levels of the overlapped genes (GNA14 and LRRC34). Through the above process, we successfully identified a key biomarker, GNA14, highly related to the diagnosis and prognosis of NPC.

Enrolled patients and follow-up

Tissue specimens for immunohistochemical (IHC) examination of GNA14 were obtained from 165 diagnosed NPC patients treated at Zhongshan City People's Hospital (Guangdong, China) from January 2015 to December 2017, along with 30 patients diagnosed with chronic rhinosinusitis during the same period. This study was conducted in accordance with the Declaration of Helsinki. The study received approval from the Clinical Research Ethics Committee of the Zhongshan City People's Hospital. All NPC tissues were collected before anti-cancer treatment. All patients following these criteria were retrospectively enrolled: (a) histopathologically confirmed NPC; (b) clinical stages I-IVa according to the 8th edition AJCC/UICC staging system; (c) received either solely intensity-modulated radiation therapy, concurrent chemoradiotherapy, with or without induction chemotherapy or adjuvant chemotherapy; (d) had complete baseline data; (e) had no severe heart, lung, liver, kidney diseases, or other cancers at NPC diagnosis. NPC patients who completed treatment were followed monthly for the first 3 months, every 3 months for the next 3 years, every 6 months for the next 2 years, and annually thereafter. Follow-up ended in December 2023.

IHC and scoring strategies

All Tissues were fixed in 4% formaldehyde and embedded in paraffin. To assess the expression level of GAN14, IHC examinations were conducted using the GNA14 antibody (Polyclonal, rabbit, 13350-1-AP-50UL, 1:200 dilution, Wuhan Sanying). Tissues were cut into 4 µm sections, deparaffinized with xylene, and subsequently rehydrated with graded ethanol. The slides were then incubated with a 3% H2O2 solution for 10 minutes to quench endogenous peroxidase activity, and 0.01 mmol/L citrate buffer (pH 6.0) was used for antigen retrieval in a high-pressure cooker. The sections were incubated with the primary antibody for 3 hours at room temperature, followed by rinsing with TBS. Following incubation with the secondary antibody (Anti-Mouse/Rabbit Universal Immunohistochemical Test Kit, PK10006, Wuhan Sanying), the sections were stained with DAB. All slides were then re-stained with hematoxylin, examined under the microscope, and photographed. Positive control sections were provided by the antibody manufacturer. Cell staining intensity was scored based on a previous study(21, 22). The IHC results were assessed by calculating the total score (0–12) by multiplying the intensity of positive staining (negative, 0; weak, 1; moderate, 2; or strong, 3) by the proportion of target immunopositive cells (< 25%, 1; 25–49%, 2; 50–75%, 3; or > 75%, 4). IHC results were evaluated independently by two pathologists, and any discrepancies were resolved through consensus. Based on the median IHC score, the high GNA14 expression group was defined as samples with an IHC score > 4, and the low GNA14 expression group was defined as samples with an IHC score ≤ 4.

Expression profile of GNA14

RNA-seq data were extracted from the training and validation sets to compare the expression levels of GNA14 in NPC and normal tissue. IHC staining was employed to detect and compare the expression of GNA14 in nasopharyngeal tissues from patients with chronic sinusitis and NPC. Furthermore, we assessed the differential expression of GNA14 in various solid tumors by analyzing RNA-Seq data from TCGA database, covering 33 different types of tumors and their corresponding normal tissues.

Functional Enrichment Analysis

NPC patients in GSE102349 were categorized into high and low GNA14 expression groups. Identification of DEGs between two groups using the “limma” package (|log2FC| >1, FDR < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs were performed using the “clusterProfiler” package. We selected “C5.go.Hs.symbols.gmt” from the Molecular Signatures Database (MSigDB) as the reference gene set, and gene set enrichment analysis (GSEA) was performed for genes in the GNA14 high and low expression groups.

Immune infiltration and drug sensitivity analysis

The “estimate” package in R was employed to predict the ImmuneScore, StromalScore, and their combined scores (ESTIMATE score) in tumor samples. Correlations between GNA14 expression and 79 immune checkpoint genes were analyzed(23), and the immune checkpoint genes strongly associated with GNA14 were selected (p < 0.001). The single-sample gene set enrichment analysis (ssGSEA) algorithm was utilized to assess the relationship between the proportions of various immune cell types in NPC and GAN14 expression. The gene annotation file contains 28 tumor-infiltrating immune cells from TISIDB. Furthermore, the “oncoPredict” package was used to estimate the chemotherapeutic response of patients from high and low GNA14 groups. The chemotherapeutic response was determined by the half maximal inhibitory concentration (IC50) of each NPC patient and the IC50 data was sourced from the GDSC website (https://www.cancerrxgene.org/).

Statistical Analysis

Descriptive statistical analysis was performed on the collected data, expressed as mean ± standard deviation (SD) or percentage (%). Continuous variables were compared using the independent samples t-test or Mann-Whitney U test. Correlations between variables were assessed using Pearson or Spearman correlation analysis. The division of high and low GNA14 expression groups was based on the median GNA14 expression level. Survival analysis was performed using the Kaplan-Meier method, and log-rank tests were used to compare differences. Correlations between GNA14 expression and clinicopathological features were analyzed using the chi-square test or Fisher exact test. The cutoff value for the high and low EBV DNA groups was chosen as 4000 copies per milliliter based on a previous study of the prognostic value of EBV DNA(24). Univariate and multivariate Cox regression models were used to identify the risk factors influencing the prognosis of NPC. A nomogram was constructed to predict the 3-year and 5-year DMFS of NPC patients based on the results of multivariate analysis, and the predictive ability of the models was assessed by calculating the consistency index (C-index). All tests were two-tailed, with significance levels set at p < 0.05. All statistical analyses were performed using R software (version 4.0.0) or SPSS software (version 20.0, IBM, New York, USA).

Differential expression analysis and machine learning algorithms based on GEO datasets.

A total of 795 DEGs were identified between NPC and normal tissues based on GEO public datasets (Supplementary Table 2). Among these genes, 515 were up-regulated and 280 were down-regulated in NPC samples (Fig. 1A). Using the RF algorithm, LASSO regression algorithm, and SVM-RFE algorithm, 4, 21, and 16 genes were identified as being highly associated with NPC, respectively. (Fig. 1B-F) (Supplementary Table 3).

GNA14 may be a key biomarker in NPC

The Venn diagram showed two overlapped genes (GNA14, LRRC34) identified by three machine learning algorithms (Fig. 2A, Supplementary Table 2). ROC curve analysis demonstrated that GNA14 had an AUC of 0.982 (95% CI: 0.96–0.99) in the training set (Fig. 2B), and achieved an AUC of 0.941 (95% CI: 0.85-1.00) in the validation set (Fig. 2C). The results of the survival analysis indicated that the group with low GNA14 expression exhibited significantly shorter PFS compared to the high-expression group (Fig. 2D). Conversely, there were no significant differences in PFS between the high-expression and low-expression groups of LRRC34 (Fig. 2E). In addition, we found that in head and neck squamous cell carcinoma (HNSCC), thyroid carcinoma (THCA), cholangiocarcinoma (CHOL), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and uterine corpus endometrial carcinoma (UCEC), the risk of tumor progression was higher in the GNA14 low-expression group than in the GNA14 high-expression group (Fig. 2F). Therefore, GNA14 was selected as a key biomarker for further study.

Expression profile of GNA14

Analysis of the RNA-seq data demonstrated a significant reduction in GNA14 expression in NPC compared to normal nasopharyngeal tissues in training set (p < 0.001) (Fig. 3A), a result corroborated by the validation set (Fig. 3B). Through IHC examination, we observed that the expression level of GNA14 in NPC samples was significantly lower than in non-cancerous nasopharyngeal tissues (p < 0.01), and GNA14 was primarily localized in the cell membrane (Fig. 3C, D, E). Furthermore, it was discovered that in various tumor samples, such as head and neck squamous cell carcinoma (HNSCC), bladder urothelial carcinoma (BLCA), liver hepatocellular carcinoma (LHIC), lung squamous cell carcinoma (LUSC), and thyroid carcinoma (THCA), the expression level of GNA14 was significantly lower than that of the corresponding normal samples (p < 0.001) (Fig. 3F).

Patient characteristics and clinical sample analysis

Of the 165 patients enrolled, 110 (66.7%) were male and 55 (33.3%) were female, with a male-to-female ratio of 2:1, and a median age of 49 years (range: 22–76 years). 39 (23.6%) patients were diagnosed as stage I-II and 126 (76.4%) as stage III-IV. The majority of patients had a pathological diagnosis of WHO type III (97.5%). The median duration of follow-up was 69 months (range: 4–95). There were 92 cases in the GNA14 low expression group and 73 cases in the GNA14 high expression group. We found that the low GNA14 expression was significantly associated with advanced clinical staging (p = 0.013) and increased risk of distant metastasis (p = 0.008) (Table 1). The PFS (56.5% vs. 74.0%; p = 0.023) and DMFS (70.0% vs. 87.7%; p = 0.008) of patients in the GNA14-low expression group were significantly shorter than that of the GNA14-high expression group (Fig. 4A, B), while there was no significant difference in overall survival (OS) and locoregional recurrence-free survival (LRFS) between the two groups (p = 0.088, p = 0.478, respectively) (Fig. 4C, D). The results of the multivariate Cox regression analysis indicated that T stage and EBV DNA were significant factors for OS (p = 0.024 and p = 0.029, respectively). EBV DNA was identified as a significant factor for PFS (p = 0.012), while T stage was a significant factor for LRFS (p = 0.031). GNA14 expression was an independent risk factor affecting DMFS in NPC (p = 0.030) (Table 2). We constructed a nomogram that incorporates the GNA14 expression (Fig. 4E, F), EBV DNA, and N stage to predict the 3-year and 5-year DMFS in NPC patients (Fig. 4G). The C-index of the nomogram was 0.73 (Fig. 4H).

Table 1

Baseline information of enrolled patients
Characteristics	GNA14 expression		P value	overall
Characteristics	Low	High	P value	overall
n	92	73		165
Sex, n (%)			0.912
female	31 (33.7%)	24 (32.9%)		55 (33.3%)
male	61 (66.3%)	49 (67.1%)		110 (66.7%)
Smoking, n (%)			0.059
No	75 (81.5%)	67 (91.8%)		142 (86.1%)
Yes	17 (18.5%)	6 (8.2%)		23 (13.9%)
Age, n (%)			0.356
< 60	73 (79.3%)	62 (84.9%)		135 (81.8%)
≥ 60	19 (20.7%)	11 (15.1%)		30 (18.2%)
T.Stage, n (%)			0.304
T1-T2	43 (46.7%)	40 (54.8%)		83 (50.3%)
T3-T4	49 (53.3%)	33 (45.2%)		82 (50.7%)
N.Stage, n (%)			0.067
N0-N1	36 (39.1%)	39 (53.4%)		75 (45.5%)
N2-N3	56 (60.9%)	34 (46.6%)		90 (54.5%)
TNM.Stage, n (%)			0.013
I-II	15 (16.3%)	24 (32.9%)		39 (23.6%)
III-IV	77 (83.7%)	49 (67.1%)		126 (76.4%)
EBV DNA (copies/mL), n (%)			0.201
≥ 4000	47 (51.1%)	30 (41.1%)		77 (46.7%)
< 4000	45 (48.9%)	43 (58.9%)		88 (53.3%)
Death, n (%)			0.086
No	68 (73.9%)	62 (84.9%)		130 (78.8%)
Yes	24 (26.1%)	11 (15.1%)		35 (21.2%)
Recurrence, n (%)			0.605
No	74 (80.4%)	61 (83.6%)		135 (81.8%)
Yes	18 (19.6%)	12 (16.4%)		30 (18.2%)
Distant metastasis, n (%)			0.006
No	64 (69.6%)	64 (87.7%)		128 (77.6%)
Yes	28 (30.4%)	9 (12.3%)		37 (22.4%)
WHO type, n (%)			0.457
III	91 (98.9%)	70 (95.9%)		161 (97.6%)
I	1 (1.1%)	3 (4.1%)		4 (2.4%)

Table 2

Univariate and multivariate analysis of prognostic risk factors in NPC patients
Characteristics	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
OS	Univariate analysis		Multivariate analysis
GNA14 expression (high vs. low)	0.542 (0.266–1.107)	0.093	0.619 (0.299–1.279)	0.195
Sex (Female vs. male)	1.222 (0.586–2.544)	0.593
Age (< 60 vs. ≥60)	2.207 (1.081–4.509)	0.030	1.856 (0.899–3.832)	0.095
Smoking (No vs. Yes)	1.357 (0.562–3.275)	0.497
T.Stage (T1-T2 vs. T3-T4)	3.324 (1.557–7.096)	0.002	3.045 (1.160–7.996)	0.024
N.Stage (N0-N1vs. N2-N3)	1.746 (0.868–3.511)	0.118
TNM.Stage (I-II vs. III-IV)	2.669 (0.942–7.562)	0.065	0.794 (0.202–3.116)	0.741
EBV DNA (< 4000 vs.≥4000) (copies/mL)	2.729 (1.336–5.573)	0.006	2.283 (1.086–4.799)	0.029
PFS	Univariate analysis		Multivariate analysis
GNA14 expression (high vs. low)	1.866 (1.080–3.224)	0.025	1.705 (0.973–2.987)	0.062
Sex (Female vs. male)	2.207 (1.170–4.162)	0.014	1.850 (0.957–3.577)	0.067
Age (< 60 vs. ≥60)	1.554 (0.864–2.793)	0.141
Smoking (No vs. Yes)	0.976 (0.463–2.058)	0.950
T.Stage (T1-T2 vs. T3-T4)	1.479 (0.884–2.474)	0.136
N.Stage (N0-N1vs. N2-N3)	1.957 (1.133–3.381)	0.016	1.226 (0.621–2.421)	0.557
TNM.Stage (I-II vs. III-IV)	2.330 (1.105–4.912)	0.026	1.202 (0.474–3.050)	0.698
EBV DNA (< 4000 vs. ≥4000) (copies/mL)	2.530 (1.483–4.314)	< 0.001	2.041 (1.173–3.551)	0.012
DMFS	Univariate analysis		Multivariate analysis
GNA14 expression (high vs. low)	2.677 (1.263–5.675)	0.010	2.330 (1.084–5.010)	0.030
Sex (Female vs. male)	2.275 (0.999–5.179)	0.050	1.597 (0.684–3.728)	0.279
Age (< 60 vs. ≥60)	1.475 (0.696–3.126)	0.311
Smoking (No vs. Yes)	0.523 (0.161–1.703)	0.282
T.Stage (T1-T2 vs. T3-T4)	0.918 (0.481–1.752)	0.795
N.Stage(N0-N1vs. N2-N3)	4.033 (1.770–9.188)	< 0.001	3.590 (1.084–11.894)	0.037
TNM.Stage (I-II vs. III-IV)	2.880 (1.020–8.132)	0.046	0.513 (0.112–2.343)	0.389
EBV DNA (< 4000 vs. ≥4000) (copies/mL)	3.645 (1.763–7.537)	< 0.001	2.772 (1.301–5.905)	0.008
LRFS	Univariate analysis		Multivariate analysis
GNA14 expression (high vs. low)	1.300 (0.626–2.701)	0.481
Sex (Female vs. male)	2.704 (1.035–7.067)	0.042	2.561 (0.978–6.702)	0.055
Age (< 60 vs. ≥60)	1.734 (0.771–3.898)	0.183
Smoking (No vs. Yes)	2.042 (0.875–4.763)	0.099
T.Stage (T1-T2 vs. T3-T4)	2.420 (1.132–5.174)	0.023	2.309 (1.079–4.943)	0.031
N.Stage (N0-N1vs. N2-N3)	0.996 (0.486–2.041)	0.990
TNM.Stage (I-II vs. III-IV)	1.771 (0.678–4.627)	0.244
EBV DNA (< 4000 vs. ≥4000) (copies/mL)	1.733 (0.841–3.570)	0.136

Functional enrichment Analysis

We identified 280 DEGs between high and low GNA14 expression groups in GSE102349, with 266 genes exhibiting upregulation and 14 showing downregulation (Supplementary Table 2). GO analysis indicated that these DEGs were predominantly enriched in pathways related to immune response and cell migration, including B-cell activation, proliferation of various immune cells, and ciliary movement (Fig. 5A). KEGG enrichment analysis revealed that the DEGs were significantly concentrated in pathways such as chemokine signaling, NF-kB signaling, and cytochrome P450-mediated drug metabolism (Fig. 5B). According to Gene Set Enrichment Analysis (GSEA), pathways related to cell division, DNA, and chromosome replication were up-regulated in the low GNA14 expression group, and pathways related to immune cell activity and adaptive immune response were down-regulated compared to patients in the high GNA14 expression group (Fig. 5C, D).

Immune cell infiltration and drug sensitivity analysis

It was found tumor tissues with low GNA14 expression represented lower immune and stromal scores (Fig. 6A). Furthermore, in NPC patients with low GNA14 expression, the majority of immune checkpoint genes exhibit lower expression levels (p < 0.001) (Fig. 6B). Results from the immune infiltration analysis showed that a decrease in GNA14 expression level was associated with a reduced proportion of most immune cells such as B cells, CD8 T cells, and NK cells (Fig. 6C). Through drug sensitivity analysis, we observed that most chemotherapy drugs such as 5-fluorouracil, Gemcitabine, and Oxaliplatin exhibited higher IC50 values in patients with GNA14-low expression (Fig. 6D).

Exploring the key biomarkers that affect the prognosis of NPC is crucial for identifying new therapeutic targets and prolonging the survival of patients. In this study, through a comprehensive analysis of RNA-seq data, we ultimately identified GNA14 as a potential key biomarker for NPC. GNA14 is a gene encoding the G protein subunit alpha 14. G proteins (guanine nucleotide-binding proteins) are widespread intracellular signal transduction molecules. Some studies have shown that GNA14 can act as an oncogene to promote cancer development. For example, a study by Wang et al. found that GNA14 is highly expressed in endometrial cancer tissue and that GNA14 promotes the malignant growth of endometrial cancer by stimulating KLF7 to upregulate HAS2 expression(25). Interestingly, more research indicates that GNA14 may function as a tumor suppressor gene For example, Song et al. found that GNA14 was downregulated in hepatocellular carcinoma (HCC), and negatively associated with hepatitis B virus (HBV) infection, vascular invasion, and HCC prognosis(26). Huang et al. reported lower GNA14 expression in oral squamous cell carcinoma (OSCC) cell lines and tissues, indicating a poorer prognosis for OSCC(27). In a study conducted by Pan et al., GNA14 exhibited low expression in papillary thyroid carcinoma and patients in the GNA14 low expression group demonstrated a diminished disease-free survival (DFS) rate(28). The function of GNA14 in tumor development may depend on the specific tumor type, with the protein exhibiting either tumor-promoting or tumor-suppressing properties. It is noteworthy that thyroid cancer, oral squamous cell carcinoma, and nasopharyngeal carcinoma are all clinically common head and neck tumors. Similarly, our study also found that GNA14 expression was down-regulated in NPC, and patients with low GNA14 expression had significantly lower PFS and DMFS than those with high GNA14 expression.

In this study, we preliminarily investigated the possible reasons for the poor prognosis of NPC patients due to low GNA14 expression. It was observed that the expression level of GNA14 was lower in patients with advanced clinical stages (III or IVa) compared to those with early stages (I or II). It was hypothesized that GNA14 may function as an oncogene, with low expression promoting the development and progression of NPC. Furthermore, we focused on DEGs in patients with high and low GNA14 expression. The analysis of GO and KEGG indicates that these DEGs are primarily enriched in pathways associated with immune response and cell migration(29, 30). In addition, GSEA suggested that compared to patients with high GNA14 expression, pathways such as cell division, DNA, and chromosome replication were up-regulated in patients with low GNA14 expression, while pathways such as immune cell activity and adaptive immune response were down-regulated(31). Research has demonstrated that the extent of immune infiltration is linked to prognosis, with greater levels of immune infiltration generally indicating a more favorable prognosis(32). Our study discovered that NPC patients with low GNA14 expression had a significant decrease in the proportion of most immune cells, such as B cells, CD4+, and CD8 + T cells, and had lower stromal scores and immunity scores, indicating these patients had higher tumor purity and lower levels of immune infiltration(33). In addition, we found that patients with low GNA14 expression were less sensitive to chemotherapeutic agents such as 5-fluorouracil and gemcitabine, which are commonly used chemotherapeutic agents in nasopharyngeal carcinoma, suggesting that these patients may have poorer response to chemotherapy.

Numerous studies have demonstrated a close association between EBV infection and the development of NPC. EBV DNA can be used as a biomarker for early diagnosis and prognostic prediction of NPC(34, 35). In our study, multivariate Cox regression analysis showed that Pre-treatment EBV DNA was an independent risk factor for PFS and DMFS in patients, which is consistent with the findings of Tang et al(36). In addition to EBV DNA, many biomarkers have been reported to be associated with the prognosis of NPC, such as EBV serum antibodies, miR-BART2-5p, serum LDH, and C-reactive protein (CRP)(37–40). These factors have limitations in clinical application due to their susceptibility to patient conditions and the complexity of the inspection techniques. We found that GNA14 expression and N stage were equally independent risk factors for DMFS. We have developed a nomogram based on GNA14 expression that can more easily help clinicians evaluate patients with a high risk of distant metastasis. For instance, a more intense regimen can be used in individuals with low GNA14 expression.

Although our study explores for the first time the important value of GNA14 in the prognosis of NPC, we acknowledge that the limitations of this study include the limited source of data and the lack of confirmatory experiments. We look forward to future clinical research and basic experiments that can validate our findings and explore the role of GNA14 in the development and individualized treatment of NPC.

In conclusion, we found that GNA14 expression was down-regulated in NPC tissues, and its low expression may be closely associated with advanced tumor stage and distant metastasis. GNA14 expression combined with pre-treatment EBV DNA load and N stage shows potential for predicting NPC patients with a high risk of distant metastasis.

NPC

Nasopharyngeal carcinoma

RNA-seq

Transcriptome sequencing

GNA14

G Protein Subunit Alpha 14

IHC

Immunohistochemical

GEO

Gene Expression Omnibus

TCGA

The Cancer Genome Atlas

EBV

Epstein-Barr virus

C-index

Concordance index

CHOL

Cholangiocarcinoma

HNSCC

Head and Neck squamous cell carcinoma

KIRC

Kidney renal clear cell carcinoma

LIHC

Liver hepatocellular carcinom

THCA

Thyroid carcinoma

UCEC

Uterine Corpus Endometrial Carcinoma

HCC

Hepatocellular carcinoma

OSCC

Oral squamous cell carcinoma

DEGs

Differentially expressed genes

FDR

False discovery rate

Random Forest

LASSO

Least Absolute Shrinkage and Selection Operator

SVM-RFE

Support Vector Machine-Recursive Feature Elimination

ROC

Receiver operating characteristic curve

HGB

Hemoglobin

ALB

Albumin

LDH

Lactate dehydrogenase

WHO

World Health Organization

Male

Female

PFS

Progression-free survival

Overall survival

DMFS

Distant metastasis-free survival

LRFS

Locoregional recurrence-free survival

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GSEA

Gene set enrichment analysis

ssGSEA

Single-sample gene set enrichment analysis

IC50

Half maximal inhibitory concentration

Ethics approval and consent to participate

The retrospective study was approved by the Clinical Research Ethics Committee of the Zhongshan City People’s Hospital (Guangdong, China) (Project No: 2024-065).

Consent for publication

All authors have consented to the publication of this manuscript.

Availability of data and materials

The raw data supporting the results of this study can be obtained in the TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), GDSC (https://www.cancerrxgene.org/), MSigDB (https://www.gsea-msigdb.org/), and TISIDB (http://cis.hku.hk/TISIDB/). If there is a reasonable request, data can be requested from the corresponding author.

Conflict of Interest

The authors declare no conflict of interest in this study.

Author contributions statement

HJR: Conceptualization, methodology, writing - original Draft, and data curation. LXL and SSJ: IHC examination and supervision. YX, DL, XL, LSF, DY, and JMF： Validation, writing - review, and supervision. All authors read and approved the final manuscript.

Acknowledgments

We thank all participants, collaborators, and team members of this study.

Author contributions statement

Funding

1. 2023 Zhongshan Social Welfare and Basic Research Project (2023B3008).

2. 2020 National Major Chronic Noncommunicable Disease Prevention and Control Research, “Research on the Development of New Technologies and Optimization of Nasopharyngeal Cancer Screening in high-incidence Areas” (2020YFC1316900), subproject “Building a Preliminary Screening Model for Individualized disease Risk” (2020YFC1316902).

Competing interests

The authors declare no conflict of interest in this study.

Data availability

Ethics declarations

The retrospective study was approved by the Clinical Research Ethics Committee of the Zhongshan City People's Hospital (Guangdong, China) (Project No: 2024-065).

Consent to participate

All authors have consented to the publication of this manuscript.

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No competing interests reported.

Supplementarydocument.xlsx
Supplement information Supplementary Table 1: The characteristics of the GEO datasets Supplementary Table 2: The results of differential expression analysis in the training set. Supplementary Table 3: Results of RF algorithm, LASSO regression algorithm, and SVM-RFE algorithm. Supplementary Table 4: The DEGs between high and low GNA14 expression groups in GSE102349.

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GNA14 may be a potential prognostic biomarker in nasopharyngeal carcinoma

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