PLEK2 and SCN7A: novel biomarkers of non-small cell lung cancer

Objective Lung cancer is the leading cause of cancer-related death globally, and non-small cell lung cancer (NSCLC) is the most common type of lung cancer. However, the diagnosis and prognosis of NSCLC remain dim. Our team has focused on identifying differentially expressed genes (DEGs) between NSCLC tissues and adjacent tissues, which may be useful as effective diagnostic markers that can better explain the progression of NSCLC. Methods The Gene Expression Omnibus (GEO) database was used to screen the Gene Expression Omnibus series, which records the information of a large number of patients with primary NSCLC (n > 50). Then, the DEGs were validated using Student’s t -test. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DAVID. The prognosis information was analyzed separately using data obtained from three databases, Human Protein Atlas, Kaplan–Meier Plotter, and SurvExpress. Results A series of 180 DEGs (33 upregulated and 147 downregulated genes), mainly involving genes associated with extracellular exosomes, focal adhesion, and cell adhesion, were identified via GO analysis. Subsequently, KEGG analysis demonstrated that focal adhesion, cell adhesion molecules, and PPAR signaling pathway were the most enriched pathways. Then, we paid particular attention to pleckstrin 2 (PLEK2) and sodium voltage-gated channel alpha subunit 7 (SCN7A), as they have not been investigated as cancer-related genes previously. Kaplan–Meier survival analysis illustrated that PLEK2 and SCN7A levels were significantly correlated with the prognosis of NSCLC. Conclusions Our research found that, as potential biomarkers, both PLEK2 and SCN7A are related to the development and prognosis of NSCLC. They may be used in disease screening and prognosis. The clinical significance of these two genes deserves further investigation.


Introduction
Lung cancer, which includes non-small cell lung cancer (NSCLC) and small cell lung cancer, is the most deadly malignancy globally [1,2]. NSCLC accounts for 80-85% of all cases of lung carcinoma, and it includes three major histological types: lung adenocarcinoma, lung squamous cell carcinoma, and large cell carcinoma [3,4]. Lung adenocarcinoma has surpassed lung squamous cell carcinoma as the most prevalent histologic subtype of lung carcinoma [5]. Currently, several treatments are available for NSCLC, such as surgery, chemotherapy, irradiation, and molecular targeted therapy [6].
However, the 5-year mortality rate is extremely poor for NSCLC, ranging from 51 to 99% depending on the TNM stage. Because of the metastatic nature of NSCLC and its untypical symptoms, patients are normally diagnosed with advance disease, and only 5% of patients with stage Ⅳ experience long-term survival [7]. Most patients with stage IV NSCLC are ineligible for surgery, and their survival is consequently poor. Unfortunately, the symptoms of NSCLC are not sufficiently obvious to permit diagnosis at an early stage [8].
Therefore, new effective biomarkers for early diagnosis and prognosis would be of great value for improving the clinical outcomes of patients with NSCLC.
In the progression of NSCLC, some genes are overexpressed, whereas others are inhibited.
For example, with the development of lung cancer, calreticulin (CANX) is significantly upregulated [9]. Therefore, the serum CANX level has been used as a biomarker for the early detection of lung cancer in the clinic. Moreover, biomarkers related to the diagnosis and prognosis of NSCLC could be used as new targets for molecular targeted therapies.
Prior studies demonstrated the diagnostic value of growth factor receptor mutation.
However, the use of bioinformatics in detecting and identifying biomarkers of NSCLC still needs to be actualized.
With the development of bioinformatics, genome-scale analysis has been widely used in the research of cancer, especially in identifying potential biomarkers related to the development and prognosis of cancer [10]. These identified biomarkers could be used to more precisely characterize patients and facilitate the development of personalized targeted therapy. Moreover, bioinformatics could help to reveal the characteristics of tumorigenesis, identify pathogenic genes, and improve our understanding of tumor biology and genetics [11]. To investigate new targets to improve the diagnosis rate of lung cancer and predict patient outcomes, we downloaded five transcriptome microarray datasets from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) between tumor and adjacent samples. We also integrated the results of these datasets to identify the common DEGs and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of these common DEGs to identify their biological roles and pathways. Additionally, we downloaded the prognostic information of the DEGs from three different platforms (Human Protein Atlas, Kaplan-Meier Plotter, and SurvExpress) and analyzed the results of these three platforms. We found that pleckstrin 2 (PLEK2) upregulation was associated with a poor prognosis. However, patients with lower sodium voltage-gated channel alpha subunit 7 (SCN7A) expression had worse overall survival rates. Our team aimed to determine the relationship between these two genes and the diagnosis and prognosis of NSCLC.

NSCLC transcriptome microarray dataset
Five NSCLC datasets (GSE19804, GSE27262, GSE43458, GSE75037, GSE103512), including 308 tumor samples and 207 normal lung samples, were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo). The inclusion criteria for these datasets were as follows: (1) primary NSCLC histology; (2) no history of radiotherapy or chemotherapy; and were used to generate plots. Student's t-test was applied for comparisons between two groups. Survival analysis was performed using the Kaplan-Meier method, and the log-rank test was used to evaluate the statistical significance of the differences. Differences were considered statistically significant at P < 0.05.

Results
Identification of DEGs between lung adenocarcinoma and normal lung  Table 2. PLEK2 and SCN7A dysregulation are indicators for prognosis of patients with NSCLC Patients with increased PLEK2 expression had a lower survival rate than those with reduced PLEK2 expression (P < 0.05, Figure 7a). Similarly, lower SCN7A expression was linked to a poorer survival rate (P < 0.05, Figure 7b). Interestingly, SLIT2, which had lower expression in tumor tissue than in normal tissue, also related with poorer outcomes (Figure 8).

Discussion
Aberrant expression of mRNA is widely considered a distinct characteristic of the development and progression of various cancers, especially lung cancer. To investigate the dysregulation of mRNA expression in NSCLC, our group integrated the results of five NSCLC transcriptome microarray datasets that are widely considered to have high credibility. Additionally, these five datasets were downloaded from different platforms, which allowed us to avoid the biases of the platforms. By integrating these five datasets, we largely ensured the credibility of our results. Our research suggests that PLEK2 and SCN7A may be used as potential biological markers for the early diagnosis of NSCLC.  29]. Besides, we identified PLEK2 and SCN7A as potential biomarkers of NSCLC. PLEK2 was first cloned in 1999, and it is widely expressed in diverse adherent cell lines [30,31].
It is thus tempting to consider that PLEK2 is related to cytoskeletal rearrangement and cell spreading, as well as the development of large lamellipodia [32]. Moreover, lamellipodia are widely believed to participate in angiogenesis by endothelial cells and metastasis by melanoma cells [33][34][35]. Hamaguchi also reported that PLEK2 induced PI3kinase-dependent F-actin reorganization and cell spreading [36]. Consistent with this hypothesis, our study revealed that PLEK2 may result in poor prognosis by enhancing the efficiency of tumor growth and the metastasis of NSCLC cells. SCN7A encodes a voltagedependent sodium channel of the excitable membrane, and it is reported to be downregulated in colorectal carcinoma at the mRNA level [37]. However, the antitumor activity of SCN7A is unknown. Therefore, more elaborative research is needed.
Similarly, Liu reported that low eIF3a expression is a negative factor in the development of colonic carcinoma [38,39]. In addition, KEGG pathway analysis indicated that the PPAR signaling pathway, cell adhesion molecules, and ECM-receptor interaction were the most significantly enriched pathways. Our results indicated some of these genes, which were found to play critical roles in the promotion of EMT and the invasion and migration of cells

Conclusions
In this study, we examined the changes of gene expression in NSCLC and identified a series of DEGs, and we specifically identified potential roles of PLEK2 and SCN7A in the diagnosis and prognosis of NSCLC. They could also be new potential therapeutic targets that could improve the patient survival in NSCLC.