High FHL2 mRNA expression and its prognostic value in lung cancer

Patient features

The RNA expression information and related clinical data were acquired from TCGA database. In total, 1018 patients with lung cancer, including 407 females and 611 males were analyzed. Moreover, 109 patients were <55 years old, and 881 patients were ≥55 years old. The detailed clinical features of the patients derived from TCGA were demonstrated in Table 1. There were significant differences (P < 0.05) in histological type, T, N classification, and vital status. However, no statistical differences were found in other parameters.

High FHL2 mRNA expression in lung cancer

The mRNA expression of FHL2 in lung tumor tissue was significantly higher (P < 2.2e-16) than that in normal lung tissue (Figure 1A). Moreover, different FHL2 expression levels were observed (Figure 1B–1L). Lung adenocarcinoma patients had higher FHL2 expression levels (P < 0.01) than lung squamous cell carcinoma patients (Figure 1B). Despite the P value was bigger than 0.05, the result indicated a gradual increase with higher stages (Figure 1C). FHL2 mRNA expression levels of T, N, M classifications are illustrated in Figure 1D–1F. Despite statistical differences are not significant, we still identified patients who were female, <55 years old, deceased and treated with radiation therapy had higher FHL2 expression levels than patients who were male, ≥55 years old, living, and not treated with radiation therapy respectively (Figure 1G–1J). With regard to residual tumor, 4 groups had similar FHL2 expression, while R2 had the higher FHL2 expression level compared to the other groups (Figure 1K). Patients with primary tumor seemed to have lower FHL2 expression levels than patients with recurrent tumor (Figure 1L, P = 0.74), but further studies need to be conducted due to only 2 patients with recurrent tumor.

Figure 1. Expression of FHL2 in normal population and lung cancer patients and its relationship with clinicopathological parameters. (A) The expression difference of FHL2 between normal population and lung cancer patients. (B, C) Expression of FHL2 in different histological types and stages. (D–F) The relationship between FHL2 expression and T classification, N classification, and M classification. (G–L) The relationship between FHL2 expression and age, gender, vital status, radiotherapy, residual tumor, and recurrent tumors. ^*P < 0.05; ^**P < 0.01.

Capability of FHL2 to diagnose lung cancer

The ROC curves were utilized to analyze the diagnostic value of FHL2. A moderate diagnostic capability in lung cancer was observed with the AUC of 0.857 (Figure 2A). We also evaluated the diagnostic ability of FHL2 in the different stages. The similar results were found (Figure 2B–2E) that AUC values were 0.861 (stage I), 0.835 (stage II), 0.877 (stage III) and 0.866 (stage IV), respectively.

Figure 2. ROC curve to assess the diagnostic ability of FHL2 at different stages. (A) The diagnostic value of FHL2 in lung cancer patients. (B–E) Diagnostic value of FHL2 in patients with stage I, stage II, stage III, and stage IV.

Relationships between clinical features and FHL2 expression

The patients were divided into the low- and high- groups according to the threshold of FHL2 level determined from the ROC curve. As demonstrated in Table 1, high FHL2 mRNA expression was highly correlated with histological type (P = 0.0005), T classification (P = 0.0048), N classification (P = 0.0057) and vital status (P = 0.0251) in the light of Chi-square tests.

FHL2 mRNA expression is associated with overall survival

As demonstrated in Figure 3, the Kaplan–Meier curve with log rank tests showed the relationship between FHL2 mRNA expression and overall survival. High FHL2 mRNA expression was remarkably related with poor overall survival (Figure 3A, P = 0.0005). The further subgroup analysis (Figure 3B–3P) revealed that high FHL2 expression was associated with poor OS of patients with lung adenocarcinoma (P = 0.0110), lung squamous cell carcinoma (P = 0.0160), stage II (P = 0.0059), T2 (P < 0.0001), N2 (P = 0.0033) and M0 (P = 0.0048). Additionally, although P value was bigger than 0.05, we still identified that high FHL2 expression was correlated with poor OS patients with stage I, stage III, stage IV, T1, T3, T4, N0, N1 and M1 NSCLC.

Figure 3. The relationship between FHL2 mRNA expression and overall survival. (A) Overall survival group by FHL2 in all tumors. (B–P) Overall survival group by FHL2 in lung adenocarcinoma, lung squamous cell carcinoma, stage I, stage II, stage III, stage IV, T1, T2, T3, T4, N0, N1, N2, M0, M1.

Univariate Cox analysis identified several crucial variables, and the subsequent multivariate analysis confirmed that stage [hazard ratio (HR): 1.343, 95% confidence interval (CI): 1.148–1.570, P < 0.001], T classification (HR: 1.185, 95% CI: 1.027–1.367, P = 0.020), residual tumor (HR: 1.138, 95% CI: 1.003–1.291, P = 0.045) and FHL2 expression (HR: 1.415, 95% CI: 1.160–1.726, P < 0.001) had independent prognostic value for OS of patients with lung cancer. (Figure 4).

Figure 4. The subsequent multivariate analysis about FHL2 and overall survival in lung cancer. (A) Univariate analysis of overall survival in lung cancer patients. (B) Multivariate analysis of overall survival in lung cancer patients. ^*P < 0.05; ^**P < 0.01; ^***P < 0.001.

FHL2 mRNA expression is correlated with relapse-free survival

The Kaplan–Meier survival curve was utilized to evaluate the relationship between FHL2 expression and RFS (Figure 5). Similar to the outcomes above, high FHL2 expression demonstrated a close association with lung adenocarcinoma, stage II, T2, and M0 lung carcinoma. High FHL2 expression showed remarkable prognostic value (P = 0.0036). More importantly, we plotted ROC curves to assess the value of FHL2 in predicting one-, three-, and even five-year RFS in lung cancer patients. Additionally, univariate Cox analysis was utilized to select the crucial prognostic factors. Furthermore, multivariable analysis was implemented to modify interaction between factors. Eventually, FHL2 expression was validated as an independent prognostic factor of patients with NSCLC (Figure 6).

Figure 5. The relationship between FHL2 mRNA expression and relapse free survival. (A) Relapse free survival group by FHL2 in all tumors. (B–P) Relapse free survival group by FHL2 in lung adenocarcinoma, lung squamous cell carcinoma, stage I, stage II, stage III, stage IV, T1, T2, T3, T4, N0, N1, N2, M0, M1.

Figure 6. The subsequent multivariate analysis about FHL2 and relapse free survival in lung cancer. (A) Univariate analysis of relapse free survival in lung cancer patients. (B) Multivariate analysis of relapse free survival in lung cancer patients. ^*P < 0.05; ^**P < 0.01; ^***P < 0.001.

FHL2 can serve as an independent predictor of survival

To evaluate the value of FHL2 as an independent indicator for predicting patient survival, we used the data downloaded from the TCGA database to draw a nomogram of FHL2 predicting patient survival and progression-free survival, and reflected the prediction results through the ROC curve.

As shown in Figure 7, the results showed that patients with high FHL2 expression had shorter OS. Besides, the patients with higher stages, higher T classification, higher N classification, or higher residual tumor also had shorter overall survival. The time-dependent ROC curve exhibited a moderate diagnostic capability with AUC of 0.56 at 1 year, 0.54 at 3 and 5 years (Figure 7A). The nomogram-predicted probability of 1-year, 3-year, and 5-year OS were close to the corresponding actual OS (Figure 7B–7D). At the same time, the decision curve was used to reflect the FHL2 prediction model, and the results showed that patients with high FHL2 expression could predict shorter OS (Figure 7E–7G).

Figure 7. The ROC curves and nomogram about FHL2 and overall survival in lung cancer. (A) ROC curves evaluating the value of FHL2 for predicting overall survival in lung cancer patients. (B) Nomogram predicted 1-year overall survival versus actual 1-year overall survival. (C) Nomogram predicted 3-year overall survival versus actual 3-year overall survival. (D) Nomogram predicted 5-year overall survival versus actual 5-year overall survival. (E–G) Decision curve analysis reflects the feasibility of FHL2 in predicting 1-year, 3-year, and 5-year overall survival of patients.

As shown in Figure 8, the results showed that patients with high FHL2 expression had shorter RFS. Similarly, the patients with adenocarcinoma, higher stages, higher T classification, or higher N classification also had shorter RFS. The time-dependent ROC curve exhibited a moderate diagnostic capability with AUC of 0.56 at 1 year, 0.53 at 3 years, and 0.51 at 5 years (Figure 8A). The nomogram-predicted probability of 1-year, 3-year, and 5-year RFS were close to the corresponding actual RFS (Figure 8B–8D). At the same time, the decision curve was used to reflect the FHL2 prediction model, and the results showed that patients with high FHL2 expression could predict shorter RFS (Figure 8E–8G).

Figure 8. The ROC curves and nomogram about FHL2 and RFS in lung cancer. (A) ROC curves evaluating the value of FHL2 for predicting RFS in lung cancer patients. (B) Nomogram predicted 1-year RFS versus actual 1-year RFS. (C) Nomogram predicted 3-year RFS versus actual 3-year RFS. (D) Nomogram predicted 5-year RFS versus actual 5-year RFS. (E–G) Decision curve analysis reflects the feasibility of FHL2 in predicting 1-year, 3-year, and 5-year RFS of patients.

Patients with high FHL2 expression are more prone to glycolysis and unfold protein response

To further explore the mechanism by which FHL2 affects lung cancer progression, the GSEA analysis through the database obtained from TCGA was further performed to examine the correlation between high FHL2 expression and Glycolytic and unfolded protein responses. The relevant data scores are shown in Table 2.

The results showed that high FHL2 expression was highly correlated with Glycolysis and unfolded protein responses (Figure 9). This suggests that FHL2 may affect lung cancer progression by regulating Glycolysis and inducing unfolded protein response.

Figure 9. Enrichment analysis of FHL2 expression and glycolytic and unfolded protein responses. (A) High expression of FHL2 is enriched in the Glycolytic pathway. (B) High FHL2 expression is highly correlated with unfolded protein response.

FHL2 knockdown inhibits proliferation, migration, and invasion of NSCLC cells

In order to explore the effect of FHL2 on the biological behavior of NSCLC in vitro, we selected 95-D and NCI-H1299 cell lines with high invasiveness and active proliferation for subsequent experiments. We established A549 and NCI-H1299 cells stably expressing FHL2 targeted shRNA, and detected the transfection efficiency by qRT-PCR (Figure 10A). The results of CCK8 assays showed that the proliferation ability of the cells was markedly decreased in sh-FHL2 group compared to sh-NC group (Figure 10B). This indicates that the proliferation ability of cells was inhibited after silencing FHL2. We further explored the effects of FHL2 on NSCLC cell migration and invasion. As shown in Figure 10C–10F, the number of migrated and invaded cells were decreased in sh-FHL2 group compared to sh-NC group. These experiments proved that FHL2 may play an important role in promoting NSCLC by promoting the migration, invasion, and proliferation ability of NSCLC cells. This finding provides a new idea for the clinical treatment of NSCLC.

Figure 10. Effects of FHL2 on proliferation, migration, and invasion of NSCLC. (A) qRT-PCR detected the efficiency of transfection. (B) CCK8 detected the viability of NSCLC cells. (C, D) Wound healing experiment detected the migration ability of NSCLC cells. (E) Transwell experiment detected the migration ability of NSCLC cells. (F) Transwell experiment detected the invasive ability of NSCLC cell. ^*P < 0.05; ^**P < 0.01; ^***P < 0.001.

Clinical data

The complete RNA-Seq expression information and corresponding clinical characteristics were acquired from TCGA database. The FHL2 mRNA expression was calculated as log₂ (x + 1) and converted into RSEM normalized counts.

Nomogram plotting

The lung cancer patients were grouped according to FHL2 expression. Based on the different FHL2 expression, the T/N/M classification, stage, grade, and prognosis of patients were compared.

GSEA assay

The TCGA database was screened, followed by performing GSEA analysis through the online website to explore the relationship between FHL2 expression and Glycolysis and unfolded protein reflection.

Cell culture

Human lung cancer cell lines 95-D and NCI-H1299 were cultured in 1640 medium containing 10% fetal bovine serum and 1% penicillin-streptomycin solution in a 5% CO₂ humidified incubator at 37°C.

Plasmid and transfection

The sh-FHL2 plasmid and sh-control plasmid were purchased (Miaoling Bio, China), followed by transfection into the cell and generation of the stable clone.

RT-qPCR

All the reagents and kits were purchased from Invitrogen (USA). The TRIzol was used to extract total RNA. The reverse transcription was performed using 1 mL of extracted RNA. Finally, the real time quantitative PCR (RT-qPCR) was carried out.

Wound healing assay

The cells were scratched to form a 1-mm wide gap, and cultured for 2 days [24]. In order to measure the breadth of the injured region, pictures were obtained at 0 and 48 hours.

Transwell invasion assay

The plates with 8 μm pore were used, covering with Matrigel (Corning, USA). In the Transwell’s upper chamber, cells were added. Then, the crystal violet-stained cells invasive to the lower surface were observed.

CCK-8 assay

The cells were seeded with density of 1 × 10⁵/well [25]. After culturing for 24 h, DOX was added. Then, 10 μL CCK-8 reagent was added and the absorbance at 450 nm was measured.

Statistical analysis

All materials were retrospectively evaluated using R (version 3.5.1) [26]. We utilized non-parametric rank sum examinations to evaluate FHL2 mRNA expression levels. Wilcoxon rank sum examinations were utilized to contrast two subgroups, and Kruskal–Wallis tests were utilized for comparing multiple subgroups. We utilized the pROC package to depict Receiver Operating Characteristic (ROC) curves for the analysis of FHL2 diagnostic ability through calculating area under ROC curves (AUC) values and measuring ideal cutoff point [27]. In addition, association between clinical characteristics and FHL2 expression was evaluated by chi-square tests with Fisher’s exact test. Kaplan–Meier curve was utilized to study survival status. Univariate Cox and multivariate Cox analysis were utilized to study the independent prognostic ability of FHL2 in lung cancer was identified through. P < 0.05 was statistically significant.