Downregulation of SNAI1 expression in LUSC.

To elucidate the expression profile of SNAI1 in both normal and neoplastic tissues, we conducted a comprehensive analysis using LUSC data from The Cancer Genome Atlas (TCGA) database. Our investigation revealed distinct expression patterns of SNAI1 across various tumor types when compared to normal control tissues. Notably, we observed a significant downregulation of SNAI1 expression in both LUSC and lung adenocarcinoma (p < 0.05) (Fig. 1A).

Fig. 1

Expression of SNAI1 in tumor tissues and compared to normal control tissues. A. Tumor tissues and compared to normal control tissues. B. Different stages. C. Metastasis status

To further explore the clinical relevance of SNAI1 expression, we examined its relationship with different stages of LUSC progression and metastasis status. Our analysis revealed an intriguing pattern of SNAI1 expression across various clinical stages and metastasis status. We observed a trend of increasing expression levels correlating with advancing clinical stages and metastasis (M1)(p < 0.001) (Fig. 1B, C).

SNAI1 expression significantly correlates with overall survival in LUSC

To elucidate the prognostic significance of SNAI1 gene expression in Lung Squamous Cell Carcinoma (LUSC), we conducted a comprehensive analysis using data from The Cancer Genome Atlas (TCGA) database. Employing X-tile software, we performed an optimal threshold analysis to stratify SNAI1 expression levels.

Initially, we categorized SNAI1 expression into three distinct groups: high, medium, and low, based on optimal cut-off values determined by the X-tile software (Fig. 2A). Kaplan-Meier survival analysis revealed statistically significant differences among the survival curves of these three groups (p < 0.001). The median survival times for the high, medium, and low expression groups were 1.6 years, 3.0 years, and 5.8 years, respectively (Fig. 2B). This trichotomization demonstrated a clear trend: patients with lower SNAI1 expression exhibited progressively better long-term survival outcomes.

Fig. 2

Optimal Threshold Analysis of SNAI1 expression. Optimal threshold analysis software(X-tile Software) was employed to stratify SNAI1 expression into three-tier (A, B) and two-tier (C, D) classifications for patient prognosis analysis. Impact of Metastasis status (E) and SNAI1 Expression (F) on LUSC Prognosis

To further refine our analysis, we utilized the X-tile software to determine an optimal cut-off value for dichotomizing patients into high and low SNAI1 expression groups. This data-driven approach ensures an unbiased stratification of the patient cohort. Subsequent Kaplan-Meier analysis of these two groups revealed that patients with high SNAI1 expression exhibited significantly poorer overall survival compared to those with low SNAI1 expression (p < 0.001) (Fig. 2C-D).

These findings consistently demonstrate a strong association between SNAI1 expression levels and patient outcomes in LUSC. The robust statistical approach, incorporating both three-tier and two-tier classifications, provides compelling evidence for the prognostic value of SNAI1 in LUSC. Patients with low SNAI1 expression consistently achieved better long-term survival, while those with high expression showed significantly reduced survival periods.

Interaction between SNAI1 expression and metastatic status

Metastasis is a critical determinant of cancer prognosis. Our analysis of LUSC patients revealed a significant difference in median survival based on metastatic status: 4.5 years for non-metastatic patients versus 2.0 years for those with metastasis (p = 0.013) (Fig. 2E). Patients with high SNAI1 expression exhibit poorer survival outcomes compared to those with low SNAI1 expression, irrespective of metastatic status. The presence of metastasis (M1) appears to have a substantial negative impact on survival, as indicated by the lower survival probabilities for both high and low SNAI1 expression groups when metastasis is present. This aligns with the established clinical understanding that metastatic disease is a strong predictor of poor prognosis in cancer patients. Interestingly, the survival curves suggest that the combination of high SNAI1 expression and metastatic disease (M1) results in the poorest survival outcomes. Conversely, patients with low SNAI1 expression and no metastasis (M0) demonstrate the most favorable survival probabilities, indicating a potential protective effect of low SNAI1 expression in the absence of metastasis (Fig. 2F).

SNAI1: a pan-cancer prognostic indicator of poor outcomes

The role of Snail family zinc finger 1 (SNAI1) in cancer biology is not limited to LUSC. Our comprehensive analysis of The Cancer Genome Atlas (TCGA) database has uncovered a significant correlation between elevated SNAI1 expression and reduced overall survival (OS) across a wide range of malignancies such as squamous cell carcinoma, adenocarcinomas (lung adenocarcinoma, colon adenocarcinoma), neuroepithelial neoplasms (low-grade glioma, biphasic glioblastomas) and other tumor types (renal papillary cell carcinoma, malignant mesotheliomas), positioning SNAI1 as a potential pan-cancer biomarker for poor prognosis. (Supplemeantary Fig. 1).

Validation of SNAI1 expression and prognosis correlation in multiple LUSC datasets

To further validate our findings on the relationship between SNAI1 expression and patient outcomes in LUSC, we extended our analysis to multiple independent datasets from the Gene Expression Omnibus (GEO) database(GSE3141, GSE29013, GSE4573, GSE157011). We examined several LUSC datasets to assess the consistency of the association between SNAI1 expression and patient prognosis. Across multiple datasets, we observed a recurring pattern: high SNAI1 expression was consistently associated with unfavorable prognosis in LUSC patients. (Fig. 3)

Fig. 3

Validation of SNAI1 expression and prognosis correlation in multiple LUSC datasets (Gene Expression Omnibus (GEO) database: GSE3141, GSE29013, GSE4573, GSE157011)

Relationship between SNAI1 expression and clinical features in LUSC

To investigate the clinical relevance of SNAI1 expression in LUSC, we categorized patients into high and low SNAI1 expression groups using the optimal cutoff values and analyzed the association with various clinical features.

Our analysis revealed significant correlations between high SNAI1 expression and several important clinical parameters, including metastasis status (M), clinical stage, survival time (days), and survival status (P < 0.05) (Table 1). These associations suggest that SNAI1 expression may have important implications for disease progression and patient outcomes in LUSC.

Interestingly, we observed a significant correlation between SNAI1 expression and the expression levels of SNAI2 and SNAI3, indicating a potential interplay among members of the SNAI family in LUSC. However, it is noteworthy that the expression levels of SNAI2 and SNAI3 did not show significant associations with patient prognosis (data not shown).

To further evaluate the prognostic value of SNAI1, we performed univariate and multivariate Cox regression analyses. These analyses demonstrated that SNAI1 expression, along with age, clinical stage, and TNM classification (T, N, M), were significantly associated with clinical prognosis. Importantly, SNAI1 expression remained an independent prognostic factor in the multivariate analysis, suggesting its potential utility as a biomarker in LUSC (Table 2).

Table 1 Clinical characteristicsTable 2 Univariate and multivariate Cox regression analysesConstruction of a nomogram for survival prediction in LUSC patients

To enhance the clinical utility of our findings, we developed a nomogram to predict survival outcomes in LUSC patients. This prognostic tool was constructed based on the results of our multivariable Cox regression analysis.

The nomogram incorporates statistically significant variables identified in our previous analyses, including SNAI1 expression and relevant clinical and pathological characteristics. Each variable is assigned a score on the nomogram, reflecting its relative contribution to patient prognosis.

To use the nomogram, clinicians can calculate a total score for individual patients by summing the scores corresponding to their specific clinical and pathological features. This total score is then used to estimate the probability of 1-year and 2-year overall survival (OS) for LUSC patients.

The inclusion of multiple variables in this model, each independently associated with survival outcomes, allows for a more comprehensive and potentially more accurate prediction of patient prognosis. This approach takes into account the complex interplay of various factors influencing LUSC outcomes ( Fig. 4A). The model’s discrimination ability, quantified by a C-index of 0.71, and its calibration plots, which demonstrate the alignment of predicted probabilities with observed outcomes, substantiate its reliability as a prognostic instrument. (Fig. 4B, C)

Fig. 4

Construction of a nomogram to predict the survival of patients with LUSC. A. Nomogram for Survival Prediction in LUSC Patients and its calibration plots (B, C)

High expression of SNAI1 protein in LUSC correlates with poor prognosis

Our research has identified a significant correlation between SNAI1 gene mRNA levels and the clinical prognosis of patients with LUSC. To further elucidate the link between SNAI1 protein expression and patient outcomes, we conducted an analysis utilizing the Human Protein Atlas database. The findings of our study indicate that elevated levels of SNAI1 protein in LUSC are significantly associated with a poorer prognosis. The median OS for patients in the high and low expression groups of SNAI1 protein were 2.9 years and 6.1 years, respectively (P < 0.05). This statistically significant difference underscores the prognostic value of SNAI1 protein expression in LUSC. Patients exhibiting high expression of SNAI1 protein face a considerably shorter median survival time, suggesting that SNAI1 may serve as a critical biomarker for predicting patient outcomes. (Fig. 5A).

Fig. 5

The expression of SNAI1 protein in LUSC associated with prognosis

Subcellular localization of SNAI1 protein in LUSC tissue cells

The subcellular localization of the SNAI1 protein within LUSC tissue cells was investigated using immunohistochemistry data sourced from the Human Protein Atlas database. The results consistently showed that the SNAI1 protein is predominantly localized in the cell nucleus across varying expression levels, including low, moderate, and high expression groups. This nuclear predominance suggests a pivotal role for SNAI1 in the transcriptional and regulatory processes within the cell.

However, in instances of strong expression, additional cytoplasmic and membrane localization of the SNAI1 protein was observed. This observation of varied subcellular localization in cases of robust expression may indicate that SNAI1 engages in functions beyond nuclear activities, potentially implicating it in other cellular functions or signaling pathways. (Supplementary Figure 2).

The multifaceted role of SNAI1 in LUSC

In our comprehensive analysis of SNAI1’s role in LUSC, we employed Gene Set Enrichment Analysis (GSEA) utilizing the MSigDB database. The identified gene sets associated with SNAI1 expression reveal a complex network of cellular processes and signaling pathways. Upon careful examination, several key themes emerge: (1). Inflammatory and Immune Responses: Gene sets such as HALLMARK_TNFA_SIGNALING_VIA_NFKB, HALLMARK_INFLAMMATORY_RESPONSE, and HALLMARK_INTERFERON_GAMMA_RESPONSE indicate a strong association between SNAI1 and inflammatory pathways. This suggests SNAI1 may modulate the tumor microenvironment and immune responses in LUSC. (2). Cellular Plasticity and Metastasis: The enrichment of HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION and HALLMARK_APICAL_JUNCTION gene sets suggests SNAI1’s involvement in cellular plasticity and potential metastatic processes. This aligns with SNAI1’s known role in epithelial-mesenchymal transition (EMT). (3). Metabolic Reprogramming: HALLMARK_GLYCOLYSIS and HALLMARK_ADIPOGENESIS gene sets. (Table 3)

Table 3 The hallmark gene sets correlated with SNAI1 expressionSNAI1 expression correlates with immune checkpoint molecules in LUSC

Given the association between SNAI1 and immune responses revealed by our GSEA analysis, we further investigated the relationship between SNAI1 and key immune checkpoint molecules in LUSC. Specifically, we examined the correlation between SNAI1 expression and the expression of PDCD1 (PD-1), PDCD1LG2 (PD-L2), CTLA4, LAG-3, TIM-3, TIGIT, and VISTA (V-domain Ig Suppressor of T cell Activation).Our analysis revealed a positive correlation between SNAI1 expression and the expression of all examined immune checkpoint molecules. These positive correlations suggest a potential interplay between SNAI1 and immune checkpoint mechanisms in LUSC (Fig. 6).

Fig. 6

SNAI1 Expression Correlates with Immune Checkpoint Molecules in LUSC

Identification of potential molecular targeted drugs for SNAI1

Utilizing the CELLMINER platform, our investigation delved into a broad spectrum of compounds with the potential to target SNAI1, inclusive of mTOR, as detailed in Table 4. The mTOR (mechanistic target of rapamycin) pathway is a central signaling axis pivotal to the regulation of cell proliferation and survival. Within the oncological landscape of lung cancer, mTOR overexpression is frequently observed, rendering the targeting of mTOR for down-regulation a promising therapeutic strategy in lung cancer treatment, as evidenced by a body of literature [15,16,17].

Table 4 Identification of potential molecular targeted drugs for SNAI1