
==== Front
J Cardiothorac Surg
J Cardiothorac Surg
Journal of Cardiothoracic Surgery
1749-8090
BioMed Central London

3044
10.1186/s13019-024-03044-8
Research
SNAI1: a key modulator of survival in lung squamous cell carcinoma and its association with metastasis
Li Beibei 1037750251@qq.com

Li Rongkai
https://ror.org/038hzq450 grid.412990.7 0000 0004 1808 322X Department of Respiratory Medicine, the Fifth Affiliated Hospital of Xinxiang Medical University (The First People’s Hospital of Xinxiang), Xinxiang City, 453000 Henan Province China
18 9 2024
18 9 2024
2024
19 53119 1 2024
31 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Snail family zinc finger 1 (SNAI1) has been implicated in cancer progression and prognosis across various malignancies. This study aims to elucidate the prognostic significance of SNAI1 expression in Lung Squamous Cell Carcinoma (LUSC) using data from The Cancer Genome Atlas (TCGA) database.

Methods

SNAI1 expression levels in LUSC patients were stratified using X-tile software to establish optimal cut-off values. Kaplan-Meier survival analysis was performed to assess the impact of SNAI1 expression on overall survival (OS). Univariate and multivariate Cox regression analyses were conducted to evaluate the prognostic value of SNAI1, considering clinical parameters such as age, clinical stage, and TNM classification. Additionally, we explored the interaction between SNAI1 expression and metastatic status, and performed Gene Set Enrichment Analysis (GSEA) to investigate associated cellular pathways. Correlations between SNAI1 and immune checkpoint molecules were also examined.

Results

Kaplan-Meier analysis revealed significant differences in OS among high, medium, and low SNAI1 expression groups (p < 0.001), with median survival times of 1.6, 3.0, and 5.8 years, respectively. Dichotomizing patients into high and low SNAI1 expression groups confirmed that high SNAI1 expression was associated with significantly poorer OS (p < 0.001). SNAI1 remained an independent prognostic factor in multivariate analysis. High SNAI1 expression correlated with poorer survival outcomes regardless of metastatic status, and the combination of high SNAI1 expression and metastasis resulted in the poorest survival. GSEA identified significant associations between SNAI1 and inflammatory, immune response pathways. Positive correlations were observed between SNAI1 and key immune checkpoint molecules, suggesting an interplay with immune checkpoint mechanisms.

Conclusions

High SNAI1 expression is a robust prognostic indicator of poor survival in LUSC, independent of other clinical factors. Its association with immune checkpoint molecules highlights its potential as a therapeutic target. These findings underscore the prognostic and therapeutic relevance of SNAI1 in LUSC and possibly other cancers. Further research is warranted to explore targeted therapies against SNAI1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13019-024-03044-8.

Keywords

SNAI1
Lung squamous cell carcinoma
Prognosis
Immune checkpoints
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pmcIntroduction

Lung cancer remains a formidable challenge in oncology, with lung squamous cell carcinoma (LUSC) accounting for a significant proportion of cases worldwide [1–3]. Characterized by its aggressive nature and propensity for local invasion and distant metastasis, LUSC poses a substantial threat to public health. Despite advances in diagnostic techniques and therapeutic strategies, the prognosis for patients with LUSC continues to be poor, highlighting the urgent need for novel treatment approaches. Within the Chinese context, the narrative is no different, with lung cancer securing the highest ranks in terms of morbidity and mortality, thereby presenting a profound threat to public health. The gravity of this disease underscores the urgency and complexity of its prevention and management strategies [3–5].

The Snail family zinc finger 1 (SNAI1) gene, a member of the Snail superfamily of transcription factors, has garnered substantial attention in the field of cancer biology due to its pivotal role in the regulation of epithelial-mesenchymal transition (EMT). EMT is a developmental process that has been co-opted by cancer cells to facilitate invasion, metastasis, and resistance to therapy. The expression of SNAI1 is tightly regulated in normal tissues, but its dysregulation has been implicated in the progression of various malignancies [6–9].

Our research group’s previous endeavors have cast a spotlight on the pivotal biological roles that SNAI1 assumes in both healthy and tumorigenic tissues. Motivated by these findings, the present study ventures to delineate the specific role of SNAI1 and to unravel the underlying molecular mechanisms through which it operates in LUSC.

Methods

GEPIA2 database analysis

For the examination of gene expression patterns, we employed the GEPIA2 database, an online resource known for its extensive collection of gene expression data. The GEPIA2 platform (http://gepia2.cancer-pku.cn) provides access to a comprehensive dataset encompassing 9,736 tumor samples and 8,587 normal tissue samples. These samples are sourced from the renowned The Cancer Genome Atlas (TCGA) project and the Genotype-Tissue Expression (GTEx) project, offering a rich tapestry of expression profiles for in-depth analysis. Within the functional module of GEPIA2, we conducted a single-gene analysis focused on SNAI1. This analysis was designed to explore the expression levels of SNAI1 across a spectrum of cancer types and normal tissues. We further investigated the correlation of SNAI1 expression with clinical parameters, including staging, overall survival (OS), and disease-free survival (DFS). For the analysis, we adhered to the default settings and thresholds provided by the GEPIA2 database, ensuring that our findings are based on the standard parameters utilized by the platform.

TCGA database utilization

For our investigation into the molecular characteristics of LUSC, we sourced gene expression and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. The data were meticulously downloaded and subsequently integrated using R-4.3.3 software, ensuring the compatibility and coherence of the dataset for subsequent analysis.

Optimal cut-off value analysis using X-tile software

In our quest to discern the prognostic significance of SNAI1 gene expression in LUSC patients, we employed the X-tile software, a robust tool for determining optimal cut-off values in biomarker analysis. This software-enabled approach allowed for the stratification of patients into distinct prognostic groups based on the expression levels of the SNAI1 gene.

The expression levels were meticulously categorized into three-tier and two-tier classifications for patient prognosis analysis, providing a nuanced view of the gene’s expression profile in relation to patient outcomes. Utilizing the Kaplan-Meier method, we constructed survival curves to explore the correlation between the categorized SNAI1 expression levels and the survival rates of LUSC patients.

GEO data validation

To substantiate the correlation between SNAI1 expression and patient prognosis in LUSC, we conducted a validation analysis using multiple datasets from the Gene Expression Omnibus (GEO) database, specifically GSE3141, GSE29013, GSE4573, and GSE157011. These datasets were selected to provide a comprehensive and independent verification of our initial findings. For each dataset, SNAI1 gene expression levels were extracted and normalized to ensure comparability across different platforms. Patients were categorized into high and low SNAI1 expression groups based on the optimal cutoff value. Kaplan-Meier survival analysis was then applied to assess the impact of SNAI1 expression on overall survival, with statistical significance determined using the log-rank test.

Analysis of SNAI1 protein expression using the human protein atlas

The Human Protein Atlas (HPA) database was utilized as a rich source of information regarding SNAI1 protein expression patterns across diverse tissues and tumor samples. This comprehensive resource offers valuable insights into the spatial and cellular distribution of proteins in both normal and pathological conditions. We specifically examined the expression profile of the SNAI1 protein in patients diagnosed with LUSC by analyzing the immunohistochemistry (IHC) data available on the HPA platform. The database’s extensive collection of IHC data allowed us to investigate the subcellular localization patterns of the SNAI1 protein within LUSC tissues.

Gene set enrichment analysis (GSEA)

The GSEA software, version 4.0.3, was employed to conduct enrichment analysis. GSEA is a computational tool that determines whether a gene set is enriched in a specific biological state or condition compared to a reference state. Prior to analysis, the gene expression data were preprocessed to ensure compatibility with GSEA requirements. This included normalization and transformation of raw data to ensure accurate representation of gene expression levels. Gene sets representing hallmark biological pathways and oncogenic signatures were defined based on established gene ontology and known molecular pathways. These gene sets served as the basis for evaluating the enrichment of biological themes within the LUSC samples. The analysis was performed with default settings in GSEA, which include a weighted enrichment statistic (WES) and a nominal P-value to assess the significance of gene set enrichment. The threshold for significance was set at a nominal P-value < 0.05 [10].

TIMER database

The TIMER (Tumor Immune Estimation Resource) database was employed to retrieve expression data for SNAI1 and a panel of immune checkpoint molecules. For the purpose of this study, we selected SNAI1 and the following immune checkpoint molecules: PDCD1 (PD-1), PDCD1LG2 (PD-L2), CTLA4, LAG-3(HAVCR2), TIM-3, TIGIT, and VISTA (V-domain Ig Suppressor of T cell Activation, C10ORF54). These genes were chosen based on their known roles in immune regulation and checkpoint blockade in cancer therapy [11–13].

Screening potential molecular targeted drugs for SNAI1 using CELLMINER

Utilizing the CELLMINER platform, we conducted a targeted drug screening against the SNAI1 gene. The selection of compounds was based on their significant efficacy against cell lines with elevated SNAI1 expression, suggesting their potential as targeted therapeutic agents. To assess the relationship between drug sensitivity and SNAI1 expression, we employed Spearman’s correlation coefficients. A significant correlation was defined as a correlation coefficient (cor) greater than 0.3, indicating a positive association between drug efficacy and SNAI1 expression levels [14].

Statistical analysis

The statistical analyses for this study were executed utilizing IBM SPSS software, version 26.0. For the comparison of numerical data, we applied the non-parametric Mann-Whitney U test, which is well-suited for assessing differences between two independent groups without assuming a normal distribution. In the context of categorical data, the chi-square test was employed to examine the association between categorical variables. Survival analysis was approached using the Kaplan-Meier method, a standard technique for estimating the survival function from time-to-event data. The log-rank test was incorporated to compare survival curves between different groups, assessing the statistical significance of observed differences. To evaluate the overall survival (OS) and identify potential prognostic factors, univariate and multivariate Cox proportional hazard models were constructed. A backward elimination procedure was applied to refine the model, retaining variables that significantly contributed to the predictive accuracy of OS. A Nomogram plot was developed for predicting the OS of cancer patients. This visual predictive tool was created using R programming language packages, specifically the ‘survival’ and ‘rms’ libraries, which facilitated the integration of various prognostic factors into a single predictive model. All statistical tests performed were two-tailed, and a P-value of less than 0.05 was considered to indicate statistical significance, denoting a rejection of the null hypothesis in favor of the alternative hypothesis.

Results

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 characteristics

Characteristic	N	SNAI1high, N = 2631	SNAI1low, N = 2291	p-value2	
age	492	69 (62, 74)	68 (61, 73)	0.13	
gender	492			0.6	
FEMALE		71 (27%)	57 (25%)		
MALE		192 (73%)	172 (75%)		
stage	492			< 0.001	
stageI		116 (44%)	106 (46%)		
stageII		61 (23%)	87 (38%)		
stageIII		40 (15%)	34 (15%)		
stageIV		46 (17%)	2 (0.9%)		
T	492			0.3	
T1		62 (24%)	48 (21%)		
T2		145 (55%)	144 (63%)		
T3		43 (16%)	27 (12%)		
T4		13 (4.9%)	10 (4.4%)		
N	492			0.3	
N0		170 (65%)	144 (63%)		
N1		64 (24%)	63 (28%)		
N2		27 (10%)	18 (7.9%)		
N3		2 (0.8%)	4 (1.7%)		
M	492			< 0.001	
M0		218 (83%)	227 (99%)		
M1		45 (17%)	2 (0.9%)		
SNAI1	492	4.46 (3.40, 6.49)	1.52 (1.12, 1.96)	< 0.001	
SNAI2	492	20 (12, 29)	22 (16, 32)	0.003	
SNAI3	492	0.87 (0.58, 1.33)	0.54 (0.36, 0.88)	< 0.001	
futime	489	16 (7, 29)	22 (12, 43)	< 0.001	
fustat	492	119 (45%)	76 (33%)	0.006	
1Median (IQR); n (%)

2Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Table 2 Univariate and multivariate Cox regression analyses

	Univariate Analysis	Multivariate Analysis	
	HR	95%CI	P-value	HR	95%CI	P-value	
SNAI1	1.77	(1.25–3.26)	0.001	1.54	(1.06–2.63)	0.023	
age	1.12	(1.01–1.36)	0.028	1.02	(1.01–1.82)	0.036	
sex	1.02	(0.62–1.44)	0.6				
stage	1.39	(1.49-2. 43)	0.006	1.18	(1.09–1.82)	0.048	
T	1.52	(1.35–2.84)	0.001	1.34	(1.16–1.92)	0.019	
N	1.36	(1.06–1.88)	0.019	1.31	(1.06–1.82)	0.028	
M	2.52	(1.84–5.61)	0.014	1.85	(1.26–2.83)	0.046	

Construction 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 expression

NAME	ES	NES	P-value	FDR q-val	
HALLMARK_TNFA_SIGNALING_VIA_NFKB	0.44	2.09	0	0	
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	0.42	2.06	0	0.001	
HALLMARK_P53_PATHWAY	0.43	2.06	0	0.001	
HALLMARK_HYPOXIA	0.4	1.94	0	0.001	
HALLMARK_UV_RESPONSE_UP	0.35	1.65	0	0.013	
HALLMARK_INFLAMMATORY_RESPONSE	0.33	1.63	0	0.013	
HALLMARK_GLYCOLYSIS	0.31	1.48	0.005	0.047	
HALLMARK_INTERFERON_GAMMA_RESPONSE	0.28	1.38	0.01	0.113	
HALLMARK_APICAL_JUNCTION	0.28	1.35	0.027	0.121	
HALLMARK_ADIPOGENESIS	0.28	1.35	0.023	0.113	
HALLMARK_COMPLEMENT	0.28	1.34	0.025	0.107	

SNAI1 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 [15–17].

Table 4 Identification of potential molecular targeted drugs for SNAI1

		Drug activities		
Correlations	P-value	Name	Mechanism	FDA status	
0.455	0.000	GSK-2,636,771	PK: PIK3,MTOR	Clinical trial	
0.451	0.000	JNJ-42,756,493	PK: FGFR, FGFR1,FGFR2,FGFR3,FGFR4	FDA approved	
0.404	0.002	E-3810	PK: PDGFR, FGFR, VEGFR	Clinical trial	
0.382	0.003	KU-55,933	PK: ATM	Clinical trial	
0.379	0.004	PF-03084014 diastereomer 1	PSM|APH1|Gamma secretase	Clinical trial	
0.374	0.004	E-7820	Ang|ITG	Clinical trial	
0.346	0.007	Lificguat	HYP|HIF1|EPAS1	Clinical trial	
0.357	0.007	PRN-1371	PK: FGFR	Clinical trial	
0.341	0.008	Sonidegib	SMO|Hg-Smo	FDA approved	
0.345	0.009	GSK-690,693	PK: AKT, AKT1,AKT2,AKT3	Clinical trial	

Discussion

The present study elucidates the prognostic significance of SNAI1 gene expression in LUSC by leveraging comprehensive data from the TCGA database. Our findings indicate a substantial correlation between SNAI1 expression levels and patient survival outcomes. The initial stratification of SNAI1 expression into high, medium, and low groups, based on optimal cut-off values determined by X-tile software, revealed a clear survival trend. Notably, patients with lower SNAI1 expression exhibited progressively better long-term survival, with median survival times increasing from 1.6 years for the high expression group to 5.8 years for the low expression group. This observation underscores the potential of SNAI1 as a prognostic biomarker in LUSC. Further refinement of our analysis through dichotomization of SNAI1 expression into high and low groups, using an optimal cut-off value, confirmed the initial findings. Kaplan-Meier survival analysis of these two groups revealed a significant difference in overall survival, with high SNAI1 expression being associated with poorer outcomes (p < 0.001). This robust statistical approach, incorporating both three-tier and two-tier classifications, provides strong evidence for the prognostic value of SNAI1 in LUSC. The impact of metastasis on LUSC prognosis was also examined, revealing a significant difference in median survival based on metastatic status. However, the stratified survival analysis indicated that elevated SNAI1 expression may be associated with a poor prognosis, irrespective of the presence of clinically detectable metastasis. This observation is intriguing and suggests that SNAI1 may play a role in promoting aggressive tumor behavior, even in the absence of overt metastatic disease.

Our comprehensive analysis of the TCGA database highlights the significant role of SNAI1 as a pan-cancer prognostic indicator. Elevated SNAI1 expression is notably correlated with reduced overall survival across various malignancies, including squamous cell carcinoma, adenocarcinomas, neuroepithelial neoplasms, and other tumor types such as renal papillary cell carcinoma and malignant mesotheliomas. This widespread association underscores the potential of SNAI1 as a robust biomarker for poor prognosis in diverse cancer types.

To validate these findings specifically in LUSC, we conducted an analysis using multiple independent datasets from the Gene Expression Omnibus (GEO). Consistent with our initial observations, high SNAI1 expression was recurrently linked with unfavorable prognosis across all examined LUSC datasets (GSE3141, GSE29013, GSE4573, GSE157011). This consistency across multiple independent cohorts strengthens the evidence for SNAI1’s role as a negative prognostic marker in LUSC.

The recurring pattern of high SNAI1 expression being associated with poor outcomes in LUSC patients suggests that SNAI1 could be pivotal in the underlying mechanisms driving aggressive tumor behavior. This aligns with previous studies indicating that SNAI1 is involved in processes such as epithelial-mesenchymal transition (EMT), which is critical for cancer metastasis and progression [18–23]. Therefore, targeting SNAI1 could offer a potential therapeutic strategy to improve patient outcomes in LUSC and potentially other cancers with elevated SNAI1 expression.

This study highlights the clinical significance of SNAI1 expression in LUSC. Our findings demonstrate that elevated SNAI1 expression is significantly associated with advanced metastasis status, clinical stage, and worse survival outcomes, suggesting its potential role in LUSC progression and prognosis. These results align with previous studies implicating SNAI1 in promoting epithelial-mesenchymal transition (EMT) and cancer cell invasiveness [6–9, 24].

The univariate and multivariate Cox regression analyses emphasize the independent prognostic value of SNAI1 expression in LUSC, alongside established clinical variables such as age, clinical stage, and TNM classification. To facilitate clinical application, we constructed a nomogram integrating SNAI1 expression with relevant clinical and pathological features for individualized survival prediction. The model’s discrimination ability and calibration plots support its reliability in estimating 1-year and 2-year overall survival probabilities.

Our study establishes a significant correlation between elevated SNAI1 protein expression and poor prognosis in Lung Squamous Cell Carcinoma (LUSC), as evidenced by reduced median overall survival in patients with high SNAI1 levels. This finding reinforces the prognostic significance of SNAI1 in LUSC. The predominant nuclear localization of SNAI1, with additional cytoplasmic and membrane presence in strongly expressing cells, suggests its multifaceted role in cellular processes. Our GSEA analysis further delineates SNAI1’s involvement in inflammation, immune responses, cellular plasticity, and metabolic reprogramming, indicating its complex interaction with LUSC pathogenesis.

Our comprehensive analysis employing Gene Set Enrichment Analysis (GSEA) uncovers a multifaceted role for SNAI1 in LUSC. The enrichment of gene sets associated with inflammatory and immune responses suggests that SNAI1 may modulate the tumor microenvironment, influencing immune cell infiltration and activity. This is corroborated by the positive correlation observed between SNAI1 expression and the expression of key immune checkpoint molecules, indicating a potential interplay between SNAI1 and immune checkpoint mechanisms in LUSC. These findings are of particular interest given the emerging role of immunotherapy in cancer treatment and warrant further investigation into the potential of SNAI1 as a target for immune checkpoint blockade.

The exploration of therapeutic strategies targeting SNAI1, as detailed in our analysis using the CELLMINER platform, points towards the mTOR pathway as a promising avenue. The mTOR pathway’s central role in cell proliferation and survival, positions mTOR inhibition as a potentially effective therapeutic approach. This aligns with a growing body of literature that supports the targeting of mTOR in cancer treatment [15–17].

In conclusion, our study provides a comprehensive view of SNAI1’s role in LUSC, highlighting its potential as a prognostic biomarker and therapeutic target. The positive correlations between SNAI1 expression and immune checkpoint molecules, along with the enrichment of gene sets associated with diverse cellular processes, suggest a complex interplay of SNAI1 in LUSC pathogenesis. Future research should focus on dissecting these interactions and evaluating the efficacy of targeted therapies, such as mTOR inhibition, in the context of SNAI1-driven LUSC. The development of novel therapeutic strategies that harness the insights gained from this study may hold promise for improving patient outcomes in LUSC.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We thank the editor and reviewers for relevant and helpful comments on the manuscript.

Author contributions

(I) Conception and design: Beibei Li, Rongkai Li; (II) Administrative support: Rongkai Li; (III)Data analysis and interpretation: Beibei Li; (IV) Manuscript writing: Beibei Li; (V) Final approval of manuscript: All authors.

Funding

This study was supported by the Joint Construction Project LHGJ20210891 from the Medical Science and Technology Research Project of Henan Provincial Health Commission.

Contributions: (I) Conception and design: Beibei Li; (II) Administrative support: Rongkai Li; (III)Data analysis: Beibei Li; (IV)Manuscript writing: Beibei Li; (V)Final approval of manuscript: All authors.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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