Comprehensive genomics and functional analyses identify SMARCAL1 as a key oncogenic regulator in lung adenocarcinoma

Introduction

Non-small cell lung cancer (NSCLC) has been the leading cause of cancer-related deaths on a global scale, with lung adenocarcinoma (LUAD) representing the most prevalent subtype of NSCLC (1). Early diagnosis and treatment are critical to the prognosis of lung cancer patients. Radical surgery is the most effective treatment for early-stage NSCLC patients (2). However, patients often experience recurrence due to micro-metastases. Concurrently, despite the continuous advancements in comprehensive treatment options and therapeutic efficacy for NSCLC, the challenges of metastasis, recurrence, and drug resistance in a subset of patients have resulted in the remaining limitations of chemotherapy, targeted therapy, and immunotherapy (3). Consequently, it is imperative to investigate the underlying mechanisms that contribute to the observed heterogeneity in treatment response among patients and to identify novel biomarkers that can enhance prognostic assessment and facilitate more informed therapeutic decisions.

The loss and repair of DNA are critical components of the process of tumorigenesis. The accumulation of structural and functional changes in the genome, mutations in proto-oncogenes that cause cells to proliferate and divide out of control, and mutations in oncogenes that result in their inability to initiate apoptosis when necessary, ultimately contribute to tumorigenesis (4). The DNA damage response (DDR) is initiated when DNA damage occurs in the body due to radiation, chemicals, or spontaneously. The DDR mechanism detects this damage and initiates repair signals, mobilizes repair factors, and determines the fate of the cell in terms of senescence or apoptosis. The switch/sucrose non-fermentable (SWI/SNF) complex, as a component of the chromatin enzyme, participates in the DDR by enhancing the mobility of nucleosomes through the activity of adenosine triphosphatase (ATPase), thereby affecting the DNA repair pathway (5). This, in turn, has the potential to influence the DNA repair pathway.

SMARCAL1 belongs to the SNF2 family of ATP-dependent chromatin remodeling enzymes and encodes a protein that belongs to the SWI/SNF family of proteins. These proteins are involved in gene transcription, DNA damage repair, DNA recombination, DNA methylation, and cell cycle regulation by altering the chromatin structure around genes. SMARCAL1 functions as a DNA fork remodeling enzyme, catalyzing the reannealing of DNA to restart stalled forks (6). Additionally, SMARCAL1 has been shown to alleviate replication stress by limiting the progress of replication forks through the process of fork inversion (7). During DNA replication, SMARCAL1 is recruited to stalled replication forks via its interaction with replication protein A (RPA), which coats exposed single-stranded DNA (ssDNA). Once recruited, this translocase catalyzes the regression and remodeling of the stalled forks, promoting replication fork stability and allowing the cell to safely navigate replication obstacles. By resolving these replication obstacles, SMARCAL1 plays a crucial role in maintaining genomic stability. Disruption or dysregulation of this delicate repair mechanism allows for the accumulation of unresolved DNA damage and structural changes in the genome, which provides the mutagenic environment necessary to drive tumorigenesis.

Previous research has established SMARCAL1 as a multifaceted protein with significant implications in oncology. It has been demonstrated that SMARCAL1 can inhibit alternative lengthening of telomeres (ALT) to influence tumorigenesis (8). As a DDR factor, SMARCAL1 acts as a crucial immune modulator, participating in the immune escape process of tumors by affecting the autonomous immunity of tumors and the expression level of programmed cell death ligand 1 (PD-L1) (9). Pan-cancer analyses have successfully linked SMARCAL1 to poor prognosis in patients with glioma, clear renal cell carcinoma, hepatocellular carcinoma, LUAD, and other cancers (10). Consequently, SMARCAL1 is considered an extremely promising target in the fields of tumor biology and therapeutics due to its involvement in DNA damage repair, immune escape, and tumor cell proliferation (11).

However, significant shortcomings remain in the current understanding of SMARCAL1’s clinical application. While SMARCAL1 overexpression and poor prognosis were previously noted across cancers, its specific oncogenic role and molecular mechanisms driving LUAD have remained largely unclear. Previous research lacks comprehensive clinical and functional validation of SMARCAL1 as a central driver in LUAD. Furthermore, the field currently lacks an integrated approach that correlates SMARCAL1 expression with tumor mutational burden (TMB), anti-tumor immune infiltration, and predicted immunotherapy response to construct a clinically actionable prognostic model for risk stratification in LUAD.

To address these knowledge gaps, this study explored the potential of SMARCAL1 as a biomarker and therapeutic target for LUAD. We conducted an analysis of its expression in LUAD samples using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) and employed a small interfering RNA (siRNA) approach to knock down SMARCAL1 in LUAD cells. This investigation was undertaken to elucidate the effects of SMARCAL1 on cell proliferation, migration, and invasion. Furthermore, we employed bioinformatics analysis in an effort to explore the potential for constructing prognostic prediction models for patients with LUAD using SMARCAL1 (Figure 1). We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2900/rc).

Figure 1 Schematic overview of SMARCAL1 gene function analysis. DEG analysis revealed that SMARCAL1 is highly expressed in lung cancer and strongly correlates with clinical staging and a range of cancer cell functions. We validated the impact of SMARCAL1 on cellular functions by knocking down its expression in lung cancer cells. Subsequently, to further explore the gene’s clinical value, we constructed a prognostic prediction model for lung adenocarcinoma patients using SMARCAL1 and its co-expressed genes, and conducted drug sensitivity analysis, revealing its potential value as a biomarker (created in BioRender, Xie YT [2026], https://BioRender.com/hmpjxwy). DEG, differentially expressed genes; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas.

Methods

Differential expressed genes (DEG) & boxplot

The RNA sequencing (RNA-seq) data for LUAD were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), which contains 541 tumor samples and 59 normal samples. All samples were normalized to transcripts per million (TPM) format. Furthermore, 116 relevant datasets (GSE32863) (12) and clinical information for LUAD were downloaded from the Gene Expression Omnibus (GEO) database via the R package GEOquery (13) (HTTPS://www.ncbi.nlm.nih.gov/geo/). All samples were obtained from Homo sapiens. GPL6884 Illumina HumanWG-6 v3.0 Expression Beadchip. Subsequently, correlation analyses of SMARCAL1 expression and clinical features were performed, and boxplots were generated using the R package ggpubr.

Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA)

The gene SMARCAL1 was analyzed by GO and KEGG annotation using the R package clusterprofiler (14), and the inclusion criteria were set as P<0.05 and false discovery rate (FDR) <0.05.

GSEA (15) was conducted to investigate the biological pathways associated with LUAD and SMARCAL1 expression, which was performed using the data analysis website Linkedomics (https://www.linkedomics.org/login.php).

Prognostic analysis

The six genes most related to SMARCAL1 were selected by Pearson correlation analysis to form the key genes for risk scoring of LUAD patients and constructing prognostic prediction models. The risk score for each patient was calculated based on the linear combination of the expression levels of these key genes multiplied by their corresponding regression coefficients derived from a multivariate Cox proportional hazards regression model. The prognostic Kaplan-Meier (KM) curve analysis of SMARCAL1 and the six related genes was performed using the R package “survival”, and differences between high- and low-expression groups were compared using the log-rank test (16). To enhance clinical applicability, the risk scores and pertinent clinicopathological characteristics were integrated into a multivariate Cox regression framework to formulate a prognostic nomogram. The rms R package was utilized to construct the nomogram, facilitating the prediction of 1-, 3-, and 5-year overall survival (OS) probabilities for LUAD patients. Furthermore, calibration curves were generated to assess the predictive accuracy and discriminatory capacity of the nomogram, comparing predicted survival probabilities against actual observed outcomes. To evaluate the predictive accuracy and discriminatory capacity of the established prognostic nomogram, time-dependent receiver operating characteristic (ROC) curve analysis was employed. The timeROC R package was utilized to generate ROC curves and calculate the corresponding area under the curve (AUC) values specifically for 1-, 3-, and 5-year OS. The AUC serves as a quantitative measure of the model’s ability to correctly distinguish between patients who experience the event (death) and those who do not at the specified time points.

TMB & tumor drug sensitivity analysis

TMB, a recognized biomarker for immunotherapy efficacy, was systematically evaluated across different SMARCAL1 expression subgroups. Somatic mutation data for LUAD (comprising 16,642 mutations across 557 samples) were acquired from the TCGA database. The maftools R package, renowned for its efficiency in summarizing, analyzing, and visualizing mutation annotation format (MAF) files, was employed to calculate TMB and generate waterfall plots to visually compare the mutation landscapes between the SMARCAL1 high and low expression cohorts (17). To predict the clinical response to established chemotherapeutic and targeted agents, we utilized the oncoPredict R package, a robust tool that leverages in vivo and in vitro cell line screening data to accurately predict drug responses in clinical tumor samples (18). The reference drug sensitivity data [half-maximal inhibitory concentration (IC₅₀) values] were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Wilcoxon rank-sum tests were then applied to evaluate the statistical differences in predicted drug sensitivities between the defined SMARCAL1 subgroups.

Single-sample GSEA (ssGSEA) and tumor immune microenvironment (TIME) profiling

To delineate the immune landscape of LUAD and its association with SMARCAL1, ssGSEA and the CIBERSORT algorithm were implemented. ssGSEA, an extension of standard GSEA, was employed to quantify the relative abundance of tumor-infiltrating immune cell types by computing enrichment scores for specific immune-related gene signatures within each individual sample. This method provides a robust, rank-based assessment of immune cell populations resistant to cross-sample normalization errors. Concurrently, the CIBERSORT R package, utilizing a validated leukocyte gene signature matrix (LM22) comprising 22 distinct immune cell types, was applied to deconvolve bulk RNA-seq data and estimate the proportions of immune infiltrates across tumors with varying SMARCAL1 expression levels. Violin plots, generated via the vioplot R package, were utilized to visualize the differential distribution of these immune cell abundances between the subgroups. To further elucidate the relevance of SMARCAL1 in the context of immunotherapy, the expression profiles of 47 established immune checkpoint genes were extracted, and the limma R package was used to statistically evaluate their correlation with SMARCAL1 expression (19). Finally, the tumor immune dysfunction and exclusion (TIDE) computational framework was utilized to predict the potential clinical response to immune checkpoint blockade (ICB) by integrating T-cell dysfunction and exclusion signatures.

LUAD samples and RT-qPCR

From October 2024 to December 2024, fresh cancerous and paracancerous tissues from 30 patients diagnosed with LUAD were obtained from The First Affiliated Hospital of Guangxi Medical University. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Guangxi Medical University (approval No. 2025-E0858). Informed consent was taken from all the patients (or a statement that it was not required and why)

Fresh tissue samples or cells were then added to a solution of RNAiso Plus (Takara, Kyoto, Japan) at 4 °C, in order to extract total RNA. The generation of cDNA was initiated with the reverse transcription of 1.0 µg of RNA, a process that was executed in accordance with the protocol stipulated by the manufacturer. The designated protocol was that of the Prime Script RT Master Mix (Takara).

RT-qPCR was conducted using a 2X Q3 SYBR qPCR Master Mix (ToloBio, China) in the Roche LightCycler480II Real-time PCR System to measure gene expression levels. The relative quantitative gene expression of SMARCAL1 and control genes was analyzed using the 2^−ΔΔCt method. SMARCAL1 forward, 5'-AGCTCTACACGCAGATCATCGC-3', and reverse, 5'-GGTTGGAGGAACCTGAGTAGTC-3'; GAPDH forward, 5'-GCACCGTCAAGGCTGAGAAC-3', and reverse, 5'-ATGGTGGTGAAGACGCCAGT-3'.

Cell culture and transfection

Human LUAD cell lines A549 and H1299 were sourced from the Chinese Academy of Sciences (Shanghai, China), and the cells were passaged and cultured in Dulbecco’s modified Eagle medium (Gibco, Grand Island, USA) with the addition of fetal bovine serum (Gibco), penicillin, and streptomycin. These cell lines were cultivated in a humidified incubator maintained at 37 °C with 5% CO₂.

SMARCAL1 siRNA was purchased from Nanning Gensis Biotechnology Ltd (Nanning, China), with the sequence 5'-GCAGAAGAUCUACGACCUATT-3', 5'-GCUCUUGAGUUAUGAGUUATT-3' and 5'-GAUUGAAGAGAAUCGACAATT-3'. SMARCAL1 siRNA or negative control (NC) siRNA was transfected into serum-free medium using Lipofectamine 3000 (Hanbio, China), followed by 24-hour culture.

Cell viability assay

To assess cell viability, we employed the Cell Counting Kit-8 (CCK-8; Beyotime) and the BeyoClickTM EdU-555 Cell Proliferation Detection Kit (EdU; Beyotime). The cells were seeded into 96-well plates, and CCK-8 solution was added at 24-, 48- and 72-hours post-transfection. The cell numbers were subsequently measured at a wavelength of 450 nm. The cells were seeded into 6-well plates, and 10 µM EdU solution was added 24 hours after the transfection. Subsequently, the cell images were observed and photographed using a fluorescence microscope (EVOS M7000, Thermo Fisher Scientific, USA).

Wound healing assay

Cultivation of cells were conducted in a 6-well plate containing complete medium until the density reaches approximately 80–90%. The tip of a 1,000 uL pipette was utilized to create a vertical wound, followed by the removal of cell debris using phosphate-buffered saline (PBS). A Nikon microscope (Japan) then was utilized to photograph the same area at 0–48 h.

Transwell assay

Invasion and migration experiments were conducted using Transwell chambers with a pore size of 8 µm. The cells then were collected, resuspended in fresh basal medium, and added to the upper chamber in a volume of 200 µL. Subsequently, 500 µL of the medium containing 10% fetal bovine serum (FBS) was added to the lower chamber. The samples were then placed in a culture incubator for 36 h. Thereafter, the samples were stained with a crystal violet solution and subsequently imaged under microscope.

Colony formation assay

A total of 500 cells that had undergone transfection were distributed uniformly across a six-well plate and subsequently cultured for a period of 14 days. The cells were then fixed with 4% paraformaldehyde, stained with crystal violet, photographed, and counted using ImageJ.

Statistical analysis

All data processing and analysis in this article were conducted utilizing R software version 4.2.1 or GraphPad Prism 9. Continuous variables are presented as mean ± standard deviation (SD). The Wilcoxon Rank Sum Test was used for comparison between the two groups. Univariate survival analysis was performed by KM survival analysis with the log-rank test. Multivariate survival analysis was performed using the Cox regression model, and a P value of less than 0.05 was used as the criterion for significant difference.

Results

SMARCAL1 expression correlates with tumor size, lymph node involvement and metastasis (TNM) stage of LUAD

Gene expression matrix from TCGA and GEO (GSE32863) databases for LUAD samples were divided into tumor group and normal group, revealed SMARCAL1 is up-regulated in lung tumor tissue (Figure 2A,2B). Then using TCGA clinical dataset, the association between SMARCAL1 and clinical pathological variables was explored. A substantial association was identified between SMARCAL1 expression and clinicopathological variables. An escalation in expression levels was observed concomitant with more advanced clinicopathological stages (N, M, stage). Nevertheless, an apparent absence of correlation between SMARCAL1 expression and both age and sex were observed (Figure 2C-2I).

Figure 2 SMARCAL1 expression correlates with TNM stage of LUAD. (A,B) Based on the TCGA and GSE32863, the expression of SMARCAL1 in LUAD. (C) The expression of SMARCAL1 in different age groups of LUAD. (D) The expression of SMARCAL1 in different genders of LUAD. (E-H) The expression of SMARCAL1 in different TNM stages of LUAD. (I) Heatmap of clinical feature between different SMARCAL1 clusters in TCGA-LUAD. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; TNM, tumor-node-metastasis.

Enrichment analysis for SMARCAL1

A comprehensive investigation was conducted into the relationship between the biological process (BP), cellular component (CC), molecular function (MF), and KEGG pathways of SMARCAL1 and LUAD via GO and KEGG enrichment analysis. The findings indicated that SMARCAL1 was predominantly enriched in BP, including gland development, monocyte differentiation, and epithelial tube morphogenesis. Additionally, it was enriched in CC, such as collagen-containing extracellular matrix (ECM), neuronal cell body, and glutamatergic synapse. Furthermore, SMARCAL1 was found to be enriched in MF, for instance, DNA-binding transcription activator activity, DNA-binding transcription factor binding, and metal ion transmembrane transporter activity (Figure 3A). Moreover, KEGG analysis revealed that SMARCAL1 was enriched in the calcium signaling pathway and the PI3K-Akt signaling pathway (Figure 3B). Then we showed that SMARCAL1 was enriched for cell cycle, DNA repair, and mismatch repair-related pathways in tumor tissues by GSEA analysis (Figure 3C,3D).

Figure 3 Enrichment analysis for SMARCAL1. (A,B) The bar graph shows GO and KEGG enrichment analysis results of SMARCAL1: BP, CC, MF and pathway. (C,D) GSEA revealed the affected pathways by SMARCAL1. The screening criteria for GSEA, GO and KEGG enrichment analysis were P<0.05 and FDR value (q value) <0.05. BP, biological process; CC, cellular component; FDR, false discovery rate; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Silencing SMARCAL1 inhibits the proliferation of lung cancer cells

RT-qPCR analysis demonstrated that the messenger RNA (mRNA) expression of SMARCAL1 was significantly increased in LUAD tissues compared with normal lung tissues (Figure 4A). Subsequently, to investigate the role of SMARCAL1 in lung cancer progression, we transfected SMARCAL1 using targeted siRNA. The results of RT-qPCR analysis confirmed that the mRNA expression of SMARCAL1 was significantly decreased in A549 and H1299 after transfection (Figure 4B,4C).

Figure 4 Silencing SMARCAL1 inhibits the proliferation of lung cancer cells. (A) qRT-PCR analysis showing differential expression of SMARCAL1 in lung cancer tissues and adjacent non-cancerous tissues. GAPDH was used as the endogenous control. (B,C) SMARCAL1 expression after knockdown in A549 and H1299. (D,E) CCK-8 assay for cell viability in A549 and H1299 cells following transfection. (F) Representative images and quantification of EdU assay in the transfected A549 and H1299 cells (magnification ×). (G,H) Representative images and quantification of colony formation assays in the transfected H1299 and A549 cells (stained). *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. CCK-8, Cell Counting Kit-8; mRNA, messenger RNA; qRT-PCR, quantitative reverse transcription polymerase chain reaction; si-NC, small interfering RNA-negative control; si-SMA, small interfering RNA targeting SMARCAL1.

The cells were divided into two groups for experimental validation: the normal control (NC) group and the SMARCAL1 interference (SI) group. The CCK-8 assay demonstrated that the knockdown of SMARCAL1 led to a significant decrease in cell viability for both A549 and H1299 cells (Figure 4D,4E). The EdU assay also indicated that the proliferation of A549 and H1299 cells was suppressed in the SI group (Figure 4F). Furthermore, the clone formation assay revealed that the knockdown of SMARCAL1 significantly suppressed the proliferation of A549 and H1299 cells (Figure 4G,4H).

Knocking down SMARCAL1 affects the invasion and migration of lung cancer cells

In addition, the effects of SMARCAL1 on the invasion and migration ability of lung cancer cells were investigated. The wound healing assay demonstrated that the migration ability of A549 and H1299 was significantly decreased after SMARCAL1 knockdown (Figure 5A,5B). Similarly, the transwell assay revealed that the invasion and migration ability of lung cancer cells was significantly inhibited after SMARCAL1 knockdown (Figure 5C,5D).

Figure 5 Knocking down SMARCAL1 affects the invasion and migration of lung cancer cells. (A,B) Representative images and quantification of the wound-healing assays in the transfected H1299 and A549 cells (magnification ×, stained). (C,D) Representative images and quantification of the transwell assays in the transfected A549 and H1299 cells (magnification ×, stained). ns, not significant; *, P<0.05; ***, P<0.001; ****, P<0.0001. si-NC, small interfering RNA-negative control; si-SMA, small interfering RNA targeting SMARCAL1.

Survival analysis of SMARCAL1 and its co-expressed genes

We identified the genes associated with SMARCAL1 expression using Spearman correlation analysis. Then five meaningful genes were selected with the largest correlation coefficients to construct a risk-prognostic model together with SMARCAL1 (Figure 6A,6B). Subsequently, we performed survival analysis of SMARCAL1 and its related genes and risk scores, and KM curves were plotted by grouping according to gene expression (Figure 6C-6F). The survival analysis revealed a significant difference in the survival prognosis of LUAD patients with high and low expression of SMARCAL1, PSMD1, and NCKAP1, with the high-expression group demonstrating a worse prognosis. Additionally, a significant difference in the survival prognosis was observed between the high- and low-risk score groups, with the high-risk group exhibiting a worse prognosis.

Figure 6 Survival analysis of SMARCAL1 and its co-expressed genes. (A) Volcano plot of genes associated with SMARCAL1 expression. (B) Chord plot of genes associated with SMARCAL1 expression. (C-F) Survival analysis for different expression levels of key genes.

Developing a prognostic prediction model for LUAD

Subsequently, the clinicopathologic characteristics and risk scores were subjected to one-way COX regression analysis, as illustrated in the forest plot (Figure 7A). A multifactorial COX regression analysis was performed using clinicopathologic features and risk scores. Nomogram plots showed the 1-, 3-, and 5-year survival rates of lung cancer patients predicted by this risk score model (Figure 7B). The predicted survival probabilities were close to the true survival probabilities, which indicated that the predictive performance of the model was relatively impressive (Figure 7C). The time-dependent ROC curve analysis was performed. The areas under the curve (AUC) for 1-, 3-, and 5-year OS rates for risk scores were 0.638, 0.610, and 0.630, respectively (Figure 7D). A further comparison was conducted of the AUC values of 5-year OS rates, with these values being based on risk scores and clinical characteristics of lung cancer patients (Figure 7E).

Figure 7 Key gene-based prognostic model. (A) Forest plot for single factor Cox regression analysis of clinical characteristics and risk scores. (B) Prognostic nomogram of multivariate Cox regression model. (C) Calibration curve. (D) Time-dependent ROC curves of risk scores predicting 1-year, 3-year, and 5-year OS, and corresponding AUC values. (E) Time-dependent ROC curves of risk scores and clinical characteristics predicting 5-year OS, and corresponding AUC values. *, ***. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; M, metastasis; N, node; OS, overall survival; ROC, receiver operating characteristic; T, tumor.

Therapeutic sensitivity analysis for SMARCAL1

In the subsequent stage of the research, an analysis of gene mutations was conducted in order to achieve a more profound comprehension of the influence that SMARCAL1 expression levels exert on the outcomes of LUAD treatment. We found that the number of mutations in the SMARCAL1 high-expression group was significantly higher than in the SMARCAL1 low-expression group (P<0.05) (Figure 8A,8B). Additionally, we extracted sensitivity data for popular anticancer drugs from the GDSC database and compared drug sensitivity between two distinct SMARCAL1 expression clusters (Figure 8C). The subsequent enumeration comprises a selection of pharmaceuticals which demonstrate discrepancies in efficacy between the two clusters: nilotinib, axitinib, SB216763, KU-55933, doramapimod, AD8055, PF-4708671, GSK269962A, SB505124, entinostat, ribociclib, JAK1_8709, KRAS inhibitor-12, venetoclax, ABT737, GSK2578215A, WEHI-539, AZD6482. All of them show statistically significant differences.

Figure 8 SMARCAL1 expression correlates with therapy sensitivity and immune landscape. (A,B) Comparison of TMB high-expression and low-expression groups. (C) Comparison of targeted therapies in high and low SMARCAL1 clusters. (D) Correlation of SMARCAL1 expression with tumor-infiltrating immune cells based on ssGSEA. (E) Comparison of ICIs of high and low SMARCAL1 clusters. (F) TIDE score in different SMARCAL1 subgroups. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. ICI, immune checkpoint inhibitor; ssGSEA, single-sample gene set enrichment analysis; TIDE, tumor immune dysfunction and exclusion; TMB, tumor mutational burden.

The present study demonstrated the relationship between immune cells and SMARCAL1 expression in LUAD tissues using Cibersort immune infiltration analysis based on the grouping of SMARCAL1 expression (Figure 8D). The results showed a statistically significant difference in the abundance of memory B cells, plasma cells, CD8⁺ T cells, follicular helper T cells, M0 macrophages, M2 macrophages, activated dendritic cells, and neutrophils in the high and low SMARCAL1 expression groups. To further explore the relationship between SMARCAL1 expression pairs and tumor immunity and its clinical value, we analyzed the differences of immune checkpoint genes in the SMARCAL1 high and low expression groups using the (ICG) algorithm (Figure 8E). We found that the differences in the expression of the immune checkpoint genes, such as BTLA, CD27, and PD-L1, were statistically significant. In order to investigate the potential impact of SMARCAL1 expression on immunotherapy efficacy, the TIDE algorithm was performed to analyze different SMARCAL1 subgroups (Figure 8F). The findings indicated that patients exhibiting high SMARCAL1 expression levels had significantly higher TIDE scores compared to those with low expression. This observation suggests that patients with low SMARCAL1 expression levels may potentially derive greater benefit from immunotherapy compared to those with high expression levels.

Discussion

Despite advancements in early detection and multimodal interventions, a substantial proportion of patients with NSCLC continue to experience disease recurrence and therapeutic resistance (20,21). The underlying mechanisms driving this clinical heterogeneity frequently involve the dysregulation of chromatin remodeling complexes, which fundamentally govern gene transcription and genomic stability, thereby affecting key aspects of cancer development—the stability of oncogenes and tumor suppressor genes (22,23).

Chromatin remodeling complexes have been shown to alter the structure of nucleosomes, thereby exposing genes that affect transcriptional processes (24). It has been determined that these complexes are a key component in maintaining genomic homeostasis. There are four major families of chromatin remodeling factors, including the SWI/SNF, imitation switch (ISWI), Mi-2, and IN080 families. Genes from members of the SWI/SNF complex are mutated in more than 25% of tumors, making them the most frequently mutated chromatin-associated cancer genes (25). The SWI/SNF complex has been observed to interact with a variety of transcription factors, thereby influencing the accessibility of chromatin. Moreover, the SWI/SNF complex has been implicated in the process of DNA repair, and it has been shown to promote chromatin remodeling at sites of DNA double-strand breaks (26). This process occurs through the recruitment of mismatch repair proteins into the chromatin, thereby initiating the mismatch repair system. Collectively, these functions contribute to the maintenance of genomic stability and the prevention of genetic and chromosomal abnormalities that can result in cancer.

Within this context, our study identifies SMARCAL1, an ATP-dependent chromatin remodeling enzyme, as a pivotal oncogenic regulator in LUAD. Rather than merely functioning as a passive prognostic marker, our integrated analysis suggests that SMARCAL1 actively orchestrates LUAD progression through the intersection of replication stress tolerance, oncogenic signaling cascades, and profound immune microenvironment remodeling. Clinically, this is manifested in a positive correlation with advanced clinicopathological features (especially N stage, M stage, and overall clinical stage).

In fact, the association between SMARCAL1 and clinicopathological features further confirms the results of our enrichment analysis. The association with advanced T stage (primary tumor growth) can be largely attributed to SMARCAL1’s role in accelerating the cell cycle and mitigating replication stress. Our KEGG and GSEA analyses revealed significant enrichment in cell cycle regulation and the PI3K-Akt signaling pathway. The PI3K-Akt axis is a master regulator of cell survival and proliferation in NSCLC. As an ATP-dependent chromatin remodeler, SMARCAL1 may increase the chromatin accessibility of key downstream effectors within the PI3K-Akt cascade, thereby providing the sustained proliferative signaling required for rapid primary tumor expansion. Furthermore, by restarting stalled replication forks, SMARCAL1 may enable rapidly dividing cancer cells to bypass DNA damage-induced apoptosis, leading to continuous tumor bulk growth. More critically, the pronounced correlation between SMARCAL1 and advanced N and M stages suggests a potent role in facilitating tumor dissemination. Metastatic cascade initiation requires tumor cells to acquire motility and degrade surrounding structural barriers. Our GO analysis notably highlighted SMARCAL1’s enrichment in processes involving the “collagen-containing ECM” and “epithelial tube morphogenesis”. These pathways are inextricably linked to epithelial-mesenchymal transition (EMT) and ECM remodeling. Members of the SWI/SNF chromatin remodeling complex have been previously documented to interact with EMT-inducing transcription factors (such as SNAIL and TWIST) and upregulate matrix metalloproteinases (MMPs) (27). Therefore, it is highly plausible that high SMARCAL1 expression alters the epigenetic landscape to favor an EMT phenotype, directly granting LUAD cells the invasive properties required to breach the basement membrane, intravasate into lymphatic vessels (advancing N stage), and establish distant metastases (advancing M stage). Successful metastasis (M stage) requires circulating tumor cells to evade immune surveillance. Collectively, these mechanisms—PI3K-Akt-driven proliferation, ECM/EMT-mediated invasion, and PD-L1/BTLA-associated immune evasion—provide a comprehensive biological rationale for the strong correlation observed between SMARCAL1 overexpression and advanced TNM staging in LUAD.

Our functional assays demonstrated that silencing SMARCAL1 profoundly suppresses the proliferation, migration, and invasive capacities of LUAD cells. To contextualize these phenotypic changes, our KEGG and GSEA enrichment analyses revealed that SMARCAL1 is significantly associated with the PI3K-Akt and calcium signaling pathways, as well as cell cycle regulation. The PI3K-Akt axis is a canonical driver of survival and metastasis in NSCLC. It is highly probable that SMARCAL1 overexpression facilitates chromatin accessibility for downstream effectors of the PI3K-Akt pathway, thereby sustaining the uncontrolled proliferative signaling observed in our in vitro models. Furthermore, SMARCAL1’s established role in resolving replication stress through fork inversion and maintaining ALT via interactions with the WRN helicase (28,29) likely provides LUAD cells with the necessary genomic plasticity to survive continuous replicative pressure.

Paradoxically, while SMARCAL1 mitigates acute replication catastrophe, its dysregulation in LUAD appears to foster a highly mutagenic landscape. We observed a significantly elevated TMB in patients with high SMARCAL1 expression. This aligns with our GSEA findings linking SMARCAL1 to mismatch repair pathways. The aberrant fork remodeling activity of overexpressed SMARCAL1 may bypass normal DNA damage checkpoints, allowing cells to survive and proliferate while accumulating mutations. TMB has been identified as a predictor of ICB treatment response (30,31). Clinically, this high TMB profile translates into differential vulnerabilities; our drug sensitivity analysis indicated that SMARCAL1-high tumors exhibit distinct responses to targeted agents such as entinostat (an HDAC inhibitor) and ribociclib (a CDK4/6 inhibitor). Research by Eichner et al. (32) revealed that entinostat, an inhibitor of HDAC1 and HDAC3, can reverse lung cancer resistance to the chemotherapy drug trametinib, suggesting entinostat’s potential for combination therapy in lung cancer treatment. Ribociclib, a CDK4/6 inhibitor, has been shown to reverse chemotherapy resistance in small cell lung cancer by inhibiting autophagy (33), indicating its potential for combination therapy with chemotherapeutic agents in the treatment of small cell lung cancer. This suggests that SMARCAL1’s interplay with chromatin structure creates unique, targetable synthetic lethality dependencies in LUAD.

Beyond its cell-intrinsic oncogenic roles, our findings shed light on SMARCAL1’s critical function in shaping the TIME. Recent studies have implicated SMARCAL1 in modulating innate immune signaling, specifically by influencing the cGAS-STING pathway to induce PD-L1 expression and promote immune evasion (34). Our data corroborate and expand upon this mechanism in LUAD. We found that high SMARCAL1 expression is significantly associated with the depletion of critical anti-tumor effectors, including plasma cells and cytotoxic CD8⁺ T cells (35). Because plasma cells promote the formation of tumor-infiltrating lymphocytes (TILs) and CD8⁺ T cells execute direct anti-tumor cytotoxicity, their exclusion suggests a robust, SMARCAL1-driven immunosuppressive niche (36,37). This immunosuppression is further reinforced by the concomitant upregulation of immune checkpoint molecules, notably PD-L1 and BTLA, in the SMARCAL1-high cluster. BTLA possesses the inhibitory tyrosine motif ITMI, which suppresses T-cell proliferation and cytokine production. The upregulation of BTLA in lung cancer TILs has been demonstrated to induce T-cell exhaustion, thereby contributing to tumor immune escape (38). The PD-L1/programmed death-1 (PD-1) signaling pathway is widely regarded as the gold standard for cancer immunotherapy (39). The engagement of BTLA, which suppresses T-cell proliferation, alongside PD-L1, indicates that SMARCAL1 overexpression drives severe T-cell exhaustion. Consequently, SMARCAL1 expression profiling could prove invaluable for identifying patients who might benefit from emerging dual-blockade immunotherapies targeting both the PD-1 and BTLA axes.

To synthesize these complex molecular and immunological interactions into a clinically actionable tool, we integrated SMARCAL1 with its top co-expressed genes to construct a robust prognostic model. This model successfully stratifies patient risk, directly reflecting the cumulative impact of the underlying signaling networks discussed above. Our study constructed a multidimensional prognostic nomogram by integrating SMARCAL1 with its most stable co-expressed gene network. This allows molecular expression profiling analysis to be translated into a practical clinical tool, accurately predicting 1-, 3-, and 5-year survival rates, and providing new insights for individualized risk stratification of patients.

While prior pan-cancer transcriptomic analyses broadly identified SMARCAL1 as a generalized prognostic risk factor across various malignancies, those foundational investigations were predominantly computational and lacked histology-specific functional validation. The primary innovation of our study lies in bridging this gap through direct experimental and clinical validation specifically within LUAD. Unlike purely in silico studies, we confirmed the marked overexpression of SMARCAL1 utilizing fresh clinical LUAD tissues via RT-qPCR. Furthermore, through targeted siRNA knockdown, we provided definitive in vitro evidence that SMARCAL1 actively drives LUAD cell proliferation, migration, and invasion, establishing it not merely as a bystander biomarker, but as a central oncogenic driver. In the realm of treatment and precision medicine, our comprehensive analysis uncovers new avenues for managing therapeutic resistance. We have demonstrated that high SMARCAL1 expression is associated with elevated TMB and targeted vulnerability. Moreover, we have identified drugs such as entinostat and ribociclib as potentially effective therapies for this particular type of LUAD. By mapping the SMARCAL1-driven depletion of critical anti-tumor effectors alongside the concurrent upregulation of multiple immune checkpoints and elevated TIDE scores, our findings provide a novel framework for predicting immunotherapy efficacy. Consequently, SMARCAL1 profiling could critically inform the clinical selection of LUAD patients for emerging dual-blockade immunotherapies, solidifying its value as an indispensable biomarker for therapeutic decision-making.

However, we acknowledge several limitations in our study. While our transcriptomic analyses and in vitro validations provide strong evidence for SMARCAL1’s oncogenic role, reliance on public databases (TCGA and GEO) carries inherent biases. Additionally, as our study focused predominantly on mRNA expression and in vitro functional assays, the exact protein-level interactions and post-transcriptional modifications of SMARCAL1 in vivo require further elucidation. Future research should prioritize in vivo models and multi-omics approaches to comprehensively map SMARCAL1’s direct binding targets and its cross-talk with the PI3K-Akt and cGAS-STING pathways in lung malignancies.

Conclusions

Our research indicates that SMARCAL1 exhibits a significant positive correlation with the development and progression of LUAD. Consequently, a prognostic prediction model for LUAD was constructed, which has the potential to serve as a novel foundation for personalized treatment in patients with this disease. Additionally, we discovered that SMARCAL1 expression levels correlate with alterations in the TIME, suggesting its potential to guide patient selection for immunotherapy regimens.

Acknowledgments

The authors are indebted to public databases such as TCGA and GEO, which play a vital role in the advancement of human medicine through their invaluable contributions. The authors gratefully acknowledge financial support provided by three major initiatives: the National Key Clinical Specialty Construction Project, the Guangxi Medical and Health Key Discipline Construction Project, and the Guangxi Key Clinical Specialty Construction Project.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2900/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2900/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2900/prf

Funding: This work was supported by the National Key Clinical Specialty Construction Project, the Guangxi Medical and Health Key Discipline Construction Project, and the Guangxi Key Clinical Specialty Construction Project.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2900/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Guangxi Medical University (approval number: 2025-E0858). Informed consent was taken from all the patients (or a statement that it was not required and why)

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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