Abstract
Introduction:
The tumor suppressor kinase liver kinase B1 (LKB1) is known to regulate the activity of the metabolic sensor AMP-activated protein kinase (AMPK), which, under energy stress, shifts metabolism from anabolism to catabolism, thus linking LKB1 to AMPK-mediated gene expression. Coupled with its role as a tumor suppressor kinase, LKB1 is an important metabolic regulator implicated in multiple malignancies and is frequently mutated in lung cancer. Previously, we discovered that LKB1 binds to the switch/sucrose non-fermenting (SWI/SNF) chromatin remodeling ATP-dependent helicase subunit SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 (SMARCA4), directly linking LKB1 to gene expression. How LKB1 and SMARCA4 collaborate to regulate gene expression in lung cancer has not been well characterized.
Methods:
We used an in silico approach to explore how LKB1 and SMARCA4 may cooperate to regulate gene expression. We analyzed our previous single-cell RNA-seq (scRNA-seq) dataset from four lung cancer cell lines with differential LKB1 and SMARCA4 expression status to identify genes regulated by both LKB1 and SMARCA4. We correlated our results using bulk RNA-seq results from human lung tumors.
Results:
We show that LKB1 and SMARCA4 likely function together to regulate gene expression in multiple biological processes in lung cancer cell lines. Gene expression profiles from LKB1- and SMARCA4-mutant cells are similar, suggesting that LKB1 and SMARCA4 function in a linear pathway to regulate gene expression. Furthermore, we observed similar results in human lung tumors, particularly in late-stage disease.
Discussion:
We propose a model where LKB1 acts as a nexus between metabolism and gene expression, acting via the SMARCA4–SWI/ SNF complex to regulate gene expression in lung cancer.
Introduction
Metabolic reprogramming plays a crucial role in cancer cells’ ability to thrive in nutrient-poor environments and regulate gene expression through metabolic signaling intermediates. Epigenetic enzymes utilize key metabolic intermediates such as ATP, acetyl-CoA, and S-adenosylmethionine (SAM) to modulate gene expression through histone modifications (Gut and Verdin, 2013; Kottakis et al., 2016; Sutendra et al., 2014). Epigenetic histone modifications alter the chromatin landscape, generating landmarks at gene regulatory elements to differentiate transcriptionally active chromatin regions from transcriptionally silent regions. The switch/sucrose non-fermenting (SWI/SNF) chromatin remodeling complexes, for example, hydrolyze ATP to displace nucleosomes, mobilize histones, and open chromatin for regulating gene expression (Bultman et al., 2000; Kwon et al., 1994). The availability of ATP, acetyl-CoA, and SAM can drastically alter the global chromatin landscape, and the availability of these cofactors is determined by the cells’ metabolic state. This mechanism enables cells to leverage metabolic output to influence cellular responses via epigenetic regulation of gene expression. Mutations in tumor suppressor genes that disrupt metabolic pathways can result in aberrant epigenetic changes commonly associated with malignant transformation.
Important regulators of metabolism and epigenetics are tumor suppressors liver kinase B1 (LKB1) and SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4 (SMARCA4), which are often mutated or lost in non-small-cell lung cancer (NSCLC) (Rodriguez-Nieto and Sanchez-Cespedes, 2009; Marignani, 2005). Interestingly, LKB1 and SMARCA4 are also located in close proximity on chromosome 19p13.3, and loss of heterozygosity (LOH) is frequently observed (Rodriguez-Nieto and Sanchez-Cespedes, 2009; Marignani, 2005). The best characterized role of LKB1 is in energy metabolism, where LKB1 activates AMP-activated protein kinase (AMPK) to shift metabolic pathways from anabolism to catabolism (Bourouh and Marignani, 2022; Hawley et al., 2003; Shaw et al., 2004). SMARCA4 is the ATP-dependent helicase subunit of the SWI/SNF chromatin remodeling complex, hydrolyzing ATP to displace nucleosomes, opening chromatin for transcription factors to bind gene regulatory elements (Bultman et al., 2000; Kwon et al., 1994). Both SMARCA4- and LKB1-mutant NSCLC cases are associated with smokers and, consequently, with KRAS mutations. LKB1 is mutated in approximately 20% of NSCLC cases (La Fleur et al., 2019; Gao et al., 2022), while SMARCA4 is mutated in 8% of NSCLC cases, with 39% of those cases representing LKB1–SMARCA4 co-mutation (Medina et al., 2008; Schoenfeld et al., 2020).
Our laboratory previously discovered that nuclear localized LKB1 binds to SMARCA4 in vivo, specifically to its helicase domain, promoting SMARCA4 ATPase activity, independent of LKB1’s catalytic function (Marignani et al., 2001). ATPase experiments in vitro showed that in the presence of DNA, the ATPase activity of SMARCA4 is three-fold higher in the presence of LKB1 compared with the presence of SL26, a catalytically deficient mutant of LKB1 first identified in Peutz–Jeghers syndrome (Hemminki et al., 1998). This strongly suggests that the binding of SMARCA4 to LKB1 is necessary for the ATPase activity of SMARCA4 and that the ATP-dependent chromatin remodeling function of SMARCA4 is reliant on LKB1 (Marignani et al., 2001). We have also shown that the tumor suppressor function of SMARCA4 is partially dependent on LKB1. When SMARCA4 is expressed in SW13 cells, a SMARCA4-mutant cell line that expresses endogenous LKB1, SW13 cells undergo cell cycle arrest. The cell cycle arrest is suppressed when SMARCA4 is co-expressed with SL26 but not LKB1, indicating that the catalytic activity of LKB1 is required for SMARCA4-mediated cell cycle arrest (Marignani et al., 2001). In a later study, we discovered that catalytically deficient LKB1 mutants promote the expression of CYCD1 by directly binding to the CYCD1 promoter (Scott et al., 2007). Since SMARCA4 promotes the expression of CDKN1A (P21), leading to the inhibition of CDK4–CYCD1, which reduces the phosphorylation of retinoblastoma (RB), thus repressing E2F transcription factors and ultimately inhibiting the expression of G1/S factors (Kang et al., 2004). More recently, the loss of binding between LKB1 and SMARCA4 has been shown to result in PRC2-dependent transcriptional inhibition through increased H3K27me3. This, in turn, leads to upregulation of oxidative stress pathways and dysregulation of amino acid metabolism (Mével-Aliset et al., 2025).
Indirect observations implicate LKB1 and SMARCA4 in lipid metabolism, a well-characterized function of LKB1. The LKB1–AMPK pathway can regulate the expression of genes involved in fatty acid (FA) biosynthesis. Phosphorylation of sterol regulatory element-binding protein 1 (SREBP1), a transcriptional co-activator, by AMPK results in inhibition of SREBP1, leading to deregulation of FA biosynthesis genes (Seo et al., 2015; Li et al., 2011). SMARCA4 can bind to SREBP1, suggesting that SMARCA4 plays a role in regulating the expression of FA biosynthesis genes, indirectly linking SMARCA4 to LKB1 functions (Li et al., 2018a). In addition to SREBP1, the LKB1–AMPK signaling pathway is involved in regulating β-oxidation through peroxisome proliferator-activated receptor (PPAR) nuclear receptors. Here, LKB1–AMPK signaling activates PPARα in skeletal muscle cells, which leads to increased transcription of genes involved in β-oxidation (Juszczak et al., 2020; Lee et al., 2006). SWI/SNF complexes also play a role in the PPAR pathway as the SWI/SNF complex is recruited to PPARγ target genes (Salma et al., 2004). These results indirectly suggest that the interaction between LKB1 and SMARCA4 regulates lipid metabolism.
Finally, LKB1 and SMARCA4 both regulate transcription mediated by the ERα receptor (Nath-Sain and Marignani, 2009; DiRenzo et al., 2000). LKB1 binds to ERα independently of catalytic activity, although LKB1 catalytic activity is required for transcription of ERα target genes. Interestingly, ERα can recruit SMARCA4 to ERα target genes, where this recruitment is dependent on histone acetylation. Here, LKB1 and SMARCA4 may function together to regulate the expression of ERα target genes, with histone modifications playing an important role (Nath-Sain and Marignani, 2009; DiRenzo et al., 2000).
LKB1 has also been linked to chromatin organization independently of SMARCA4 (Kottakis et al., 2016; Pierce et al., 2021). One study examined the effect of Lkb1 on chromatin accessibility using the assay for transposase-accessible chromatin using sequencing (ATAC-seq) after restoring Lkb1 expression in Lkb1-mutant mouse lung tumor cells and found that more than 30,000 genomic loci exhibited changes in chromatin accessibility (Pierce et al., 2021). Furthermore, another study linked Lkb1 loss in mouse lung tumors to upregulation of SAM production, leading to global histone methylation and repression of retrotransposons (Kottakis et al., 2016).
The consequences of the Lkb1 chromatin-remodeling function have been observed in collaboration with Kras-mutant lung cancer in mouse models. It was observed that Lkb1 loss in Kras lung cancer mouse models exhibits loss of H3K27me3 and gain of H3K27ac and H3K4me3 histone modifications. These modifications correlated with the transition from the lung adenocarcinoma subtype to squamous cell carcinoma, indicating that this epigenetic change was facilitated through Lkb1 loss and suggesting that Lkb1 loss impacted differential subtypes of lung cancer progression mediated by histone modifications (Zhang et al., 2017).
Independent of Kras, Lkb1 functions to regulate chromatin dynamics in pancreatic β cells. Lkb1 loss was correlated with the dysregulation of Foxa, Mafa, and Rfx6 transcriptional regulatory elements. This highlights a specific role of Lkb1 in regulating chromatin dynamics across a variety of cells, independent of Kras and Smarca4 (Haberman et al., 2024).
These studies highlight the functional role of LKB1 in regulating chromatin remodeling via SMARCA4; however, the involvement of SMARCA4 in LKB1-dependent pathways and gene expression regulation has not been well characterized. To elucidate the functional relationship between LKB1 and SMARCA4 in lung cancer, we conducted single-cell RNA-seq (scRNA-seq) on four lung cancer cell lines with varying LKB1 and SMARCA4 mutation statuses (Carretero et al., 2004; Kim et al., 2021). Our analysis revealed that LKB1 and SMARCA4 function together in a linear pathway to modulate overlapping gene expression profiles in lung cancer cells. Notably, we observed that the expression profiles associated with LKB1 and SMARCA4 become apparent in late-stage human lung tumors. Our findings support a model wherein LKB1 functions dually, both as a metabolic regulator, acting through AMPK signaling, and as a transcriptional regulator, acting through the SMARCA4–SWI/SNF chromatin remodeling complex. Consequently, our model positions LKB1 as a crucial nexus bridging metabolic processes and gene expression regulation in lung cancer.
Results
LKB1 and SMARCA4 regulate expression of overlapping genes
To characterize the interaction between LKB1 and SMARCA4, we analyzed our scRNA-seq dataset from four well-characterized lung cancer cell lines with varying LKB1 and SMARCA4 expression statuses: Calu-3, H460, H1299, and A549 (Figure 1A) (Carretero et al., 2004; Kim et al., 2021). Calu-3 cells (WT) are wild-type for both LKB1 and SMARCA4; therefore, they are referred to as WT. H1299 cells (S) are SMARCA4-deficient, H460 cells (L) are LKB1-deficient, and A549 cells (LS) are LKB1- and SMARCA4-deficient (Figure 1A) (Blanco et al., 2009). None of these mutations produce functional protein (Carretero et al., 2004; Blanco et al., 2009; Medina et al., 2005).
FIGURE 1
To classify differentially expressed genes (DEGs), we compared log2 fold change (log2FC) of all 11,002 DEGs detected in our scRNA-seq dataset between LvsWT, SvsWT, and LSvsWT (Supplementary Table S1). We classified DEGs as LKB1-specific (L-DEGs), SMARCA4-specific (S-DEGs), or LKB1-SMARCA4-specific (LS-DEGs) based on the DEG expression profile. We reasoned that L-DEGs are detected only when LKB1 is mutated (LvsWT and LSvsWT, n = 911) and S-DEGs are detected only when SMARCA4 is mutated (SvsWT and LSvsWT, n = 814) (Figure 1B). LS-DEGs comprise the largest classification (n = 5,528, ∼50% of genes detected) and represent DEGs detected when either LKB1 or SMARCA4 is mutated (LvsWT, SvsWT, and LSvsWT) (Figure 1B). Therefore, our results suggest that LKB1 and SMARCA4 are important regulators of gene expression and may largely function together to regulate the expression of common genes.
LKB1 and SMARCA4 have considerable overlapping functions
We then performed gene set enrichment analysis (GSEA) on L-DEGs, S-DEGs, and LS-DEGs to identify overrepresented Gene Ontology (GO) terms within the biological process (BP) category (see Materials and methods) (Supplementary Table S2). L-DEGs were enriched in biological processes related to metabolic pathways, particularly in fatty acid and cholesterol metabolic processes, consistent with well-characterized LKB1 functions (Figure 1C) (Bourouh and Marignani, 2022; Scott et al., 2007; Hendricks et al., 2004; Gupta et al., 2020). S-DEGs were enriched in diverse biological processes, including protein transport, cell cycle, cell proliferation, and DNA repair (Figure 1C). L-DEGs and S-DEGs displayed overlapping enrichment in biological processes related to gene expression, depicted by overlapping functions in transcription, chromatin organization, and chromatin remodeling (Figure 1C).
LS-DEGs represented the overwhelming majority of classified DEGs. If these DEGs represented overlapping functions of LKB1 and SMARCA4, LS-DEGs would be enriched for similar BP terms as L-DEGs and S-DEGs. Enriched BP terms for LS-DEGs showed significant overlap with enriched biological processes of L-DEGs and S-DEGs, suggesting that LS-DEGs were enriched for genes that represent LKB1 and SMARCA4 functions (Figure 1C). In addition, LS-DEGs also exhibited unique enrichment in various biological processes implicated by LKB1 and SMARCA4 (Figure 1D). Many overrepresented BP terms corresponded to metabolic processes, but we also observed enrichment in translation, transcription, immunity, and cell cycle biological processes (Supplementary Figure S1A). Therefore, our workflow identified both unique and overlapping functions of LKB1 and SMARCA4, consistent with their previously characterized roles (Bourouh and Marignani, 2022; Scott et al., 2007; Hendricks et al., 2004).
LKB1- and SMARCA4-mutant human tumors exhibit similar expression profiles
The largely overlapping BP terms suggest that LKB1 and SMARCA4 function together to regulate gene expression of common pathways in cultured cells. To explore the translational consequences of this interaction in lung cancer, we asked whether our scRNA-seq results could be recapitulated using lung tumor transcriptional data from the cBioPortal cancer genomics database (Cerami et al., 2012; de Bruijn et al., 2023; Gao et al., 2013). KRAS-LKB1-mutant lung tumors exhibit distinct transcriptional and phenotypic properties compared with KRAS-TP53-mutant lung tumors (Skoulidis et al., 2015); however, the relationship between LKB1- and SMARCA4-mutant tumors has not been investigated. We examined bulk RNA-seq data of tumors from the cBioPortal database, selecting tumors with genetic backgrounds similar to the cell lines used in our scRNA-seq analysis: Calu-3, H460, H1299, and A549, with TP53-mutant tumors representing Calu-3 (Figure 1A; Supplementary Table S3). We first explored the overall transcriptional similarity between cell lines and primary lung tumors. We performed a correlation analysis to examine transcriptomic relationships among the four tumor types, namely, TP53, LKB1, SMARCA4, and LKB1/SMARCA4 double mutants, and compared the correlation matrices of these tumors with those of the cell lines used in our scRNA-seq data. We observed transcriptional similarity between cell lines and tumors mutant for LKB1 (H460 and A549) and between LKB1- and LKB1-SMARCA4-mutant tumors (Figure 2A). Moreover, SMARCA4- and TP53-mutant tumors expression profiles are correlated, such as the comparison between H1299 (SMARCA4-mutant) and Calu-3 cells (TP53-mutant) (Figure 2A). These results revealed transcriptional similarity between the cell lines used in our scRNA-seq and primary lung tumor bulk RNA-seq data from cBioPortal.
FIGURE 2
To investigate pathways implicated in LKB1- and SMARCA4-mutant tumors, we performed GSEA on DEGs from LKB1- and SMARCA4-mutant tumors, using the same pipeline as the GSEA used for scRNA-seq data (Supplementary Table S4). We then compared enriched biological processes between cell lines and tumors. Metabolic and cell cycle BP terms are common between LKB1- and SMARCA4-mutant lung tumors and cell line GSEA results, with genes that function in the lipid metabolic process also enriched (Figure 2B; Supplementary Figure S2A). We next compared the expression of enriched DEGs that comprise the lipid metabolic process identified in GSEA of tumors between our scRNA-seq and cBioPortal bulk RNA-seq datasets using a heatmap plot (Figures 2C,D; Supplementary Table S5). Most of the genes exhibit expression patterns characteristic of LS-DEGs (Figure 2C) and display similar expression levels in LvsWT, SvsWT, and LSvsWT comparisons. There is individual contribution from both LKB1 and SMARCA4 to the expression of genes linked to lipid metabolism, but the majority of DEGs are impacted by both LKB1 and SMARCA4 loss. We also applied our workflow to investigate the LKB1–SMARCA4 transcriptional phenotype, using WT tissue and KRAS-mutant tumors as controls. When comparing LKB1- and SMARCA4-mutant tumors to WT tissue, TP53-, and KRAS-mutant tumors, we observed an inverse relationship with respect to correlation scores of enriched GO terms in LKB1- and SMARCA4-mutant tumors compared to WT, KRAS-, and TP53-mutant tumors (Supplementary Table S6). Furthermore, enriched GO terms in LKB1- and SMARCA4-mutant tumors exhibit similar enrichment scores in all three comparisons (Supplementary Table S6). Therefore, these results suggest that there is some transcriptional similarity between LKB1- and SMARCA4-mutant tumors (Supplementary Table S6).
We next examined the expression profile of lipid metabolic process DEGs in TP53, LKB1, and SMARCA4 early-stage and late-stage lung tumors (Figure 2D). Early-stage TP53-, LKB1- and SMARCA4-mutant tumors predominantly exhibit downregulation of lipid metabolic process genes (Figure 2D). Interestingly, in late-stage tumors, we observed that LKB1- and SMARCA4-mutant tumors demonstrate strikingly similar expression profiles of genes involved in lipid metabolic processes, particularly upregulation of several genes (Figure 2D). Furthermore, this expression signature differs from that of late-stage TP53-mutant tumors and early-stage LKB1- and SMARCA4-mutant tumors, suggesting that LKB1- and SMARCA4-mutant tumors exhibit similar phenotypes that emerge only during late-stage disease progression (Figure 2D). To determine whether other pathways exhibit a similar expression signature, we examined DEGs that comprised the cell cycle BP term, and we observed a similar gene expression pattern between LKB1- and SMARCA4-mutant tumors, with cell cycle genes largely upregulated in late-stage tumors and in cell lines (Supplementary Figure S2B,C). These results indicate that LKB1 and SMARCA4 function cooperatively to regulate tumorigenesis in lung cancer.
SMARCA4 and LKB1 regulate gene expression in a linear pathway
The transcriptional similarity between our scRNA-seq data from cultured cells and bulk RNA-seq data from human lung tumors establishes a link between LKB1 and SMARCA4 in transcriptional regulation. We noticed that the majority of DEGs from scRNA-seq were classified as LS-DEGs, encompassing ∼50% of all genes detected in our scRNA analysis, indicating that LKB1 similarly plays an important role in regulating gene expression as SMARCA4 (Figure 1B). Furthermore, L-DEGs outnumbered S-DEGs, suggesting a greater requirement for LKB1 in regulating gene expression. Since LKB1 has been shown to bind and promote the ATPase activity of SMARCA4 and LKB1 localizes to both the nucleus and cytoplasm (Marignani et al., 2001; Nezu et al., 1999; Boudeau et al., 2003), we postulated that the transcriptional regulatory function of LKB1 could be mediated primarily through SMARCA4. To evaluate this, we compared the expression of LS-DEGs present in LvsWT with that in SvsWT using a scatter plot (Figure 3A). We observed that LS-DEGs were similarly expressed in LvsWT compared with SvsWT, exhibiting a strong correlation of expression (R = 0.69) (Figure 3A). Interestingly, our analysis revealed that only 454 genes out of 5,528 (8.2% of genes) were inversely regulated, suggesting that LKB1 and SMARCA4 primarily co-regulate LS-DEGs in a linear pathway (Supplementary Table S1).
FIGURE 3
To evaluate the consequences that LKB1 and SMARCA4 loss have on gene expression, we visualized the expression pattern of DEGs identified in our GSEA for lipid metabolic process, cell cycle, and chromatin organization in LvsWT, SvsWT, and LSvsWT using heatmap plots (Figure 3B; Supplementary Table S7). We observed that L-DEGs involved in the lipid metabolic process are generally downregulated and exhibit similar expression in LvsWT compared with LSvsWT. LS-DEGs that function in the lipid metabolic process displayed comparable differential expression when either LKB1 or SMARCA4 was mutated, with an equivalent number of genes up- and downregulated, suggesting that loss of either LKB1 or SMARCA4 produced similar transcriptional consequences on genes implicated in the lipid metabolic process (Figure 3B). Likewise, LKB1 or SMARCA4 mutations had similar effects on the expression of genes associated with cell cycle and chromatin organization (Figure 3B). Loss of either LKB1 or SMARCA4 caused upregulation of cell cycle genes, and the magnitude of upregulation was similar between LKB1 loss and SMARCA4 loss. We also noticed a similar profile with genes that function in chromatin organization (Figure 3B). These results highlight the phenotypic similarity resulting from the loss of SMARCA4 or LKB1 on gene expression across diverse biological processes and suggest that LKB1 and SMARCA4 function within a linear pathway.
The similar transcriptional profile observed in multiple biological processes between LKB1 and SMARCA4 loss suggested that LKB1 and SMARCA4 largely function together to regulate gene expression (Mével-Aliset et al., 2025). To validate our results, we applied an unbiased approach by examining the expression of genes within our scRNA-seq dataset that are annotated to lipid metabolic process, cell cycle, and chromatin organization in the Gene Ontology Resource (Ashburner et al., 2000; Aleksander et al., 2023). We reasoned that if LKB1 and SMARCA4 function in a linear pathway with respect to gene expression regulation, most annotated genes from these biological processes would be classified as LS-DEGs and display similar expression profiles in LvsWT, SvsWT, and LSvsWT, consistent with the DEGs identified in our GSEA (Figure 3B). We visualized the expression profiles of genes annotated to lipid metabolic process, cell cycle, and chromatin organization BP terms and observed that loss of LKB1 or SMARCA4 results in a similar transcriptional phenotype (Supplementary Figure S3A; Supplementary Table S7). The majority of DEGs classified as LS-DEGs, while LKB1 and SMARCA4 provided distinct contributions to gene expression in these biological processes (Supplementary Figures S3A,B). In the lipid metabolic process, L-DEGs outnumber S-DEGs (n = 78 vs. n = 45), suggesting a greater requirement for LKB1 in lipid metabolism. Similarly, there is a greater contribution from SMARCA4 in chromatin remodeling, with S-DEGs outnumbering L-DEGs (n = 55 vs. n = 40).
To further delve into the overlapping function of LKB1 and SMARCA4, we also examined the expression of genes annotated to the immune response BP term (Supplementary Figures S1B,C). LKB1 has been implicated in immune response as human LKB1-mutant tumors are characterized by low immunogenicity, and LKB1 loss is associated with high inflammatory characteristics (Koyama et al., 2016; Skoulidis et al., 2018). Tumors deficient in LKB1 display gene expression profiles indicative of a suppressive immune tumor microenvironment, with reduced infiltration of CD8+/CD4+ T cells (Gao et al., 2022). This phenotype can be recapitulated with loss of AMPK activity, linking LKB1 loss with diminished AMPK activity. This, coupled with the observation that LS-DEGs show strong enrichment for genes involved in immunity, suggests that SMARCA4 and LKB1 collaborate to regulate the expression of immune-related genes (Figure 1D; Supplementary Figures S1B,C). We found that L-DEGs enriched in immune function are downregulated (Supplementary Figure S1B), while S-DEGs and LS-DEGs show a similar number of up- and downregulated genes. Expression of most immune response genes is dependent on both LKB1 and SMARCA4 (n = 548 out of n = 985 DEGs, ∼55.6%), indicating that LKB1 and SMARCA4 may collaborate to regulate expression of immune response genes.
SMARCA4 regulates the expression of LKB1-associated genes
Our results indicate that LKB1 and SMARCA4 regulate the expression of genes involved in LKB1 pathways. If this is true, we predict that SMARCA4 would also regulate the expression of genes known to be associated with or be substrates of LKB1. Hence, we curated a list of LKB1-associated genes, particularly metabolic regulators, chromatin remodeling enzymes, epigenetic modifying enzymes, or genes differentially expressed in the LKB1-mutant context in multiple systems (mouse and human cell lines and tumors) (Kottakis et al., 2016; Pierce et al., 2021; Zhang et al., 2017; Timilshina et al., 2019). We examined the expression of LKB1-associated genes in LvsWT, SvsWT, and LSvsWT, and interestingly, we observed that the majority of DEGs are classified as LS-DEGs, indicating that SMARCA4 plays an important role in regulating transcription of LKB1-associated genes (Figure 4A; Supplementary Table S9). Furthermore, the expression of LKB1-associated genes was remarkably similar between LvsWT, SvsWT and LSvsWT, suggesting that LKB1 and SMARCA4 work together to regulate the expression of genes particularly implicated by LKB1 (Supplementary Table S9). These results suggest that LKB1 and SMARCA4 may function in a linear pathway to regulate gene expression (Figure 4B).
FIGURE 4
LKB1 functions with SMARCA4 to regulate lipid metabolism
During a state of energy imbalance, when the AMP: ATP ratio is high, LKB1 activates AMPK, promoting increased lipid catabolism and reducing lipid anabolism. This phenotype is observed in LKB1-mutant tumors, where LKB1 catalytic activity is lost; tumors exhibit increased lipid accumulation, which is associated with diminished AMPK activity (Bhatt et al., 2019). To determine the impact of SMARCA4 on lipid metabolism, we focused on genes that comprised the lipid metabolic process BP term from GSEA of LKB1-SMARCA4 double mutant tumors and examined expression in the scRNA-seq dataset (Figure 2B). In particular, we investigated how SMARCA4 influences LKB1-mediated regulation of AMPK, which is crucial for controlling lipid metabolism (Hawley et al., 2003; Shaw et al., 2004; Lizcano et al., 2004); DEGs were then analyzed using ExpressAnalyst (Ewald et al., 2024) to further classify genes into anabolic (FA biosynthesis) and catabolic (FA β-oxidation and PPAR) processes (Supplementary Table S8). We then constructed protein interaction network maps using NetworkAnalyst and the STRING database (Zhou et al., 2019; Szklarczyk et al., 2019) to visualize the pathway interactions (Figure 5). Genes involved in FA biosynthesis were upregulated when LKB1 or SMARCA4 was mutated, consistent with previous observations that LKB1 loss leads to the upregulation of FA biosynthesis (Faubert et al., 2014). We also observed that SREBF1, the gene coding for SREBP1, was upregulated and transcriptionally regulated by LKB1 and SMARCA4 (Supplementary Table S9). Conversely, genes involved in β-oxidation of FAs were downregulated, with LKB1 and SMARCA4 contributing to regulation of β-oxidation genes (Supplementary Table S8; Figure 5). We also observed that peroxisome proliferator-activated receptors (PPARα and PPARγ), nuclear receptors that bind to fatty acids and regulate gene expression involved in β-oxidation, were downregulated (Supplementary Table S8; Figure 5) (Lu et al., 2022). These results suggest that LKB1 and SMARCA4 cooperate to regulate FA biosynthesis and β-oxidation and that loss of SMARCA4 exhibits a gene expression pattern correlated with LKB1 loss.
FIGURE 5
Discussion
Our results suggest that tumor suppressor functions of LKB1 and SMARCA4 are interconnected and that they may cooperate to regulate gene expression involved in diverse biological processes in lung cancer. We found that loss of either LKB1 or SMARCA4 results in similar transcriptional profiles, with genes involved in metabolism, chromatin organization, cell cycle, and immune response showing similar gene expression profiles. Finally, we validated our scRNA-seq results using human lung tumor bulk RNA-seq data from the cBioPortal tumor database, implicating the LKB1–SMARCA4 interaction in lung tumorigenesis.
We propose a model for LKB1–SMARCA4-mediated transcriptional regulation based on our findings and suggest that LKB1 has two master regulatory functions: the first is as a master regulatory kinase regulating energy metabolism and the availability of cofactors used for chromatin remodeling, and the second is as a transcriptional regulator in complex with SMARCA4 in the nucleus to regulate chromatin remodeling and gene expression (Figure 6) (Mével-Aliset et al., 2025).
FIGURE 6
LKB1-mutant lung tumors exhibit increased lipid accumulation due to attenuation of AMPK signaling. In an ATP-deprived state (when the AMP: ATP ratio is high), LKB1 phosphorylates and activates AMPK, leading to inhibition of anabolic pathways and promotion of catabolic pathways (Bourouh and Marignani, 2022). When LKB1 is mutated, genes involved in lipid biosynthesis are upregulated, and those involved in β-oxidation are downregulated (Figure 5). FA synthesis is regulated, in part, by AMPK phosphorylation of SREBP1, which is a transcription factor regulating the expression of genes involved in FA synthesis (Seo et al., 2015; Li et al., 2011). We observe that genes involved in FA biosynthesis are also upregulated when SMARCA4 is lost, even in the presence of LKB1, suggesting that SMARCA4 is epistatic to LKB1 in regulating gene expression of FA biosynthesis genes (Figure 5). SMARCA4 has also been shown to play a role in the expression of genes involved in FA biosynthesis (Figure 5) (Li et al., 2018b). In hepatocytes, SREBP1 has been shown to bind the SMARCA4-dependent SWI/SNF complex, suggesting that FA biosynthesis gene expression is regulated by LKB1 and SMARCA4 within a linear pathway (Li et al., 2018b).
LKB1 has been shown to interact with SMARCA4 in the nucleus (Marignani et al., 2001), and loss of this interaction leads to global downregulation of gene expression (Mével-Aliset et al., 2025). Furthermore, our laboratory has shown that LKB1 binds to and promotes the ATPase-dependent chromatin remodeling function of SMARCA4 (Marignani et al., 2001). Therefore, we speculate that the striking correlated gene expression profiles that result when either LKB1 or SMARCA4 is mutated may be due to the absence of the LKB1–SMARCA4 physical interaction (Marignani et al., 2001). Individual functions of LKB1 and SMARCA4 in regulating gene expression are evident from L-DEGs and S-DEGs implicated in diverse pathways, but LS-DEGs consistently represent the predominant classification (Figure 4B; Supplementary Figure S3A). Our findings support the hypothesis that the primary mechanism by which LKB1 regulates gene expression is through the previously observed physical interaction with SMARCA4, whereby the LKB1–SMARCA4 interaction regulates the recruitment of the transcriptional and epigenetic regulators to gene regulatory elements mediated by binding with SMARCA4, promoting the ATPase activity and chromatin remodeling function of SMARCA4 (Marignani et al., 2001).
LKB1 metabolic regulation can also impact the chromatin landscape independently of SMARCA4 (Scott et al., 2007; Zeng and Berger, 2006; Bungard et al., 2010; Hou et al., 2011). Therefore, our observations may result from independent yet collaborative pathways that converge on epigenetic modification pathways, in addition to the previously observed LKB1–SMARCA4 interaction (Scott et al., 2007; Zeng and Berger, 2006; Bungard et al., 2010; Hou et al., 2011). Metabolic pathways produce intermediates such as ATP, acetyl-CoA, and SAM, which are used as cofactors for chromatin remodeling enzymes that regulate global changes in chromatin organization and consequently gene expression (Gut and Verdin, 2013; Kottakis et al., 2016; Lee et al., 2014). The metabolic state of cells influences the abundance of these cofactors. For example, lipid metabolism drastically impacts the levels of ATP and acetyl-CoA through regulation of FA biosynthesis and β-oxidation (Sutendra et al., 2014). Conversely, SAM production is dependent on the serine–glycine one-carbon cycle (Kottakis et al., 2016). LKB1 loss is associated with global chromatin remodeling and increased histone methylation through upregulation of SAM production (Kottakis et al., 2016; Pierce et al., 2021). Metabolic intermediates from LKB1-regulated pathways may be utilized by chromatin remodeling complexes to regulate epigenetic modifications. Moreover, chromatin remodeling complexes can interact with epigenetic enzymes, regulating histone modifications, and in this way, LKB1 can regulate epigenetic modifications indirectly from SMARCA4 (Nakatsuka et al., 2017; Januario et al., 2017). We suggest that LKB1 functions as a master regulator of gene expression through regulation of metabolic pathways, production of epigenetic cofactors, and modulation of chromatin remodeling via its physical interaction with SMARCA4. Since LKB1 can traverse the nuclear membrane (Nezu et al., 1999), where it can then be recruited to transcriptional machinery through direct binding within the nucleus, LKB1 could function as an integrator of the metabolic state of cells to responses in gene expression, impacting the production of ATP and other metabolic intermediates utilized by SMARCA4 and the chromatin remodeling machinery to regulate chromatin structure and, consequently, gene expression (Figure 6).
In summary, we show that LKB1 and SMARCA4 loss exhibit similar expression profiles in lung cancer cell lines and human lung tumors and that this interaction contributes to the regulation of diverse pathways in lung cancer. We propose a model in which the tumor suppressors LKB1 and SMARCA4 cooperate to prevent lung cancer through both direct and indirect mechanisms that converge to regulate metabolism and gene expression. Our results provide evidence that implicates LKB1 as a master regulator, serving as an interface between metabolic pathways and gene expression. We propose that this function of LKB1 is mediated, in part, through its physical interaction with SMARCA4, positioning LKB1 as a nexus between metabolism and gene expression.
Methods
Workflow to identify LKB1 (L), SMARCA4 (S), and LKB1–SMARCA4 (LS) DEGs
scRNA-seq data were mined and processed as in our previous study (Kim et al., 2021). All DEGs detected from scRNA-seq were identified, and the log2FC expression level for each gene was determined with Calu-3 cells serving as a control: H460 vs. Calu-3 (L), H1299 vs. Calu-3 (S), and A549 vs. Calu-3 (LS). Classification of DEGs was as follows: L-DEGs were detected in L and LS, not in S. S-DEGs were detected in S and LS, not in L. LS-DEGs were detected in L, S, and LS. Each classification represented a gene set for gene set enrichment analysis.
Data processing and gene set enrichment analysis
For gene set enrichment analysis, we used g:Profiler (Kolberg et al., 2023) to identify enriched GO terms at the category of BP using default settings. Results from upregulated, downregulated, and complete gene sets were combined, and redundant BP terms were filtered for the highest intersection size. Enriched BP terms were then compared with each other using a similarity matrix based on overlapping gene IDs. Hierarchical clustering was performed using enriched BP terms via hierarchical k-means clustering (Silhouette method) to identify unique GeneIDs for each cluster. The similarity matrices and hierarchical clustering were generated using RStudio. Unique gene IDs for each cluster were then analyzed using the ExpressAnalyst (Liu et al., 2023) network enrichment tool with the Panther database to generate enriched BP terms for dot plots, with only terms with >3 gene IDs (hits) chosen and only statistically significant term results collected (FDR-corrected p-value<0.05). Genes that are annotated to lipid metabolic process, chromatin organization, cell cycle, and immune response GO terms were obtained from the Gene Ontology Resource (Ashburner et al., 2000; Aleksander et al., 2023).
Plots
Venn diagrams were generated using the Ghent University Venn diagram generator web tool. Heatmap plots were generated using the Broad Institute Morpheus web tool. Volcano and dot plots were generated using ggplot2 in RStudio. Network maps were generated using NetworkAnalyst (Zhou et al., 2019) and processed in Adobe Illustrator.
Correlation analysis using scRNA-seq and bulk RNA-seq lung cancer datasets
We obtained a transcripts per million (TPM)-normalized mRNA expression matrix of 517 bulk RNA-seq datasets from patients with lung adenocarcinoma (LUAD) identified in The Cancer Genome Atlas (TCGA) and Broad Genome Data Analysis Center (GDAC) Firehose in the cBioPortal for the Cancer Genomics database (Cerami et al., 2012). For the correlation analysis, we selected 66 datasets based on the mutation status of three tumor suppressor genes, namely, LKB1, SMARCA4, and TP53, and divided them into four subgroups that are represented by genotypes similar to the cell lines used for scRNA-seq: Calu-3 represented by TP53-mutant tumors (Zhou et al., 2019), H460 represented by LKB1-mutant tumors (Gao et al., 2022), H1299 represented by SMARCA4-mutant tumors (Kottakis et al., 2016), and A549 represented by LKB1-SMARCA4 double mutant tumors (Gut and Verdin, 2013). A TPM-normalized mRNA expression matrix of the 66 subgroup datasets was used for hierarchical clustering analysis following a principal component analysis for dimension reduction. Finally, gene expression-based relationships among the four lung cancer patient subgroups (bulk RNA-seq) and the four lung cancer cell lines (scRNA-seq) were respectively identified using the Pearson-correlation method. In addition, we prepared two separate TPM-normalized mRNA expression matrices from bulk RNA-seq datasets of LUAD patients for 299 early-stage samples (237 WT, 7 LKB1-mutated, 3 SMARCA4-mutated, and 51 TP53-mutated) and 83 late-stage samples (61 WT, 2 LKB1-mutated, 2 SMARCA4-mutated, and 18 TP53-mutated). A DEA was performed on each of the two mRNA expression matrices to detect marker genes significantly up- or downregulated in mutated samples of each tumor suppressor gene at the two different lung cancer stages compared to WT samples. To select significant marker genes, we applied identical parameters that were used for the DEA of scRNA-seq datasets.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.
Ethics statement
Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.
Author contributions
MB: Conceptualization, Project administration, Writing – review and editing, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft. JK: Formal analysis, Methodology, Validation, Writing – review and editing, Data curation, Software. PM: Writing – review and editing, Conceptualization, Funding acquisition, Project administration, Resources, Supervision.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Cancer Research Society, The Canadian Cancer Society Diane Campbell designated research fund (grant #706202).
Acknowledgments
The authors would like to thank members of the Marignani laboratory for their support. The authors acknowledge that Dalhousie University is located on Mi’kma’ki, the ancestral and unceded territory of the Mi’kmaq People and recognize that all people in this region are Treaty People. The authors also acknowledge that African Nova Scotians are a distinct people whose histories, legacies, and contributions have enriched the region of Mi’kma’ki known as Nova Scotia for over 400 years.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2026.1685342/full#supplementary-material
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Summary
Keywords
gene expression, liver kinase B1, lung cancer, metabolism, single-cell RNA-seq, SWI/SNF-related matrix associated actin-dependent regulator of chromatin subfamily A, LKB1, SMARCA4
Citation
Bourouh M, Kim J and Marignani PA (2026) Tumor suppressors LKB1 and SMARCA4 functionally interact to regulate gene expression across diverse biological processes in lung cancer. Front. Cell Dev. Biol. 14:1685342. doi: 10.3389/fcell.2026.1685342
Received
13 August 2025
Revised
18 December 2025
Accepted
04 February 2026
Published
17 March 2026
Volume
14 - 2026
Edited by
Hasan Korkaya, Wayne State University, United States
Reviewed by
Chantal Thibert, Centre National de la Recherche Scientifique (CNRS), France
Shashanka Shekhar Sarkar, Indian Institute of Technology Ropar, India
Updates
Copyright
© 2026 Bourouh, Kim and Marignani.
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*Correspondence: Paola A. Marignani, paola.marignani@dal.ca
ORCID: Mohammed Bourouh, orcid.org/0000-0001-7657-2142; Jinhong Kim, orcid.org/0000-0002-1813-7393; Paola A. Marignani, orcid.org/0000-0001-8070-905X
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