NCAPG reprograms glycolytic and lipid metabolism by sustaining glycerophospholipid flux in small-cell lung cancer

Abstract

Small-cell lung cancer (SCLC) is characterized by rapid growth and a pronounced neuroendocrine phenotype, accompanied by marked metabolic plasticity. Although metabolic reprogramming is a hallmark of SCLC, the molecular mechanisms coordinating glycolytic and lipid metabolic pathways remain poorly defined. Untargeted metabolomic profiling and RNA sequencing were performed on 46 surgically resected SCLC tissues, 39 paired non-tumorous lung tissues, and corresponding serum samples to identify dysregulated metabolic pathways and key regulatory genes. NCAPG expression and function were evaluated using quantitative real-time PCR, Western blotting, Seahorse extracellular flux analysis, and xenograft mouse models. Cell proliferation, apoptosis, ATP production, and reactive oxygen species (ROS) levels were quantified using standard biochemical and flow cytometric assays. Metabolomic analysis identified 438 significantly altered metabolites in SCLC, including 215 shared between tumor tissues and serum samples, indicating systemic metabolic reprogramming. Pathway enrichment analysis revealed marked activation of glycolysis, glycerophospholipid metabolism, and oxidative phosphorylation. Transcriptomic profiling identified NCAPG as one of the most upregulated genes in SCLC (fold change = 4.7, FDR < 0.001), with elevated expression associated with advanced pathological stage and poor overall survival (HR = 1.47, P = 0.002). Functional depletion of NCAPG in H69 and H446 cells reduced cell viability by 45–60% and increased apoptosis approximately twofold. Seahorse analysis demonstrated a ~ 40% reduction in extracellular acidification rate accompanied by increased oxygen consumption, indicating a metabolic shift from glycolysis toward oxidative phosphorylation. In vivo, NCAPG knockdown suppressed xenograft tumor growth by 58% and significantly downregulated key glycolytic and glycerophospholipid enzymes, including HK2, LDHA, and CHPT1. NCAPG promotes metabolic reprogramming in SCLC by sustaining coupled glycolytic and glycerophospholipid flux, thereby supporting tumor energy production and biosynthetic demands. Targeting this NCAPG-mediated metabolic axis may represent a promising therapeutic strategy for small-cell lung cancer.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s11010-026-05616-9.

Introduction

Small-cell lung cancer (SCLC) is recognized as a particularly aggressive thoracic tumor with prominent neuroendocrine features and poor patient survival [1]. Although many individuals initially respond to chemotherapy or immunotherapy, these benefits are typically transient, and rapid disease recurrence is frequently observed, emphasizing the need to clarify the intrinsic molecular programs that drive persistent growth and metabolic resilience [2]. Emerging research increasingly supports the view that extensive metabolic remodeling is fundamental to SCLC biology, equipping tumor cells with the energetic and biosynthetic capacity required for their exceptionally rapid expansion [3, 4]. Recent studies have further refined our understanding of metabolic dependencies in SCLC. For example, PKM1-driven glycolysis has been shown to confer metabolic advantages that promote tumor cell-autonomous growth [5]. In addition, MYC-driven SCLC exhibits distinct metabolic features and vulnerabilities, including sensitivity to arginine deprivation [6]. Targeting de novo pyrimidine biosynthesis through DHODH inhibition has also been identified as an effective therapeutic strategy in SCLC [7], while disruption of NAD metabolism via NAMPT inhibition induces synthetic lethality in neuroendocrine carcinomas [8]. These findings highlight the critical role of metabolic rewiring in SCLC progression and therapeutic targeting.

Among the metabolic alterations in SCLC, glycolysis and lipid metabolism play central roles [9]. Enhanced glycolytic flux not only provides ATP but also generates intermediates for anabolic processes such as nucleotide, amino acid, and lipid biosynthesis [10]. Lipid remodeling, particularly glycerophospholipid synthesis, contributes to membrane biogenesis, signal transduction, and redox homeostasis [11]. The functional coupling between glycolytic and lipid metabolic pathways ensures a continuous supply of glycerol-3-phosphate and acetyl-CoA, thereby supporting rapid membrane turnover and tumor cell survival under metabolic stress [11–14]. However, the molecular mechanisms coordinating glycolytic–glycerophospholipid flux and integrating multiple metabolic pathways into a unified program in SCLC remain incompletely understood.

NCAPG (non-SMC condensin I complex subunit G) is a mitotic chromosome condensation factor implicated in various cancers, including hepatocellular carcinoma, breast cancer, and prostate cancer [15]. Beyond its canonical role in chromosomal segregation, NCAPG has been reported to promote cell proliferation and chemoresistance through pathways related to MYC, PI3K/AKT, and cell cycle regulation [16–18]. However, whether NCAPG contributes to metabolic control, particularly within the metabolically addicted landscape of SCLC, remains unclear. Considering the pronounced metabolic reliance of SCLC and accumulating evidence supporting the oncogenic activities of NCAPG, we postulated that NCAPG might function as a key mediator of metabolic balance in this malignancy.

In this study, we demonstrate that NCAPG reprograms glycolytic and lipid metabolism in SCLC by sustaining glycerophospholipid flux. Through integrative transcriptomic and metabolomic analyses, we identified NCAPG as a key mediator linking glycolysis and glycerophospholipid biosynthesis. Experiments demonstrated that NCAPG enhances glycolysis, increases lipid biosynthetic activity, and promotes tumor expansion in cell models and mouse xenografts. Our findings uncover a previously unrecognized metabolic role of NCAPG and provide new insights into targeting metabolic vulnerabilities in SCLC.

Materials and methods

Clinical samples and study design

We obtained 46 surgically resected SCLC specimens along with 39 paired non-tumorous lung tissues from patients treated at the China-Japan Union Hospital of Jilin University. All individuals were treatment-naïve, with no history of preoperative chemotherapy or radiotherapy. Samples were snap-frozen in liquid nitrogen and stored for subsequent analysis. Metabolomic profiling was conducted using both tissue pellets (TC vs. NC) and corresponding serum supernatants (TS vs. NS). In parallel, RNA sequencing (RNA-seq) data were obtained from paired tumor and normal tissues to explore transcriptomic alterations and to perform integrative metabolic pathway analysis. Approval for this study was granted by the Ethics Committee of China-Japan Union Hospital of Jilin University, and all procedures complied with the Declaration of Helsinki. Written informed consent was obtained from every participant before specimen collection. We present this article in accordance with the STROBE reporting checklist. The study protocol involving human participants was approved by the Clinical Ethics Committee of China-Japan Union Hospital of Jilin University (Approval No. 2024 − 312).

Cell culture

H69 and H446 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (CAS, Shanghai, China) and maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS; Gibco, Cat. No. 10099-141) and 1% penicillin–streptomycin (Gibco, Cat. No. 15140-122) at 37 °C in a humidified atmosphere with 5% CO₂. Lentiviral constructs encoding NCAPG-specific shRNAs (shNCAPG#1 and shNCAPG#2) and a non-targeting control (shNC) were obtained from GeneChem (Shanghai, China). The shRNA targeting sequence for NCAPG was 5′-GCUACAUCCUGGAGCGAAU-3′. All constructs were cloned into a GV112 lentiviral vector (hU6-MCS-CMV-Puromycin). After transduction, stable knockdown populations were generated by puromycin selection (2 µg/mL) for 7–10 days prior to downstream assays. Lentiviruses were produced in HEK293T cells using psPAX2 and pMD2.G packaging plasmids at a ratio of 4:3:1. Transfection was performed using standard transfection reagents according to the manufacturer’s protocol.

qRT–PCR

Total RNA was extracted with TRIzol reagent (Invitrogen, USA) and used for cDNA synthesis and qRT-PCR with standard SYBR-based procedures. Reactions were run under standard cycling conditions, and melting-curve analysis verified amplification specificity. GAPDH was used for normalization, and relative gene expression was determined by the 2^-ΔΔCt method using the following primers: NCAPG forward 5′-GAGGCTGCTGTCGATTAAGGA-3′, reverse 5′-AACTGTCTTATCATCCATCGTGC-3′; GAPDH forward 5′-ACCCAGAAGACTGTGGATGG-3′, reverse 5′-TCAG CTCAGGGATGACCTTG-3′. Each reaction was performed in triplicate, and melting curve analysis was conducted to confirm amplification specificity.

Western blot analysis

Cells were lysed in RIPA buffer, and equal protein amounts were analyzed by SDS–PAGE and transferred to PVDF membranes. After blocking, membranes were incubated with primary antibodies against NCAPG, Ki-67, HK2, LDHA, and CHPT1, followed by HRP-linked secondary antibodies. Detailed information on the antibodies, including manufacturer, catalog number, and dilution, is provided in Supplementary Table S1. Signals were detected by ECL and quantified with ImageJ using GAPDH as the control.

Cell proliferation and colony formation assays

Cell viability was measured using the CCK-8 assay (Dojindo, Kumamoto, Japan). Briefly, 2 × 10³ cells were seeded into 96-well plates, and absorbance at 450 nm was recorded at the indicated time points. For colony formation, 500 cells were plated in 6-well plates and cultured for 10–14 days, after which colonies were fixed, crystal-violet–stained, photographed, and counted using ImageJ.

Apoptosis and reactive oxygen species (ROS) detection

Apoptosis was measured using an Annexin V–FITC/PI kit (BD Biosciences, USA) and analyzed by flow cytometry. ROS levels were detected with the DCFH-DA probe (Beyotime, Shanghai, China) after a 30-min incubation at 37 °C and quantified by fluorescence microscopy and ImageJ.

Seahorse extracellular flux analysis and ATP measurement

Real-time extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were measured using a Seahorse XF96 Analyzer (Agilent Technologies, USA). Cells were plated in Seahorse plates (2 × 10⁴ cells/well) and analyzed using the Glycolysis Stress Test and Mito Stress Test kits according to the manufacturer’s instructions. Glucose consumption and lactate production were quantified using colorimetric assay kits (Solarbio, Beijing, China). Cellular ATP levels were determined using an ATP Assay Kit (Beyotime, Shanghai, China) and normalized to protein content. For metabolic rescue experiments, cells were treated with rosiglitazone (10 µM) or pyruvate (1 mM) for 24 h before measurement.

Xenograft mouse model

Five-week-old male BALB/c nude mice were obtained from the Laboratory Animal Center of Jilin University and maintained under specific pathogen-free (SPF) conditions. H69 cells (5 × 10⁶) stably expressing shNC or shNCAPG#1 were subcutaneously injected into the flanks of the mice (n = 6 per group). Tumor dimensions were measured every three days, and tumor volumes were calculated using the formula (length × width²)/2. After 18 days, mice were euthanized, and tumors were excised, weighed, and processed for ATP measurement and histological analysis. All animal experiments were approved by the Institutional Animal Care and Use Committee of China-Japan Union Hospital of Jilin University and were conducted in accordance with institutional guidelines. All animal experiments were approved by the Institutional Animal Care and Use Committee of China-Japan Union Hospital of Jilin University (Approval No. 2024 Extension No. 552).

Immunohistochemistry (IHC)

Paraffin-embedded xenograft sections were dewaxed, rehydrated, and subjected to citrate-based antigen retrieval. After blocking with 5% goat serum, the slides were incubated overnight at 4 °C with primary antibodies against Ki-67, HK2, LDHA, CHPT1, and NCAPG, followed by HRP-labeled secondary antibodies. Signals were developed using DAB (ZSGB-Bio), and nuclei were counterstained with hematoxylin. Staining intensity and positive areas were quantified using Image-Pro Plus. Detailed information on the antibodies used is provided in Supplementary Table S1.

Metabolomic and transcriptomic analysis

Metabolite analysis was conducted by LC-MS, with data acquired under both positive and negative ionization conditions. Raw LC-MS data were analyzed in Compound Discoverer 3.1, which was used for peak detection, alignment, and metabolite annotation based on public databases including HMDB, METLIN, and KEGG. Differential metabolites were defined based on VIP > 1 and P < 0.05, and visualized by PCA, volcano, and VIP analyses. Transcriptomic data included both in-house RNA-seq results and publicly available datasets from TCGA. Sequencing reads were aligned using HISAT2. Gene expression levels were normalized using TPM (transcripts per million). Differential gene expression was analyzed using DESeq2 with thresholds of adjusted p-value (FDR) < 0.05 and |log2 fold change| > 1. Pathway enrichment was performed through KEGG and joint pathway analysis (JPA) integrating metabolite and gene expression data using MetaboAnalyst 6.0.

Statistical analysis

All experiments were performed in triplicate or more. Data are presented as mean ± SD. Statistical analyses were conducted using GraphPad Prism 9.0 and R software. Differences between two experimental groups were assessed using an unpaired Student’s t-test, whereas comparisons involving more than two groups were examined by one-way ANOVA followed by Bonferroni post hoc analysis. Survival outcomes were analyzed with Kaplan–Meier curves, and group differences were determined using the log-rank test. A P value < 0.05 (two-tailed) was regarded as indicative of statistical significance.

Results

Global metabolomic profiling reveals distinct metabolic reprogramming in small-cell lung cancer

Untargeted metabolomic profiling was performed on tumor tissues (TC, n = 46) and adjacent normal controls (NC, n = 39), as well as paired serum supernatants (TS vs. NS), to comprehensively characterize metabolic alterations in SCLC (Fig. 1A). PCA demonstrated clear separation between SCLC and normal samples in both positive and negative ion modes (Fig. 1B, D), indicating globally distinct metabolic states. Volcano plots (Fig. 1C, E) revealed extensive differential metabolites, with many upregulated species related to glycolysis and lipid metabolism. The variable importance in projection (VIP) analysis further identified key metabolites contributing to this discrimination, including Rotenone, Cer 8:0;20/9:0, and 22-Hydroxy-2-hopen-1-one (Fig. 1F). These findings suggested widespread remodeling of central carbon and lipid metabolic networks in SCLC.

Fig. 1.

Study design and overview of metabolomic analysis in small cell lung cancer (SCLC). A Schematic overview of the study design. Metabolomic profiling was performed on tissue pellets (TC vs. NC; SCLC = 46, Normal = 39) and corresponding serum supernatants (TS vs. NS). An independent transcriptomic dataset (SCLC = 7, Normal = 8) was integrated for joint pathway analysis. B, C Principal component analysis (PCA) and volcano plot of metabolites detected in the positive ion mode, showing clear separation between SCLC (TC) and normal (NC) groups. D, E PCA and volcano plot of metabolites detected in the negative ion mode, also demonstrating distinct clustering and significant metabolic differences. F Variable importance in projection (VIP) plot identifying the most discriminative metabolites contributing to group separation, including Rotenone, Cer 8:0;20/9:0, and 22-Hydroxy-2-hopen-1-one

Across tissue and serum datasets, a total of 438 significantly altered metabolites were identified (Supplementary Figure S1), of which 215 overlapped between compartments, reflecting a systemic metabolic reprogramming pattern. The complete list of significantly altered metabolites is provided in Supplementary Table S2. Pathway enrichment analysis of these differential metabolites (Supplementary Table S3) highlighted marked activation of glycolysis/gluconeogenesis, glycerophospholipid metabolism, and oxidative phosphorylation, consistent with increased energetic and biosynthetic demands of tumor proliferation. Together, these results define a distinctive metabolic signature in SCLC characterized by accelerated glycolytic flux and enhanced lipid remodeling, providing the foundation for subsequent integrative transcriptomic–metabolomic analyses.

Transcriptomic profiling identifies NCAPG as a key oncogenic gene linked to poor prognosis

To further investigate the molecular determinants underlying metabolic remodeling in SCLC, transcriptomic profiling was performed using an independent RNA-seq dataset comprising tumor (T, n = 7) and normal lung (N, n = 8) tissues. PCA showed a clear separation between SCLC and normal samples, indicating distinct transcriptomic signatures (Fig. 2A). Volcano-plot analysis revealed 13,017 upregulated and 12,083 downregulated genes in tumor tissues (Fig. 2B). KEGG enrichment analysis of the upregulated genes highlighted pathways associated with cell cycle regulation, DNA replication, and MAPK signaling, which are hallmarks of hyperproliferative malignancies (Fig. 2C). Chord diagram mapping further demonstrated strong interconnections among genes driving these pathways (Fig. 2D). Among the top hub genes identified, NCAPG was markedly overexpressed in SCLC relative to normal controls (p < 0.001), together with KIF1A, CDT1, FBXO5, NXPH4, PDIK1L, RALGAPA1, ANLN, and LRR1 (Fig. 2E).

Fig. 2.

Transcriptomic profiling identifies NCAPG overexpression and its prognostic significance in small cell lung cancer (SCLC). A Principal component analysis (PCA) of RNA-seq data from normal (N) and SCLC tumor (T) tissues showing clear separation between groups. B Volcano plot displaying differentially expressed genes between SCLC and normal tissues (|log₂FC| > 1, FDR < 0.05). C KEGG pathway enrichment analysis of upregulated genes, highlighting cell cycle, DNA repair, and MAPK signaling pathways. D Chord diagram linking top enriched pathways with representative genes involved in proliferation and cell cycle regulation. E Expression levels of selected hub genes (KIF1A, CDT1, FBXO5, NCAPG, NXPH4, PDIK1L, RALGAPA1, ANLN, and LRR1) in SCLC and control tissues, showing significant upregulation in tumors. F Kaplan–Meier survival analysis showing that high NCAPG expression is associated with poorer overall survival in SCLC patients (HR = 1.47, P = 0.002). G, H Association between NCAPG expression and clinicopathological features, indicating higher NCAPG expression in advanced pathological stages (G) and in smokers (H)

Clinicopathologic correlation based on a larger SCLC cohort (Table 1) revealed that high NCAPG expression was significantly associated with advanced T stage (p = 0.046), male sex (p < 1 × 10–¹⁰), smoking history (p = 0.0001), and increased rates of death and disease-specific events (p = 0.00025 and 0.004, respectively). Survival analysis confirmed that patients with high NCAPG expression exhibited worse overall survival (HR = 1.47, 95% CI = 1.16–1.86, p = 0.002; Fig. 2F), and expression levels increased progressively with higher pathological stages (p = 0.008) and among smokers (p < 0.001; Fig. 2G–H). In Cox regression analysis (Table 2), NCAPG overexpression remained an independent prognostic factor for poor survival in SCLC (multivariate HR = 1.25, 95% CI = 1.00–1.57, p = 0.05). Collectively, these results identify NCAPG as a transcriptionally activated oncogene closely associated with SCLC progression and unfavorable prognosis.

Table 1.

Clinicopathological characteristics of patients with small cell lung cancer (SCLC) according to NCAPG expression levels

Characteristics	Low expression of NCAPG	High expression of NCAPG	statistic	pvalue
Total cases, n	520	521
Pathologic T, n (%)			3.996821655	0.04558615
T1&T2	448 (43.2%)	428 (41.2%)
T3&T4	69 (6.6%)	93 (9%)
Pathologic N, n (%)			0.097186493	0.755232438
N0&N1	444 (43.6%)	454 (44.6%)
N2&N3	58 (5.7%)	63 (6.2%)
Pathologic M, n (%)			0.000303765	0.98609449
M0	363 (44.9%)	414 (51.2%)
M1	15 (1.9%)	17 (2.1%)
Age, n (%)			0.513449332	0.473649467
<= 65	219 (21.6%)	229 (22.6%)
> 65	289 (28.5%)	276 (27.2%)
Gender, n (%)			41.85424249	9.83372E-11
Female	261 (25.1%)	159 (15.3%)
Male	259 (24.9%)	362 (34.8%)
Pathologic stage, n (%)			3.804136686	0.051126125
Stage I& Stage II	426 (41.4%)	402 (39.1%)
Stage III & Stage IV	88 (8.6%)	113 (11%)
Smoker,n (%)			14.76455452	0.000121804
No	65 (6.4%)	30 (3%)
Yes	439 (43.3%)	481 (47.4%)
Primary therapy outcome, n (%)			0.121554606	0.727354473
PD&SD	81 (10%)	76 (9.4%)
PR&CR	347 (42.8%)	306 (37.8%)
OS event, n (%)			13.37685119	0.000254749
Alive	345 (33.1%)	288 (27.7%)
Dead	175 (16.8%)	233 (22.4%)
DSS event, n (%)			8.270120108	0.004030284
No	397 (41.7%)	347 (36.4%)
Yes	88 (9.2%)	121 (12.7%)
PFI event, n (%)			0.56547594	0.452062279
No	339 (32.6%)	328 (31.5%)
Yes	181 (17.4%)	193 (18.5%)

Table 2.

Univariate and multivariate Cox regression analyses of prognostic factors in patients with small cell lung cancer (SCLC)

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
		HR(95% CI)	P value	HR(95% CI)	P value
Pathologic T	1023
T1&T2 vs. T3&T4		1.903 (1.491–2.430)	< 0.001	1.641 (1.172–2.296)	0.004
Pathologic N	1004
N0&N1 vs. N2&N3		1.813 (1.383–2.376)	< 0.001	1.948 (1.189–3.192)	0.008
Pathologic M	796
M0 vs. M1		2.287 (1.451–3.605)	< 0.001	2.093 (1.143–3.832)	0.017
Pathologic	1014
Stage I & Stage II vs. Stage III & Stage IV		2.028 (1.627–2.526)	< 0.001	0.955 (0.583–1.564)	0.855
Age	1010
<= 65 vs. > 65		1.264 (1.033–1.547)	0.023	1.326 (1.054–1.668)	0.016
Gender	1026
Female vs. Male		1.176 (0.959–1.443)	0.119
Smoker	1000
No vs. Yes		0.924 (0.646–1.322)	0.666
NCAPG	1026
Low vs. High		1.279 (1.049–1.558)	0.015	1.254 (1.000–1.571)	0.050

Integrated metabolomic-transcriptomic analysis uncovers glycolytic-glycerophospholipid coupling in SCLC

To further define the metabolic pathways underlying SCLC progression, we performed an integrated metabolomic and transcriptomic analysis combining tissue (TC vs. NC) and serum (TS vs. NS) datasets. Pathway enrichment of significantly altered metabolites revealed extensive perturbations in multiple energy- and lipid-related processes (Fig. 3A). Among the top 25 enriched pathways, the Warburg effect, glycolysis/gluconeogenesis, citrate cycle, amino sugar metabolism, and glycerophospholipid metabolism ranked most prominent, highlighting the coexistence of enhanced glycolytic flux and lipid biosynthesis in SCLC. Venn analysis demonstrated 215 overlapping differential metabolites between tissue and serum compartments (Fig. 3B; Supplementary Table S2), indicating that metabolic remodeling in SCLC is not confined to the tumor microenvironment but also reflected systemically in circulation. ROC curve analysis further confirmed that these shared metabolites displayed excellent discriminatory performance between tumor and normal samples (Fig. 3C).

Fig. 3.

Identification of differential metabolites and integrated pathway analysis in small cell lung cancer (SCLC). A Overview of enriched metabolite sets showing the top 25 metabolic pathways significantly altered between SCLC tissues (TC) and normal controls (NC). B Venn diagram illustrating the overlap of differential metabolites between tissue (TC vs. NC) and serum supernatant (TS vs. NS), revealing 215 shared metabolites. C Receiver operating characteristic (ROC) curves of top differential metabolites with strong discriminative ability between SCLC and normal groups. D Boxplots showing the relative abundance of the top 12 differential metabolites (e.g., 12′-Apocapsorbinol, Cer 8:0;20/26:6, Rotenone, etc.) in TC and NC groups. E Top 10 enriched metabolic pathways identified by joint pathway analysis (JPA) integrating metabolomic and transcriptomic datasets

Quantitative assessment of representative metabolites showed markedly increased levels of 12′-Apocapsorbinol, Rotenone, Cer 8:0;20/26:6, and Phosphatidylcholine lyso 20:4, together with a significant reduction in chlorogenic acid hemihydrate and related oxidative intermediates (Fig. 3D). JPA integrating metabolomic and transcriptomic profiles revealed ten significantly dysregulated pathways (Fig. 3E), with the greatest impact observed in glycerophospholipid metabolism, glycolysis/gluconeogenesis, and retinol metabolism. Consistently, transcriptional enrichment of metabolic enzymes involved in HK2-LDHA-PKM2–driven glycolysis and CHPT1-LPCAT1–mediated phospholipid synthesis was observed, suggesting that NCAPG-associated transcriptional activation may sustain a coupled glycolytic–lipid metabolic flux to support tumor growth. Collectively, these results define a metabolic crosstalk between glycolytic reprogramming and glycerophospholipid biosynthesis as a central feature of SCLC metabolism.

Silencing of NCAPG suppresses proliferation and promotes apoptosis in SCLC cells

To clarify the functional significance of NCAPG in SCLC, we established H69 and H446 cell lines with stable NCAPG suppression using two distinct shRNA constructs (shNCAPG#1 and shNCAPG#2). Both qRT-PCR and Western blotting verified an evident decline in NCAPG transcript and protein abundance relative to the shNC control (Fig. 4A, B). Loss of NCAPG profoundly impaired the growth capacity of SCLC cells. In viability assays, NCAPG-silenced cells showed a progressive reduction in metabolic activity over time (Fig. 4C). Consistently, colony-forming experiments revealed a marked decrease in the number and size of colonies generated by NCAPG-knockdown cells, indicating diminished long-term proliferative potential (Fig. 4D). Consistent with the suppression of cell growth, ATP production was markedly decreased in NCAPG-knockdown cells (Fig. 4E), indicating impaired cellular energy metabolism.

Fig. 4.

Knockdown of NCAPG suppresses proliferation and induces apoptosis in small-cell lung cancer cells. A qRT-PCR analysis showing that NCAPG mRNA expression was significantly reduced in H69 and H446 cells transduced with shNCAPG#1 or shNCAPG#2 compared with shNC. B Western blot confirming efficient knockdown of NCAPG protein in both cell lines. C Cell viability was assessed by CCK-8 assay at the indicated time points following NCAPG silencing. D Representative images and quantification of colony formation assays showing decreased clonogenic ability after NCAPG knockdown. E Relative ATP production was significantly reduced in H69 and H446 cells after NCAPG depletion. F Flow cytometric analysis of Annexin V-FITC/PI staining revealed increased apoptotic rates in shNCAPG#1 and shNCAPG#2 groups. G Reactive oxygen species (ROS) levels were visualized by DCFH-DA fluorescence staining; fluorescence intensity quantification indicated elevated ROS accumulation following NCAPG knockdown. Data are presented as mean ± SD from three independent experiments. P < 0.01, ** indicates statistically significant differences compared with shNC

Furthermore, NCAPG knockdown strongly promoted apoptosis in SCLC cells. Flow cytometric analysis of Annexin V-FITC/PI staining showed a significant increase in apoptotic populations following NCAPG silencing in both H69 and H446 cells (Fig. 4F). Concomitantly, intracellular ROS levels were markedly elevated in the shNCAPG#1 and shNCAPG#2 groups compared with controls, as visualized by DCFH-DA fluorescence staining (Fig. 4G). These results suggest that NCAPG is essential for maintaining SCLC cell viability, energy homeostasis, and redox balance. Loss of NCAPG disrupts cellular energy metabolism, leading to oxidative stress–induced apoptosis and growth inhibition.

NCAPG knockdown disrupts glycolytic flux and glycerophospholipid metabolism

To elucidate the metabolic mechanisms through which NCAPG supports SCLC cell growth, we performed Seahorse extracellular flux analysis to assess glycolytic and oxidative metabolic activities. Silencing of NCAPG markedly decreased the extracellular acidification rate (ECAR) and increased the oxygen consumption rate (OCR) in both H69 and H446 cells, indicating a metabolic shift from glycolysis toward oxidative phosphorylation (Fig. 5A). Correspondingly, the levels of glucose consumption and lactate production were significantly reduced after NCAPG knockdown, and these effects were partially reversed by treatment with the glycolysis activator rosiglitazone (Fig. 5B). Quantitative RT-PCR revealed that key glycolytic and lipid-metabolic genes, including HK2, PKM2, LDHA, CHPT1, LPCAT1, and ERK, were significantly downregulated in both cell lines following NCAPG silencing (Fig. 5C–D).

Fig. 5.

NCAPG knockdown impairs glycolytic flux and glycerophospholipid metabolism in small-cell lung cancer cells. A Seahorse XF analysis showing extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) in H69 and H446 cells. Silencing NCAPG markedly decreased ECAR and increased OCR, indicating reduced glycolytic activity and enhanced oxidative metabolism. B Measurement of glucose consumption and lactate production in NCAPG-deficient cells, with or without the glycolysis activator rosiglitazone. NCAPG knockdown reduced glycolytic output, which was partially rescued by rosiglitazone. C, D qRT–PCR analysis of glycolytic and glycerophospholipid metabolic genes (HK2, PKM2, LDHA, CHPT1, LPCAT1, and ERK) in H69 and H446 cells, respectively. E Western blot analysis confirming that NCAPG silencing suppressed the expression of glycolysis- and lipid-related enzymes (HK2, LDHA, PKM2, CHPT1, LPCAT1) and phosphorylation of ERK. F ATP production, cell survival rate, and mitochondrial activity (red/green fluorescence ratio) were significantly reduced after NCAPG knockdown, but partially restored by pyruvate supplementation. G DCFH-DA fluorescence staining showing ROS accumulation in H69 and H446 cells. Chlorogenic acid treatment attenuated the ROS elevation and partially rescued the oxidative stress induced by NCAPG knockdown. Data represent mean ± SD from three independent experiments. P < 0.01 indicates statistical significance compared with the shNC group

Consistent with the transcriptional changes, Western blot analysis confirmed the reduced expression of glycolytic enzymes (HK2, PKM2, LDHA) and glycerophospholipid biosynthetic enzymes (CHPT1, LPCAT1), along with diminished ERK phosphorylation in NCAPG-deficient cells (Fig. 5E). Functionally, NCAPG knockdown led to a significant reduction in ATP production, cell survival rate, and mitochondrial activity (red/green fluorescence ratio), all of which were partially restored by pyruvate supplementation (Fig. 5F). In addition, DCFH-DA staining revealed that the increase in intracellular ROS levels induced by NCAPG depletion was attenuated by chlorogenic acid treatment (Fig. 5G). Together, these results indicate that NCAPG maintains the glycolytic–glycerophospholipid metabolic flux necessary for energy homeostasis and redox balance in SCLC cells, while its silencing disrupts this coupling and triggers oxidative metabolic stress.

NCAPG promotes tumor growth and metabolic activity in vivo

To confirm the oncogenic and metabolic functions of NCAPG in vivo, xenograft experiments were conducted using H69 cells stably transduced with shNC or shNCAPG#1. Tumors derived from NCAPG-knockdown cells were markedly smaller and lighter than those in control mice, as evidenced by gross morphology, growth curves, and final tumor weight (Fig. 6A–C). Consistent with impaired energy metabolism observed in vitro, the ATP content of xenograft tissues was significantly reduced following NCAPG depletion, indicating a suppression of tumor bioenergetic capacity. Immunohistochemical analysis further demonstrated that NCAPG knockdown diminished the expression of key proliferation and metabolic markers, including Ki67, HK2, LDHA, and CHPT1, alongside the loss of NCAPG itself (Fig. 6D). Quantitative assessment confirmed a consistent decrease in staining intensity for all markers, supporting the notion that NCAPG sustains both proliferative signaling and glycolytic–lipid metabolic activity in SCLC. Collectively, these results establish that NCAPG promotes tumor growth and metabolic activation in vivo by maintaining glycolytic flux and glycerophospholipid biosynthesis, reinforcing its critical role as a metabolic driver in small-cell lung cancer.

Fig. 6.

NCAPG knockdown suppresses tumor growth and metabolic activity in vivo. A Representative images of xenograft tumors derived from H69 cells transduced with shNC or shNCAPG#1. B Tumor growth curves showing reduced tumor volume in the NCAPG knockdown group compared with the control. C Quantification of tumor weight and ATP levels in excised xenografts. NCAPG silencing markedly decreased both tumor mass and intracellular ATP content. D Representative immunohistochemistry (IHC) staining for CHPT1, HK2, Ki67, LDHA, and NCAPG in xenograft tumor tissues. Expression of proliferation marker Ki67, glycolytic enzymes (HK2, LDHA), and glycerophospholipid biosynthetic enzyme CHPT1 were all significantly downregulated following NCAPG knockdown. Quantification of IHC staining intensity is shown on the right. Data are presented as mean ± SD from three independent experiments. P < 0.01 indicates statistical significance compared with the shNC group

These in vivo findings are consistent with the metabolic and proliferative effects observed in both H69 and H446 cells in vitro.

Discussion

Small-cell lung cancer (SCLC) remains one of the most lethal malignancies, characterized by rapid proliferation, early metastasis, and inevitable relapse despite initial therapeutic responses [19]. The aggressiveness of SCLC is closely linked to its extraordinary metabolic flexibility, which enables tumor cells to adapt to hypoxia, nutrient fluctuations, and therapeutic stress [20, 21]. Consistent with this concept, several studies have identified key metabolic drivers in SCLC. PKM1-mediated metabolic reprogramming has been shown to enhance tumor growth capacity [5], while MYC-driven SCLC displays unique metabolic dependencies that can be therapeutically exploited [6]. Furthermore, inhibition of DHODH disrupts nucleotide biosynthesis and suppresses SCLC progression [7], and recent evidence indicates that targeting NAD metabolism through NAMPT inhibition can induce synthetic lethality in neuroendocrine tumors [8]. These findings collectively emphasize that metabolic vulnerabilities represent promising therapeutic opportunities in SCLC.

In this study, we systematically investigated the metabolic landscape of SCLC through integrated metabolomic and transcriptomic profiling and identified a distinct pattern of glycolytic activation coupled with glycerophospholipid biosynthesis. Among the upregulated genes, NCAPG emerged as a key oncogenic driver associated with poor clinical outcomes and advanced pathological features. Functional experiments revealed that NCAPG promotes tumor growth by sustaining glycolytic–lipid metabolic flux, providing new insights into the metabolic basis of SCLC progression.

NCAPG, originally recognized as a structural subunit of the condensin I complex, is essential for chromosome condensation and segregation during mitosis [15]. In recent years, increasing evidence has implicated NCAPG in tumorigenesis beyond its canonical role in chromatin organization [22–26]. Overexpression of NCAPG has been reported to accelerate cell cycle progression, enhance proliferation, and promote metastasis in multiple solid tumors [22, 24]. Our study extends these findings by demonstrating a novel metabolic role of NCAPG in SCLC. Transcriptomic analysis revealed that NCAPG expression correlates strongly with the activation of cell cycle, DNA replication, and MAPK pathways—molecular events that are tightly coupled to anabolic metabolism. Knockdown of NCAPG not only reduced proliferative capacity but also disrupted energy production, suggesting that its oncogenic activity depends on metabolic control.

Although NCAPG is primarily known for its role in chromosomal condensation, several mechanistic hypotheses may explain its involvement in metabolic regulation. First, NCAPG may influence metabolic gene expression through chromatin remodeling and transcriptional control. As a component of the condensin complex, NCAPG contributes to higher-order chromatin organization, which can affect the accessibility of transcription factors to metabolic gene loci [15]. This raises the possibility that NCAPG indirectly regulates key metabolic enzymes such as HK2, LDHA, and CHPT1 by modulating transcriptional programs.

Second, NCAPG may functionally interact with oncogenic signaling pathways that are tightly linked to metabolic reprogramming. Notably, MYC is a well-established regulator of glycolysis and nucleotide metabolism in SCLC [6], and previous studies have demonstrated that NCAPG expression correlates with MYC-related pathways. Therefore, NCAPG may act as a downstream effector or co-regulator within MYC-driven metabolic networks, thereby enhancing glycolytic flux and biosynthetic activity.

However, the precise mechanistic relationship between NCAPG and MYC-driven metabolic signaling has not yet been experimentally established and warrants further investigation.

Third, the metabolic effects of NCAPG may be coupled to its role in cell cycle progression. Rapidly proliferating tumor cells require coordinated upregulation of energy production and membrane biosynthesis [11]. By promoting cell cycle progression, NCAPG may indirectly drive metabolic demand, leading to increased glycolysis and glycerophospholipid synthesis to support biomass accumulation and membrane formation.

Taken together, these potential mechanisms suggest that NCAPG may act as an integrative regulator linking chromatin organization, proliferative signaling, and metabolic reprogramming. However, further studies, such as chromatin immunoprecipitation sequencing and metabolic flux analysis, will be required to delineate the direct molecular interactions underlying these effects.

A major finding of this study is that NCAPG regulates the coupling between glycolysis and glycerophospholipid metabolism in SCLC. Metabolomic analysis identified significant enrichment of glycolytic and lipid metabolic pathways, while joint pathway analysis further revealed that glycolysis/gluconeogenesis and glycerophospholipid metabolism are co-activated at both transcriptional and metabolite levels [27]. Mechanistically, NCAPG silencing suppressed key glycolytic enzymes, including HK2, PKM2, and LDHA, as well as lipid biosynthetic enzymes such as CHPT1 and LPCAT1. These changes resulted in decreased extracellular acidification rate, reduced glucose consumption and lactate production, and impaired ATP synthesis. The simultaneous decline in glycolytic and lipid biosynthetic capacity indicates that NCAPG coordinates the flow of carbon substrates between energy generation and membrane biosynthesis, ensuring metabolic sufficiency for tumor proliferation [9, 11, 28–30].

The functional consequences of NCAPG loss further underscore its importance in maintaining redox balance and mitochondrial function. Depletion of NCAPG increased intracellular ROS levels and induced apoptosis in both H69 and H446 cells, effects that were partially rescued by antioxidants. This suggests that NCAPG-mediated metabolic flux is critical for redox homeostasis [11, 31, 32]. In line with in vitro results, xenograft models demonstrated that NCAPG knockdown markedly reduced tumor volume, ATP levels, and the expression of Ki67, HK2, LDHA, and CHPT1. Together, these data provide strong in vivo evidence that NCAPG supports tumor energy metabolism and growth through glycolytic–lipid coupling, establishing it as a central regulator of metabolic reprogramming in SCLC.

The integration of metabolic and signaling pathways identified here provides a broader perspective on how SCLC cells achieve survival under extreme metabolic stress [33]. Enhanced glycolysis not only provides rapid ATP but also generates intermediates for nucleotide, amino acid, and lipid synthesis, while glycerophospholipid remodeling ensures membrane integrity and signaling plasticity [9, 11, 34, 35]. By promoting both processes simultaneously, NCAPG enables SCLC cells to sustain anabolic metabolism even under oxygen or nutrient limitation. Considering that metabolic plasticity underlies resistance to chemotherapy and immune evasion, targeting NCAPG or its downstream metabolic enzymes could represent a promising strategy to overcome treatment resistance in SCLC [33, 36, 37]. Furthermore, given the systemic metabolic alterations observed in serum metabolites, NCAPG-driven metabolic signatures may serve as potential biomarkers for early diagnosis or therapeutic response monitoring.

Importantly, our findings position NCAPG not merely as a structural mitotic factor, but as a potential coordinator of proliferation-associated metabolic programs in SCLC.

Despite these advances, several limitations should be acknowledged.

In addition, subtype-specific analysis of NCAPG expression could not be performed because molecular subtype annotation was not available in the present cohort. Given the biological and metabolic heterogeneity of SCLC subtypes (e.g., ASCL1, NEUROD1, POU2F3, and YAP1), future studies integrating subtype classification with metabolic profiling will be important to determine whether NCAPG-associated metabolic reprogramming differs across SCLC subgroups.

The present study utilized H69 cells for in vivo xenograft experiments, while consistent metabolic and functional effects of NCAPG silencing were observed in both H69 and H446 cells in vitro. Future studies incorporating additional in vivo SCLC models would further strengthen the generalizability of these findings.

Although integrated analyses support a strong link between NCAPG and metabolic remodeling, the direct transcriptional mechanisms by which NCAPG regulates metabolic enzymes remain to be elucidated. Future work should include chromatin immunoprecipitation sequencing and metabolite tracing to delineate NCAPG-dependent transcriptional and metabolic networks. In addition, clinical validation in larger patient cohorts will be necessary to confirm the prognostic and therapeutic significance of NCAPG. Nevertheless, our findings highlight a previously unrecognized metabolic dimension of NCAPG function and provide a conceptual framework for targeting metabolic vulnerabilities in small-cell lung cancer.

Conclusion

In summary, this study identifies NCAPG as a pivotal regulator of metabolic reprogramming in small-cell lung cancer. Through integrative metabolomic and transcriptomic analyses, we reveal that NCAPG coordinates glycolytic and glycerophospholipid metabolic flux to sustain energy production, biosynthesis, and redox balance. Functional and in vivo experiments confirm that NCAPG depletion suppresses tumor growth, impairs glycolytic–lipid coupling, and induces oxidative stress–mediated apoptosis. These findings uncover a previously unrecognized metabolic role of NCAPG that extends beyond its canonical function in chromosome condensation. By linking cell cycle progression with metabolic adaptation, NCAPG provides SCLC cells with a proliferative and energetic advantage. These findings suggest that disrupting the NCAPG-driven coupling of glycolysis and glycerophospholipid synthesis could serve as an effective therapeutic avenue for mitigating the metabolic vulnerabilities of SCLC and enhancing clinical responses.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 ^{(536.5KB, pdf)}

Supplementary Material 2 ^{(5.1MB, pdf)}

Abbreviations

ATP

Adenosine triphosphate

ECAR

Extracellular acidification rate

HK2

Hexokinase 2

LDHA

Lactate dehydrogenase A

NCAPG

Non-SMC condensin I complex subunit G

OXPHOS

Oxidative phosphorylation

ROS

Reactive oxygen species

SCLC

Small-cell lung cancer

Author contributions

Conceptualization: L.L.;Methodology: Y.S.;Investigation: Y.S., J.W.;Data curation: Y.S., J.W.;Formal analysis: Y.S.;Writing – original draft: Y.S.;Writing – review & editing: Y.S., J.W., L.L.;Supervision: L.L.;All authors have read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics Approval

This study was performed in accordance with the principles of the Declaration of Helsinki and was approved by the Clinical Ethics Committee of China-Japan Union Hospital of Jilin University (Approval No. 2024 − 312).

All animal experiments were approved by the Institutional Animal Care and Use Committee of China-Japan Union Hospital of Jilin University (Approval No. 2024 Extension No. 552).

Consent to Participate

Written informed consent was obtained from all individual participants included in the study.