Integrated metabolomic, transcriptomic, single-cell, and functional analyses identify SHMT2 as a biomarker of SGOC metabolic reprogramming, aggressive behavior, and reduced predicted immunotherapy responsiveness in lung adenocarcinoma.
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Research Article
* These authors contributed equally
Integrated metabolomic, transcriptomic, single-cell, and functional analyses identify SHMT2 as a biomarker of SGOC metabolic reprogramming, aggressive behavior, and reduced predicted immunotherapy responsiveness in lung adenocarcinoma.
Serine/glycine-one-carbon (SGOC) metabolism is frequently altered in lung adenocarcinoma (LUAD), but its relationship to tumor behavior and predicted immunotherapy responsiveness remains incompletely defined. Metabolomic profiling of 23 paired LUAD and adjacent normal lung tissues was performed using internal extractive electrospray ionization mass spectrometry. Transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) were analyzed to assess SHMT2 expression, prognosis, differentially expressed genes, and immune-related features. Predicted response to immune checkpoint blockade was evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA), and drug sensitivity was inferred using oncoPredict. Single-cell RNA-seq data were used to examine the cellular distribution of SHMT2. Experimental validation included quantitative reverse-transcription PCR (RT-qPCR), western blotting, Human Protein Atlas (HPA) immunohistochemistry, and short hairpin RNA (shRNA)-mediated SHMT2 knockdown followed by proliferation, wound-healing and colony formation assays. Metabolomic analysis identified glycine, serine, and threonine metabolism as a prominently altered pathway in LUAD. SHMT2 was upregulated in LUAD and associated with worse overall survival and adverse clinicopathological features. SHMT2-high tumors displayed enrichment of cell-cycle and SGOC-related transcriptional programs, lower immune and stromal scores, and reduced predicted responsiveness to immunotherapy. Single-cell analysis showed relative enrichment of SHMT2 expression in B cell populations. In vitro, SHMT2 was overexpressed in LUAD cells, and its knockdown suppressed proliferation, migration, and clonogenic growth. Collectively, SHMT2 is associated with SGOC metabolic reprogramming, aggressive tumor phenotypes, and an immune-disadvantaged state in LUAD, supporting its potential relevance as a biomarker; therapeutic targeting requires additional pharmacologic and in vivo validation.
LUAD remains a leading cause of cancer-related mortality worldwide, driven by pronounced molecular heterogeneity, an early propensity for metastasis, and the limited durability of current treatment strategies1,2. Although immune checkpoint inhibitors (ICIs) have reshaped the therapeutic landscape of LUAD, sustained clinical benefit is restricted to a subset of patients, and both primary and acquired resistance are frequently observed3,4. This marked inter-patient variability underscores an urgent need to define the biological determinants that sculpt the tumor immune microenvironment and govern immunotherapy responsiveness, while identifying actionable vulnerabilities that can be leveraged to improve patient stratification and treatment outcomes.
Metabolic reprogramming is a central hallmark of malignant progression5. Among metabolic programs, SGOC metabolism connects glycolytic inputs to amino-acid metabolism and nucleotide synthesis, supplying one-carbon units for purine/pyrimidine production and methyl-donor generation6. Beyond biomass production, SGOC metabolism contributes to redox homeostasis and interfaces with the methionine cycle to influence DNA, RNA, and histone methylation, positioning it at the nexus of proliferation and epigenetic regulation7,8. Serine hydroxymethyltransferase 2 (SHMT2), the mitochondrial isoform of serine hydroxymethyltransferase, converts serine to glycine while generating 5,10-methylene-tetrahydrofolate and constitutes a major entry point for mitochondrial one-carbon supply within the SGOC network9. SHMT2 is aberrantly upregulated in multiple malignancies and has been associated with enhanced proliferation, invasion, therapeutic resistance, and oxidative-stress adaptation10,11,12,13. SHMT2 also intersects with inflammatory signaling through the BRISC complex, although a direct SHMT2-driven immune mechanism in LUAD remains to be established14,15. Accordingly, whether SHMT2 marks a clinically meaningful metabolic phenotype in LUAD and how it relates to immune microenvironment features and predicted immunotherapy response require integrated evaluation.
The bidirectional interplay between tumor metabolism and antitumor immunity is central to contemporary cancer biology16. Tumor cells can compromise immune competence through competitive depletion of nutrients, accumulation of immunomodulatory metabolites, and metabolic-epigenetic coupling that rewires immune-cell differentiation and effector programs7,16,17. Heightened one-carbon metabolism may influence methylation-dependent programs and metabolic fitness in T cells, but the direction and magnitude of these effects depend on cell type and nutrient context17. Adaptive immune regulation also extends beyond T cells: B cells, antibody responses, antigen presentation, and tertiary lymphoid structures (TLS) have been associated with immunotherapy efficacy18. These observations motivate a multi-layered analysis that can place a metabolic candidate within bulk-tumor, single-cell, and functional contexts without assuming that cross-platform concordance alone proves causality.
Unlike a metabolomics-only design, which identifies altered metabolites without establishing their cellular source or phenotypic relevance, and unlike a bulk-transcriptomics-only design, which may infer pathway activity without directly observing metabolite alterations, the present workflow integrates paired-tissue metabolomics, bulk and single-cell transcriptomics, and in vitro functional assays. Concordance across these layers supports candidate prioritization and reduces dependence on any single analytical platform; however, the resulting evidence remains associative for immune regulation unless validated in immune co-culture or in vivo systems.
Paired-tissue metabolomics of LUAD and adjacent normal lung was first used to identify glycine-associated metabolic perturbations. TCGA transcriptomic and clinical data were then integrated to evaluate SHMT2, a mitochondrial SGOC enzyme that was supported by pathway relevance, tumor upregulation, prognostic association, and experimental tractability. Immune microenvironment profiling, single-cell mapping, and in vitro functional validation subsequently established a multi-layered, hypothesis-generating framework spanning metabolic alteration, enzyme dysregulation, cellular distribution, and tumor cell phenotype. From a practical perspective, this workflow requires fresh-frozen paired tissues. Metabolite identification by mass spectrometry is semiquantitative; candidate metabolites should be independently validated. Immunotherapy predictions (TIDE/TCIA) and drug sensitivity inferences (oncoPredict) are transcriptome-based computational estimates that do not substitute for clinical validation in real-world ICI-treated cohorts and should be interpreted as hypothesis-generating.
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This study was approved by the Institutional Review Board of the Second Affiliated Hospital of Nanchang University (CDEFYYLK 3-05). Written informed consent was obtained from all patients prior to sample collection, in accordance with the Declaration of Helsinki. Raw data were exported and analyzed using the software and web resources listed in the Table of Materials.
Clinical tissue collection
Paired tumor and matched distant normal lung tissues were collected from patients who underwent surgical resection between January and June 2024 and were pathologically diagnosed with invasive lung adenocarcinoma. Specimens were excised by the operating surgeon, processed within 5 min of resection, immediately snap-frozen in liquid nitrogen, and stored until analysis. De-identified clinicopathological characteristics are provided in Supplementary Table 1.
Internal extractive electrospray ionization mass spectrometry (iEESI-MS) for discriminating LUAD from normal lung tissues
Tissue specimens (1 mm3) were analyzed by iEESI-MS using a hybrid linear ion trap-electrostatic orbital-trap mass spectrometer in positive-ion mode. Methanol extraction solvent (100%) was delivered at 3.0 µL/min with a +5 kV bias. The iEESI-MS1 scan range was m/z 50–2,000. iEESI-MS2 used collision-induced dissociation with a normalized collision energy of 25–35%, an isolation window of m/z 2.0, and a dynamic exclusion time of 30 s. Daily external calibration according to the instrument-standard procedure maintained a mass error <5 ppm. After median normalization, log₂ transformation, and autoscaling, OPLS-DA was performed with 200 permutation tests. Metabolites were annotated using HMDB, and pathway enrichment was performed using KEGG annotations.
Mass spectrometry data processing and metabolite identification
Raw MS data were normalized by median normalization followed by log₂ transformation and autoscaling (mean centering and division by the standard deviation of each variable). Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify discriminatory m/z features. Features were considered differential when VIP > 1.0, |log₂(FC)| > 0.58, and p < 0.05. Differential features were subjected to MS2 analysis and annotated using the Human Metabolome Database. KEGG-based pathway enrichment was then used to identify altered pathways and prioritize pathway-relevant enzymes. The annotated differential metabolites are provided in Supplementary Table 2.
TCGA-based SHMT2 expression, survival, and clinicopathological association analyses
RNA-seq expression profiles and corresponding clinical annotations for TCGA-LUAD were downloaded from the Genomic Data Commons on December 18, 2025; tumor (n = 542) and normal (n = 59). Fragments per kilobase of transcript per million mapped reads (FPKM) values were transformed as log2(FPKM + 1). Only samples with complete survival information were included in prognostic analyses. Tumor and normal SHMT2 expression values were compared using the Wilcoxon rank-sum test. Patients were divided into SHMT2-high and SHMT2-low groups at the median expression value. Overall survival was evaluated by Kaplan-Meier analysis and a log-rank test. Associations with tumor-node-metastasis (TNM) stage, age, and sex were assessed using chi-square or Wilcoxon tests, as appropriate. Time-dependent receiver operating characteristic (ROC) curves were generated for 1-, 3-, and 5-year survival. All statistical tests were two-sided.
Differential expression analysis and functional enrichment (GO/KEGG)
Differentially expressed genes (DEGs) between SHMT2-high and SHMT2-low groups were identified using a false-discovery rate < 0.05 and |log₂(FC)| ≥ 1. Results were visualized as a volcano plot and a heatmap of the top 40 DEGs with row-wise z-score normalization. Gene Ontology (GO) and KEGG enrichment analyses used an adjusted p < 0.05. Human gene annotations were obtained from Ensembl in December 2025.
Immune infiltration, immunotherapy response prediction, and drug sensitivity inference
Tumor-microenvironment scores, including ImmuneScore, StromalScore, and ESTIMATEScore, were calculated using ESTIMATE. Immune cell composition was inferred using CIBERSORT with the LM22 reference set (downloaded December 5, 2025), 1,000 permutations, and retention of samples with deconvolution p < 0.05. Groupwise comparisons were performed between SHMT2-high and SHMT2-low tumors. Spearman correlations between SHMT2 and immune checkpoint-related genes were visualized as a correlation matrix. Immunophenoscores were obtained from TCIA, and TIDE was used for complementary prediction of immunotherapy responsiveness. Drug sensitivity was inferred using oncoPredict trained on the December 2023 GDSC release to estimate IC50 values. Group differences used Wilcoxon tests, and continuous associations used Spearman correlations.
Single-cell RNA-seq analysis of LUAD
Single-cell transcriptomic data19 were downloaded from the Gene Expression Omnibus on December 20, 2025, and analyzed. Cells with 200–6,000 detected features and < 10% mitochondrial transcripts were retained. Counts were normalized using LogNormalize with a scale factor of 10,000; the top 2,000 variable genes were selected, and all genes were scaled. Principal-component analysis used the first 20 components, UMAP used dimensions 1–20, and clustering used a resolution of 0.5. Cell types were annotated using the Human Primary Cell Atlas reference. Cell-type proportions were compared between tumor and normal samples, and Spearman correlation matrices were generated. SHMT2 distributions were examined across annotated cell types. Cell-cycle states were inferred using cyclone and visualized with ridge plots.
Cell culture
BEAS-2B, H1299, and A549 cells were cultured at 37 °C in a humidified incubator set to 5% CO2 and approximately 95% relative humidity, using the media and supplements specified in the Table of Materials.
Quantitative real-time reverse transcription PCR (RT-qPCR)
Total RNA was extracted using a phenol-based RNA extraction reagent. Extracted RNA was reverse-transcribed into cDNA using a reverse-transcription master mix. Reverse transcription was performed in a 20 µL system with genomic DNA removal at 42 °C for 2 min, reverse transcription at 50 °C for 15 min, and termination at 85 °C for 5 s. Quantitative PCR was performed with a DNA-binding dye-based qPCR master mix on a real-time PCR system using 95 °C for 3 min, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s, and a melt-curve step of 95 °C for 15 s, 60 °C for 60 s, and 95 °C for 15 s. All reactions were run in triplicate. Relative expression was calculated using the 2−ΔΔCt method with GAPDH as the housekeeping gene. Primer stock solutions were prepared at 10 µM in double-distilled water and stored at -20 °C in the dark. Primer sequences are provided in Supplementary Table 3.
Western blotting
Total cellular protein was extracted using radioimmunoprecipitation assay buffer on ice for 30 min, and centrifuged at 12,000 x g for 15 min at 4 °C. Proteins were separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to 0.22 µm polyvinylidene fluoride membranes. Membranes were blocked with 5% (w/v) non-fat milk in Tris-buffered saline with 0.1% Tween 20 (TBST) for 2 h at room temperature and incubated overnight at 4 °C with anti-SHMT2 (1:1,000) and anti-GAPDH (1:2,000) primary antibodies diluted in 5% non-fat milk‑TBST. After three washes of 8 min each with TBST (20 mM Tris-HCl, 150 mM NaCl, 0.1% Tween-20), membranes were incubated with horseradish-peroxidase-conjugated secondary antibodies (1:5,000) for 1 h at room temperature. Signals were developed using an enhanced chemiluminescence substrate prepared by mixing equal volumes of solutions A and B, incubated for 2 min at room temperature in the dark, and captured with a chemiluminescence imaging system.
HPA immunohistochemistry validation
SHMT2 protein expression patterns in normal lung tissue and LUAD were evaluated using the HPA. “SHMT2” was queried in HPA, and representative immunohistochemistry (IHC) images and associated pathology annotations were downloaded for comparison. To minimize bias due to antibody specificity, antibodies with higher validation levels in HPA were prioritized. Where available, IHC results from multiple antibodies were cross-referenced (HPA020543 and HPA020549).
SHMT2 knockdown in LUAD cells
To investigate the tumor cell-intrinsic role of SHMT2 in LUAD, SHMT2 knockdown was performed in H1299 and A549 cells using shRNA. The shRNA was carried by an H1 promoter-driven, puromycin-selectable lentiviral vector; packaging plasmids were co-transfected during virus production. sh-SHMT2 targeted the human SHMT2 coding region, whereas sh-NC was a scrambled negative control. Prepared viral stocks had a titer of ≥ 1 x 108 transducing units/mL and were stored at -80 °C. Cells were infected with the prepared sh-SHMT2 or sh-NC lentiviral particles described in the Table of Materials. After 48 h, knockdown efficiency was assessed by RT-qPCR and western blotting. Only cells with confirmed SHMT2 silencing were used for proliferation, migration, and colony-formation assays.
Cell proliferation assay
Cell proliferation was evaluated using the CCK-8 assay. Transfected H1299 and A549 cells were seeded into 96-well plates at 5 x 103 cells per well in sextuplicate. On days 1, 2, 3, 4, and 5, 10 µL of CCK-8 reagent was added to each well (10% of the culture-medium volume) and incubated for 2 h at 37 °C. Absorbance was measured at 450 nm and used as the cell-viability readout.
Wound-healing assay
Cell migration was assessed using a wound-healing assay. Transfected H1299 and A549 cells were seeded into 6-well plates and cultured to 90–95% confluence. A linear wound was generated using a sterile 200 µL pipette tip, and detached cells were removed by gentle washing with phosphate-buffered saline. Cells were then maintained in serum-free or low-serum medium, and the wound area was imaged at 0 h and 24 h under an inverted microscope. Wound closure was quantified using the image-analysis software listed in the Table of Materials and calculated as the percentage reduction relative to the initial wound area.
Colony formation assay
For colony-formation analysis, transfected H1299 and A549 cells were seeded into 6-well plates at 800 cells per well and cultured for approximately 10–14 days until visible colonies formed, with microscopic monitoring during culture. Colonies were fixed with 4% paraformaldehyde for 30 min at room temperature and stained with 0.1% crystal violet for 15 min at room temperature. Colonies containing more than 50 cells were counted under a light microscope.
Statistical analysis for in vitro experiments
All in vitro experiments were performed independently at least three times. Data are presented as the mean ± standard deviation. Two-group comparisons used Student's t-test, and p < 0.05 was considered statistically significant.
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Tissue metabolomics reveals extensive metabolic remodeling in LUAD with prominent enrichment of glycine-associated pathways
Paired tumor and adjacent normal lung tissues from 23 patients with LUAD were profiled by direct iEESI-MS; patient characteristics, and differential metabolite annotations are provided in Supplementary Table 1 and Supplementary Table 2. After log2 normalization, OPLS-DA showed clear separation between tumor and...
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This study used a sequential framework comprising metabolomic discovery, enzyme prioritization, transcriptomic interpretation, immune-context assessment, single-cell localization, and tumor cell functional validation. The analyses identify glycine-related and SGOC metabolic remodeling in LUAD and associate SHMT2 with adverse outcomes and lower computationally predicted immunotherapy benefit. This prioritization is stronger than reliance on a single omics layer but does not establish an immune-causal mechanism.
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The authors declare no competing interests.
The authors thank the patients and their families for their participation in this study. The authors also acknowledge the contributors to the TCGA, GEO, and HPA databases for making their data publicly available. This research was funded by the Key Research and Development Program of Jiangxi Province (grant no. 20223BBG71009) and the National Natural Science Foundation of China (grant nos. 81860379 and 82160410).
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| A549 cells | CCL-185 | ATCC | |
| anti-GAPDH antibody | M1310-2 | Huaan Biotechnology | |
| anti-SHMT2 antibody | D197021 | Sangon Biotech | |
| BEAS-2B cells | CRL-9609 | ATCC | |
| Cell Counting Kit-8 (CCK-8) | K1018 | APExBIO | |
| Database: TCGA / GDC | Accessed 2025-12-18 | National Cancer Institute (NCI) | |
| Database: GEO | Accessed 2025-12-20 | NCBI | |
| Database: HMDB | Accessed 2025-12-12 | Human Metabolome Database | |
| Database: KEGG | Accessed 2025-12-11 | Kanehisa Laboratories | |
| Database: Human Protein Atlas | Protein Atlas | ||
| Database: GDSC | 2023-12 release | Sanger Institute | |
| Database: Ensembl | 2025-12 | EMBL-EBI | |
| Database: TCIA | Accessed 2025-12-10 | The Cancer Immunome Atlas | |
| H1299 cells (NCI-H1299) | CRL-5803 | ATCC | |
| HRP-conjugated secondary antibody | D110087 | Sangon Biotech | |
| HiScript II Q Select RT SuperMix | KR116 | Tiangen Biotech | |
| LightCycler 480 System | 5015278001 | Roche | |
| Non-fat milk | Common laboratory reagent | BD (or as used) | |
| PVDF membrane, 0.22 µm | GVHP00010 (or common laboratory reagent) | MilliporeSigma (or as used) | |
| R package: pRoloc | 1.40.0 | Bioconductor | |
| R package: TCGAbiolinks | 2.26.0 | Bioconductor | |
| R package: survival | 3.4-0 | CRAN | |
| R package: survminer | 0.4.9 | CRAN | |
| R package: timeROC | 1.0.4 | CRAN | |
| R package: DESeq2 | 1.38.1 | Bioconductor | |
| R package: ggplot2 | 3.4.4 | CRAN | |
| R package: pheatmap | 1.0.12 | CRAN | |
| R package: clusterProfiler | 4.6.2 | Bioconductor | |
| R package: org.Hs.eg.db | 3.16.0 | Bioconductor | |
| R package: estimate | 1.0.13 | Bioconductor / GitHub | |
| R package: CIBERSORT | 1 | (standalone R script) | |
| R package: corrplot | 0.92 | CRAN | |
| R package: TIDE | 0.3.0 | R package | |
| R package: oncoPredict | 1.1.1 | CRAN / GitHub | |
| R package: GEOquery | 2.66.0 | Bioconductor | |
| R package: Seurat | 4.3.0 | CRAN | |
| R package: SingleR | 1.10.0 | Bioconductor | |
| R package: scran | 1.26.0 | Bioconductor | |
| SYBR Green qPCR Master Mix (Universal) | HY-K0501A | MedChemExpress (MCE) | |
| TRIzol Universal Reagent | DP424 | Tiangen Biotech |
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