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. 2025 Jun 2;26(1):205.
doi: 10.1186/s12931-025-03277-8.

Multi-omics unveils BCAA metabolism markers L-leucine and HMGCS1 as prognostic marker for immunotherapy efficacy in non-small cell lung cancer

Affiliations

Multi-omics unveils BCAA metabolism markers L-leucine and HMGCS1 as prognostic marker for immunotherapy efficacy in non-small cell lung cancer

Liyuan Dai et al. Respir Res. .

Abstract

Background: This study aims to identify branched-chain amino acid (BCAA) plasma metabolites and gene signatures that enhance prognostic assessments in non-small cell lung cancer (NSCLC) patients receiving immunotherapy.

Methods: Plasma metabolites were measured using untargeted UPLC-MS/MS (n = 94 and 40), with lymphocyte subset tests on 72 patients. BCAA-related subtypes were identified in NSCLC datasets (n = 274, 176, and 196). A prognostic risk model was developed and validated in NSCLC (n = 16, 27, 24, and 339), melanoma (n = 25), and pan-cancer ICIs cohorts (n = 330 and 81). Immune cell infiltration and prognostic signatures were validated using mIF (n = 21 in CHCAMS), scRNA-seq (n = 8 and 21), and spatial transcriptomics (n = 2 and 6). Cell and animal experiments involving HMGCS1 were conducted in a lung cancer model. Additionally, based on our previous findings that B cells with higher malignancy exhibited enhanced cholesterol homeostasis pathways in diffuse large B-cell lymphoma (DLBCL), we further analyzed the prognostic value of HMGCS1 using our spatial transcriptomics (n = 10) and immunohistochemistry (IHC, n = 39) in DLBCL.

Results: Our plasma metabolite analysis showed higher L-leucine levels were associated with better prognosis and had higher T cell counts and CD4+ T cell counts (P < 0.05). In GEO datasets, four NSCLC subtypes were identified, showing distinct prognostic outcomes and tumor microenvironment. Five BCAA-related genes (ACAT2, ALDH2, HMGCS1, MLYCD, and PPM1 K) formed a prognostic risk model for NSCLC, validated through Kaplan-Meier and ROC curve analyses in ICI cohorts (P < 0.05). HMGCS1 was an independent prognostic value in ICI cohorts and was negatively correlated with CD8+ T cell infiltration, while positively correlating with tumor severity, cholesterol homeostasis, and BCAA degradation across multiple platforms, including GEO datasets, our mIF cohort, public scRNA-seq, and spatial transcriptomics (P < 0.05). And our cell and animal function experiments found HMGCS1 overexpression promotes metabolic pathways and accelerates tumor growth, whereas HMGCS1 knockdown suppresses tumor progression in a mouse model treated with PD-1 monoclonal antibody (P < 0.05). In DLBCL, high HMGCS1 expression was associated with shorter overall survival, enriched in B cells and relapsed patients, correlated with cholesterol homeostasis and amino acid degradation pathways, and its prognostic value was further validated at the protein level by our IHC cohort (P < 0.05).

Conclusions: This study identifies a BCAA-related plasma metabolites and gene signature as effective prognostic markers for NSCLC patients receiving immunotherapy, with HMGCS1 as a key prognostic factor influencing tumor progression and immune response.

Keywords: Branched chain amino acids; Cholesterol homeostasis; Immunotherapy; Non-small cell lung cancer; Prognostic biomarker.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study has been approved by the Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (No. 23/262–4004 and No.22/486–3688). All experiments were executed according to the Declaration of Helsinki. The experimental protocol was according to the ethical guidelines of the Helsinki Declaration and was approved by the Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (No. 23/262–4004 and No.22/486–3688). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Differential analysis of plasma metabolites receiving immunotherapy and distinct BCAA metabolism patterns in NSCLC. A Heatmap of differentially expressed metabolites between the DCB and NDB groups in the validation cohort. B Functional enrichment analysis of the differentially expressed metabolites. C Comparison of L-leucine levels between the DCB and NDB groups, along with the AUC curve and Kaplan–Meier survival analysis in both the discovery and validation cohorts. D Consensus clustering matrix for k = 4 in GSE41271, principal component analysis of the four clusters, and Heatmap displaying the distribution of 27 prognosis-related BCAA metabolism regulators across the four clusters. E Kaplan–Meier curves illustrating overall survival according to the four clusters in GSE41271 and GSE42127. F Differences in tumor-infiltrating immune cells among the four clusters. (Abbreviation: BCAA: branched chain amino acid; NSCLC: non-small cell lung cancer; NDB: non-durable clinical benefit; DCB: durable clinical benefit. Mann-Whitney test was performed between clusters.)
Fig. 2
Fig. 2
Performance of HMGCS1 in predicting survival in immunotherapy cohorts. A, E Kaplan–Meier curves for PFS based on HMGCS1 in NSCLC and melanoma immunotherapy cohorts. B Comparison of HMGCS1 expression between NDB and DCB groups. C, F Correlations between HMGCS1 and cholesterol biosynthesis in NSCLC and melanoma immunotherapy cohorts. D Correlations between HMGCS1 and CD8+ T cell infiltration in NSCLC immunotherapy cohort. G Comparison of HMGCS1 in MPR and NMPR groups in neoadjuvant immunotherapy cohort (GSE207422). H Correlations between HMGCS1 and residual tumor (%) in neoadjuvant immunotherapy cohort. IJ Kaplan–Meier curves for OS based on HMGCS1 in pan-cancer immunotherapy cohorts pre-treatment and on-treatment (KM Plot database), and GSE218989 cohort. K Correlation between HMGCS1 and cholesterol biosynthesis pathway score in GSE41271, GSE42127, and GSE37745. L Kaplan–Meier analysis for PFS based on HMGCS1 intensity, comparison of CD8+ T cells intensity in HMGCS1_high and HMGCS1_low groups, and representative mIF staining of DAPI, CD8+ T cells, HMGCS1, and pan Cytokeratin in patient #1 (PFS = 40 days) and patient #2 (PFS = 748 days) in the mIF cohort (n = 21, 5X). (Abbreviation: PFS: progression free survival; NSCLC: non-small cell lung cancer; NDB: non-durable clinical benefit; DCB: durable clinical benefit; MPR: major pathological response; NMPR: non-major pathological response; OS: overall survival. Mann–Whitney test was performed between groups; LUSC: lung squamous cell carcinoma; LUAD: lung adenocarcinoma; mIF: multiple immunofluorescence. Mann–Whitney test was performed between groups.)
Fig. 3
Fig. 3
Distribution of HMGCS1 in NSCLC by single-cell RNA sequencing and spatial transcriptome. A UMAP of cell distribution and HMGCS1 expression in a LUSC sample (LUNG_FFPE_CytAssist_10x). BC Spatial plots of HMGCS1 expression, tumor cells, CD8+ T cells, hallmark cholesterol homeostasis score, and valine leucine and isoleucine degradation score in LUSC (LUNG_FFPE_CytAssist_10x) and LUAD (GSM5420751) samples. D UMAP and Vlnplot plots displaying HMGCS1 expression in NSCLC samples (SCAR database). E Comparison of HMGCS1 expression among AIS, MIA, and IAC samples in GSE189487. F Comparison of HMGCS1 protein expression in grade 2 and grade 3 LUAD in CPTAC database. G Comparison of CNV score in HMGCS1.+ malignant cells and HMGCS1- malignant cells in GSE131907. H GSEA of hallmark cholesterol homeostasis and valine leucine and isoleucine degradation pathways between HMGCS1 positive and HMGCS1 negative tumor cells in GSE131907. (Abbreviation: NSCLC: non-small cell lung cancer; UMAP: uniform manifold approximation and projection; LUSC: lung squamous cell carcinoma; LUAD: lung adenocarcinoma; AIS: adenocarcinoma in situ; MIA: minimally invasive adenocarcinoma; IAC: invasive adenocarcinoma. **** p < 0.001.)
Fig. 4
Fig. 4
RNA-seq and animal experiments validation of HMGCS1 knockdown and overexpression in LLC1 cell line. A RNA-seq and KEGG functional enrichment of HMGCS1 KD cells, WT_KD cells, HMGCS1 OE cells, and WT_OE cells. B qPCR analysis of HMGCS1 KD cells, WT_KD cells, HMGCS1 OE cells, and WT_OE cells. CD Tumor volume and body weight measurements of four animal groups treated with anti-PD-1 therapy. (Abbreviation: NSCLC: non-small cell lung cancer; KD: knock down, OE: over express.* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.)
Fig. 5
Fig. 5
Survival and distribution of HMGCS1 in DLBCL by spatial transcriptomics (n = 10) and immunohistochemistry (20X) (n = 39). A Kaplan–Meier curves of OS in HMGCS1_high and HMGCS1_low groups in GSE31212. BC Spatial plot and vlnplot illustrating the cell distribution of HMGCS1 in non_relapsed and relapsed groups. D Correlation between HMGCS1 expression and hallmark cholesterol homeostasis score, and valine leucine and isoleucine degradation score. E Representative IHC staining images showing HMGCS1 expression levels from patient 1 (PFS = 90 months) and patient 2 (PFS = 10.4 months) are depicted, along with kaplan–meier survival curves for PFS grouped by HMGCS1 expression levels. (Abbreviation: DLBCL: diffuse large B-cell lymphoma; IHC: immunohistochemistry; PFS: progression-free survival.)

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