Title：Integrative single-cell and bulk transcriptomes analyses reveals heterogeneity of serine-glycine-one-carbon metabolism with distinct prognoses and therapeutic vulnerabilities in HNSCC

Journal：Int J Oral Sci

Impact Factor：IF=14.9

Publication Date：June 2024

Corresponding Authors:Bin Cheng 、Xianyue Ren

At the forefront of modern medical research, scientists are using techniques like single-cell RNA sequencing and bulk transcriptome analysis to probe the mysteries of cancer. It’s akin to holding a high-powered microscope to listen to cancer’s “inner monologue,” one cell at a time. Today, let’s explore a new study that unveils the hidden role of a crucial metabolic pathway in head and neck squamous cell carcinoma (HNSCC)—the serine-glycine-one-carbon (SGOC) metabolism. What’s more, the study introduces a magical 4-SGOC gene prognostic signature, like a “map of the future” for clinicians. Let’s dive into this scientific adventure!

This article utilizes single-cell RNA sequencing (scRNA-seq) and bulk transcriptome analysis. scRNA-seq provides gene expression data at the single-cell level, allowing researchers to reveal intercellular heterogeneity and complex cellular ecosystems. Bulk transcriptome analysis, via high-throughput sequencing, captures overall gene expression patterns in tissue samples, helping identify disease-related metabolic pathways and gene expression signatures. The study reveals the heterogeneity of serine-glycine-one-carbon (SGOC) metabolism in HNSCC and explores its impact on prognosis and therapeutic response.

Research Background

HNSCC is a highly heterogeneous type of cancer, and its metabolic heterogeneity plays a key role in sustaining unlimited proliferation of cancer cells and shaping the tumor microenvironment. However, the current understanding of its metabolic vulnerabilities remains limited, which restricts the advancement of precision oncology. This study aims to promote the development of personalized therapeutic strategies by deciphering the metabolic heterogeneity of HNSCC.

Research Strategy

1. Obtain scRNA-seq data from HNSCC patients and distinguish malignant from non-malignant cells using the inferCNV method.

2. Quantify the expression of 114 metabolic pathways using the GSVA algorithm. Compare differences in metabolic pathways between malignant and non-malignant cells.

3. Identify genes associated with SGOC metabolism, and verify their prognostic value through univariate COX regression analysis and Kaplan–Meier survival analysis.

4. Construct an SGOC-based prognostic signature using LASSO COX regression analysis and validate its predictive performance in different patient cohorts.

5. Identify therapeutic vulnerabilities in high-risk patients and explore IMPDH1 as a potential therapeutic target.

 

 

Data Sources

· Single-cell RNA sequencing data: scRNA-seq data from 20 HNSCC patients (GSE181919).

· Bulk transcriptome data: Derived from the TCGA-HNSCC dataset and multiple GEO datasets (GSE41613, GSE65858, GSE42743, GSE30784, GSE25099, GSE37991, and GSE31056).

Research Results

1. SGOC upregulation is a key metabolic feature of HNSCC

Analysis of scRNA-seq data from HNSCC revealed that the serine-glycine-one-carbon (SGOC) metabolic network and its branches—including serine-glycine synthesis, folate and methionine cycles, and purine nucleotide synthesis—were significantly upregulated in malignant cells. Kaplan–Meier survival analysis further indicated that high expression of the SGOC network was associated with poor prognosis, underscoring the importance of SGOC metabolism in HNSCC.

 

 

2、High SGOC metabolism promotes aggressive phenotypes in HNSCC

Further classification of malignant cells revealed five subpopulations with varying SGOC scores and transcriptional characteristics. Subpopulations with high SGOC scores were positively correlated with cell cycle features, suggesting that elevated SGOC metabolism is linked to increased cellular aggressiveness. Kaplan–Meier survival analysis showed that patients with high SGOC scores had shorter overall survival (OS), validating the role of SGOC metabolism in promoting HNSCC aggressiveness.

 

 

3、A novel 4-SGOC gene prognostic signature demonstrates strong predictive efficiency in HNSCC

A 4-gene SGOC-based prognostic signature was developed using LASSO COX regression analysis and validated across multiple patient cohorts. Results showed that high-risk patients had significantly shorter OS compared to low-risk patients. The risk score was positively correlated with SGOC scores, cell cycle activity, and cancer markers, further confirming the predictive power of the 4-SGOC gene signature.

 

 

4、High SGOC metabolism is associated with low immune cell infiltration and poor immunotherapy response

The study found that HNSCC patients with high SGOC scores exhibited lower levels of immune cell infiltration, which may lead to reduced responsiveness to immunotherapy. This suggests that SGOC metabolism not only influences tumor metabolic characteristics but may also modulate the immune status of the tumor microenvironment, thereby impacting immunotherapeutic outcomes.

 

 

5、IMPDH1 is an SGOC metabolic target associated with malignancy in HNSCC cells

Further analysis of high-SGOC score cells identified IMPDH1 as a critical metabolic target. IMPDH1 plays a pivotal role in the SGOC metabolic network, and its high expression is closely associated with malignant traits in HNSCC cells.

 

 

6、Inhibiting IMPDH1 suppresses cancer progression by triggering GTP depletion–induced nucleolar stress

Experimental results demonstrated that inhibiting IMPDH1 activity induced nucleolar stress through GTP depletion, thereby suppressing the progression of HNSCC cells. This provides strong evidence supporting IMPDH1 as a therapeutic target, with potential in halting tumor cell proliferation and invasion.

 

 

7、IMPDH inhibitors suppress HNSCC tumor growth in vivo

To assess the in vivo efficacy of IMPDH1 inhibition, researchers conducted experiments using IMPDH inhibitors in mouse models. Results showed that IMPDH inhibitors significantly suppressed HNSCC tumor growth, further validating the effectiveness and feasibility of targeting IMPDH1 as a treatment strategy.

 

 

Research Summary

This study, through integrated analysis of single-cell and bulk transcriptomic data from HNSCC, highlights the crucial role of SGOC metabolism in prognosis and treatment response. It demonstrates that high SGOC metabolic activity is associated with increased tumor aggressiveness, reduced immune cell infiltration, and poor response to immunotherapy.

The 4-gene SGOC-based prognostic signature shows strong predictive performance and holds potential for clinical patient stratification and development of personalized treatment strategies.

Moreover, IMPDH1, identified as a key target within the SGOC metabolic network, shows promise for therapeutic application in HNSCC. In vivo experiments confirm that IMPDH inhibitors significantly suppress tumor growth, providing important preclinical evidence for future clinical use.