Comprehensive multi-omics analysis reveals a fatty acid metabolism gene signature for prognostic assessment and immunotherapy in nasopharyngeal carcinoma, and identifies ABCC1 as a potential novel therapeutic target.
A novel fatty-acid-metabolism-related prognostic model is established for assessing the prognosis and potential immunotherapy response of NPC patients, as well as for characterizing the immunological features of the tumor microenvironment (TME).
Abstract
Background
Nasopharyngeal carcinoma (NPC) is a subtype of head and neck squamous cell carcinoma characterized by high recurrence and metastasis rates and poor prognosis. Although immune checkpoint inhibitors have emerged as a promising treatment strategy for recurrent/metastatic nasopharyngeal carcinoma (R/M NPC), only a few patients have benefitted significantly from them. Lipid metabolism reprogramming plays a crucial role in NPC progression and its interaction with the immune microenvironment. This study aims to establish a prognostic model for NPC based on lipid metabolism-related factors, further explore its association with tumor immunity, and investigate the potential for immunotherapy.
Methods
Collect GEO datasets(GSE53819 and GSE102349) for differential expression analysis and least absolute shrinkage and selection operator (LASSO) regression to identify prognostic genes and construct a fatty-acid-metabolism-related prognostic model. Survival analyses and time-dependent receiver operating characteristic (ROC) curves were applied to evaluate the predictive performance of the constructed prognostic markers. Furthermore, associations between the derived risk scores and immunological characteristics were systematically evaluated. Following the identification of ABCC1 as a key gene, its expression was validated through RT-qPCR, immunoblotting, and immunohistochemistry (IHC). Its functional role was further investigated using in vitro functional assays, multiplex immunohistochemistry (mIHC), co-culture experiments with CD8⁺ T cells, and in vivo xenograft tumor models in nude mice.
Results
Four genes (ABCC1, CD1D, CYP4B1, and DPEP2) were identified to constr uct a prognostic model associated with fatty acid metabolism. This model revealed significant distinctions in immune infiltration patterns between high-risk and low-risk groups. Specifically, the high-risk group displayed immunosuppressive characteristics, marked by reduced infiltration of CD8⁺T cells. Functional studies demonstrated that ABCC1 promoted NPC cell proliferation, migration, invasion, ROS accumulation, and lipid metabolic reprogramming. Mechanistically, ABCC1 was epigenetically upregulated by the histone acetyltransferase P300 and contributed to CD8⁺ T cell dysfunction and MEK/ERK pathway activation, thereby driving tumor progression.
Conclusion
In summary, we established a novel fatty-acid-metabolism-related prognostic model for assessing the prognosis and potential immunotherapy response of NPC patients, as well as for characterizing the immunological features of the tumor microenvironment (TME). Furthermore, ABCC1 emerged as a promising prognostic biomarker associated with immunotherapeutic responsiveness in NPC, warranting further validation.
CLINICAL TRIAL NUMBER
Not applicable.
Background/Objectives: Lipid metabolism plays a critical role in tumor progression and immunotherapy efficacy in oral squamous cell carcinoma (OSCC). However, clinically applicable lipid metabolism-based models for predicting prognosis and immunotherapy response remain limited. This study aimed to develop and validate such a model in OSCC. Methods: Using transcriptomic data of OSCC from the TCGA database and a set of lipid metabolism-related genes (LMRGs), we constructed an LMRG-based risk score model via LASSO regression to predict patient survival. This model was subsequently validated using the independent GEO dataset GSE41613. Results: Patients in the high-risk group exhibited significantly poorer overall survival than those in the low-risk group (training cohort: p < 0.0001; validation cohort: p = 0.0086). We also developed a nomogram incorporating the risk score and clinical characteristics, and the risk score was identified as an independent prognostic factor for OSCC patients. Furthermore, the risk score was significantly associated with the tumor immune microenvironment; samples with a lower risk score showed elevated CD8+ T cell infiltration and a better response to immunotherapy. Additionally, the high-risk group exhibited an increased tumor mutation burden and resistance to most chemotherapeutic agents. Notably, several drugs (e.g., obatoclax mesylate) showed significant efficacy in the high-risk group, representing promising therapeutic candidates. Conclusions: This study reveals that the LMRG signature could serve as a valuable tool for prognosis assessment, risk stratification, and therapy guidance in OSCC.
BACKGROUND
Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of folate metabolism in its pathogenesis and immune evasion remains unclear. This study aims to develop and validate a folate metabolism-related gene (FMRG) scoring system to stratify patients by prognostic risk and immune phenotypes, and to explore the functional role of key FMRGs in ccRCC progression.
METHODS
Using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), we developed a folate metabolism-related gene (FMRG) scoring system via integrative machine learning and validated it in an external cohort. We analyzed associations of the FMRG score with clinicopathological features, biological pathways, immune infiltration, therapeutic responsiveness, and drug sensitivity. Single-cell RNA sequencing and spatial transcriptomics mapped candidate gene expression, and in vitro experiments validated the functional role of NGF.
RESULTS
A ten-gene prognostic model based on the FMRG score stratified ccRCC patients into groups with distinct clinical outcomes, immune profiles, and therapeutic responses. NGF was upregulated in ccRCC, with heterogeneous spatial expression. Functional assays showed that NGF enhances proliferation, migration, and invasion.
CONCLUSIONS
This folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC. NGF is identified as a functional mediator of tumor progression, offering potential therapeutic targets and insights into metabolic-immune crosstalk.
Lung adenocarcinoma (LUAD) is a malignancy characterized by significant heterogeneity and variable clinical outcomes. RhoGTPases play a critical role in cancer progression; however, their precise effects on LUAD prognosis remain inadequately understood. This study aimed to construct and validate a RhoGTPase-driven prognostic signature for LUAD, alongside an examination of its biological relevance. Transcriptomic and single-cell RNA sequencing (scRNA-seq) data were obtained from public databases. Candidate genes were identified by intersecting differentially expressed genes (DEGs) with a set of 733 RhoGTPase-related genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. A prognostic model was developed and subsequently validated. Analyses including functional enrichment, immune infiltration, and drug sensitivity were conducted. Additionally, scRNA-seq analysis was employed to define key cellular populations and explore the dynamic expression of the prognostic genes. A total of 5,420 DEGs were identified, yielding 233 candidate genes upon intersection with the RhoGTPase-related gene set. Four genes (CCT6A, PLK1, DSG2, PCDH7) were designated as prognostic. The risk model effectively stratified patients into the high-risk group (HRG) and the low-risk group (LRG), revealing significant differences in overall survival rates. The risk score served as an independent prognostic indicator. Functional enrichment analysis showed that HRG was associated with cell cycle pathways, while LRG was predominantly enriched in immune-related pathways, with HRG exhibiting a more immunosuppressive microenvironment. Drug sensitivity analysis indicated potential responsiveness to specific agents, such as doramapimod, in HRG. ScRNA-seq analysis identified epithelial cells as the primary population of interest. Pseudotime analysis uncovered a developmental trajectory in which CCT6A, DSG2, and PCDH7 were associated with late-stage, differentiated epithelial cell states. A robust prognostic model based on four RhoGTPase-related prognostic genes (CCT6A, PLK1, DSG2, PCDH7) was established, effectively stratifying LUAD patients. This model provides valuable insights into the heterogeneity of LUAD and has the potential to inform personalized therapeutic strategies.
Tianchuan Li, Danhong Wu, A. Yang et al.· Scientific Reports· 0 citations
Objectives: Lung adenocarcinoma (LUAD) is molecularly heterogeneous, and the prognostic relevance of fatty acid metabolism (FAM) remains incompletely defined. We aimed to develop a concise FAM-associated prognostic signature and examine its associations with the immune microenvironment and candidate therapeutic vulnerabilities. Methods: TCGA-LUAD transcriptomic and survival data were integrated with MSigDB FAM gene sets. Univariate Cox and elastic-net Cox regression were used to derive a risk score. The locked formula was evaluated in a Japanese cohort (GSE31210) and a U.S. cohort (GSE72094). Immune-infiltration algorithms as well as TIDE, GDSC2, and CPTAC data were used for exploratory immune, drug sensitivity, and protein-level analyses. Results: The six-gene signature comprised CYP4B1, ACOXL, DPEP2, HPGDS, CA4, and ALOX15. High-risk patients had shorter overall survival in the TCGA and both external cohorts (GSE31210, log-rank p = 0.0039; GSE72094, p < 0.0001). The risk score remained independently prognostic after adjustment for age, sex, and clinical stage. High-risk tumours showed lower immune and stromal signals, greater immune exclusion, and a lower TIDE-predicted ICB response proportion. GDSC2 analyses and expression comparisons identified associations with predicted drug sensitivity and lipogenic target expression. Five detectable signature proteins were less abundant in tumours in CPTAC data. Conclusions: This signature stratified patients by prognosis in two geographically distinct external cohorts and generated testable metabolic and immune hypotheses. Prospective validation, assay standardisation, and functional studies are required before clinical use.
Q. Ma, Jianqing Liang, Jintian Li et al.· Genes· 0 citations
Background: Laryngeal squamous cell carcinoma (LSCC) is a highly aggressive malignancy with poor prognosis, particularly in advanced stages. While traditional treatments have improved survival rates, reliable biomarkers for prognosis remain limited. Methods: We analyzed RNA-seq data of LSCC patients from the Cancer Genome Atlas (TCGA) and validated the results using the Gene Expression Omnibus (GEO) dataset (GSE27020), clinical samples, and LSCC cell lines. Differentially expressed immune-related genes (DEIRGs) were identified using the “limma” R package. A prognostic signature was developed by integrating univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analysis. The signature’s predictive performance was validated using Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves. Results: A three-gene immune-related prognostic signature comprising TNFRSF4, PPARG, and PDGFA was established. In the training cohort, the model stratified patients into high- and low-risk groups with significantly different overall survival (HR = 5.81, 95% CI: 2.56–13.22, p < 0.001), with apparent 1-, 2-, and 3-year AUC values of 0.838, 0.895, and 0.947, respectively. Predictive performance was further evaluated in the TCGA testing cohort, the full TCGA cohort, and the GSE27020 cohort. Functional enrichment analysis revealed that the signature genes are involved in immune regulation and tumor progression. Conclusions: This study identified and validated a novel three-gene immune-related prognostic signature for LSCC, offering a practical tool for individualized prognosis and personalized treatment strategies. The signature provides insights into immune-related mechanisms in LSCC, presenting potential targets for therapeutic intervention.
Changding He, Wanqiu Peng, Yi Shi et al.· Journal of Clinical Medicine· 0 citations
Background Hepatocellular carcinoma (HCC) suffers from a poor prognosis largely due to its profound molecular heterogeneity and high frequency of relapse, challenges that are closely linked to the biology of cancer stem cells (CSCs) and a lack of effective stemness‐related prognostic biomarkers. Identifying CSC–related prognostic biomarkers and therapeutic targets is critical for improving patient outcomes. Methods We integrated differential expression analysis, weighted gene co‐expression network analysis (WGCNA), and CSC gene databases to identify core prognostic genes driven by stemness mechanisms. A robust prognostic model was developed and validated using multiple machine learning algorithms across TCGA and Gene Expression Omnibus (GEO) cohorts. The clinical relevance of the signature was assessed via receiver operating characteristic curve (ROC) curves, survival analysis, and association with tumor stage. Single‐cell RNA sequencing (scRNA‐seq) and computational drug repositioning coupled with molecular docking were employed to explore mechanistic insights and therapeutic candidates. Results Intersection analysis identified 12 core genes enriched in CSC–associated pathways. The optimal CoxBoost model demonstrated superior predictive performance for overall survival (OS) in internal, external, and meta‐analyses. The signature’s single‐sample GSEA (ssGSEA) score exhibited high diagnostic accuracy, correlated with advanced tumor stage, and enabled effective risk stratification. Single‐cell analysis revealed DARS2 enrichment in M1 macrophages, suggesting a role for CSCs in modulating the tumor immune microenvironment. The histone deacetylase (HDAC) inhibitor belinostat was prioritized via Drug Signature Database (DSigDB) screening and validated by molecular docking as a candidate for targeting the CSC–related signature. Conclusion This study establishes a novel CSC–associated gene signature for diagnosis and prognosis in HCC and nominates belinostat as a repurposing candidate for targeting stemness‐related pathways, offering a promising strategy for personalized therapy.
Yang Zi, Ying Zhang, Jun Wu et al.· Stem Cells International· 0 citations