A folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC.
Abstract
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.
Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization.
We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses.
SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response.
Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.
Jiaqi Tang, Yulun He, Yuqi Wang et al.· Frontiers in Immunology· 0 citations
Triple-negative breast cancer (TNBC) remains one of the most therapeutically resistant breast cancers and often displays pronounced immune heterogeneity. This study integrates multi-omics datasets to identify candidate biomarkers that may influence TNBC progression and modulate therapeutic response.
Transcriptomic, DNA methylation, microRNA (miRNA), and single nucleotide polymorphism (SNP) datasets from public repositories were analyzed using differential expression and correlation pipelines. Expression, mutation, and regulatory metrics were then integrated to prioritize key genes. Top candidates, including CCND2 and KIF1A, were preliminarily validated using qPCR and Kaplan—Meier plots. CIBERSORT was applied to examine tumor immune cell infiltration and to assess the immune-related expression of candidate genes.
Multi-omics integration highlighted CCND2 and KIF1A as highly ranked candidates associated with TNBC proliferation and cell cycle regulation. Literature and pathway analysis suggested potential involvement of these genes in key oncogenic and immune-related signaling pathways. Preliminary qPCR results supported their differential expression relative to normal breast tissue.
These findings nominate CCND2 and KIF1A as candidate biomarkers and potential therapeutic targets in TNBC. Continued pathway and functional validation will clarify how these genes contribute to tumor progression and may inform precision treatment strategies for TNBC.
n/a
Translational and Interventional Immunology (TI)
Roberto Aguilar, Victor Wang, Shane Deng· Journal of Immunology· 0 citations
BACKGROUND
Clear cell renal cell carcinoma (ccRCC) is characterized by intratumoral heterogeneity and a complex immune microenvironment, which contribute to disease progression and therapeutic resistance. Although ribosomal proteins have been implicated in tumor biology, the clinical relevance, microenvironmental impact, and biological role of ribosomal protein lateral stalk subunit P0 (RPLP0) in ccRCC remain unclear.
METHODS
We performed a comprehensive investigation integrating bulk transcriptomics (TCGA, GEO, ICGC, ArrayExpress), proteomics (CPTAC, HPA), single-cell RNA sequencing, and spatial transcriptomics to characterize RPLP0 expression, prognostic value, immunological relevance, and spatial distribution in ccRCC. The biological functions of RPLP0 were validated experimentally using clinical tissues (qRT-PCR, immunofluorescence) and ccRCC cell lines. Proliferation, migration, and invasion were assessed via CCK-8, wound healing, and Transwell assays following siRNA-mediated knockdown, with epithelial-mesenchymal transition (EMT) markers evaluated by Western blot.
RESULTS
RPLP0 was ubiquitously expressed in normal tissues but significantly upregulated in ccRCC at both the mRNA and protein levels, which was validated in clinical specimens and cell lines. High RPLP0 expression was associated with advanced tumor stage, metastasis, and poor clinical outcomes, and was identified as an independent prognostic factor. Functional enrichment analyses revealed that RPLP0 was closely linked to ribosome-related processes, DNA damage response, cell cycle regulation, and epithelial-mesenchymal transition. Immune analyses demonstrated that elevated RPLP0 expression correlated with M2 macrophages, as well as with enhanced expression of immune checkpoint and antigen presentation-related genes. Single-cell and spatial transcriptomic analyses revealed the preferential enrichment of RPLP0 in the malignant compartment, with spatial transcriptomics further demonstrating a positive correlation with macrophages. Drug sensitivity analyses based on pRRophetic predictions showed that RPLP0-high tumors were associated with higher estimated IC50 values for several targeted therapies, suggesting a potential association between elevated RPLP0 expression and predicted reduced drug sensitivity. Mechanistically, RPLP0 knockdown in vitro significantly inhibited ccRCC cell proliferation, migration, and invasion, and reversed EMT progression by downregulating mesenchymal markers and upregulating the epithelial marker.
CONCLUSIONS
These findings indicate that RPLP0 is aberrantly upregulated in ccRCC, promoting tumor progression, migration, and invasion by facilitating the EMT process. Furthermore, its close association with immune activation and spatial heterogeneity highlights its potential as a robust prognostic biomarker and a regulator of tumor-microenvironment interactions.
Bin Wang, Hongquan Liu, Yicheng Guo et al.· BMC Cancer· 0 citations
Background As the leading histological form of lung cancer, lung adenocarcinoma (LUAD) displays considerable intratumoral heterogeneity, frequent therapeutic resistance, and an unfavorable clinical outcome. Although rewiring of mitochondrial energy metabolism is known to drive tumor progression and treatment failure, a comprehensive understanding of its dual role in LUAD drug resistance and prognosis has yet to be established. Here, we built a robust predictive signature that integrates mitochondrial metabolism with drug resistance through multi-omics integration and machine learning frameworks. Methods We explored single-cell RNA sequencing profiles together with TCGA-LUAD transcriptomic data. Weighted gene co-expression network analysis (WGCNA) was applied to extract gene modules linked to mitochondrial-related genes (MRGs) and drug resistance-related genes (DRGs). From these, a five-gene (KLF4, KLF10, CAT, ALDOA, HLA-DRA) prognostic classifier, designated MDrisk, was formulated using LASSO-Cox regression and 101 combinations of 10 machine learning algorithms. Results The MDrisk model demonstrated reliable and precise prognostic capacity across training, internal test, and external GEO cohorts, serving as an independent risk factor. Elevated MDrisk scores correlated with an immunosuppressive microenvironment, higher tumor mutational burden, distinct copy-number alteration profiles, and decreased drug sensitivity in computational predictions. In vitro experiments further validated that silencing ALDOA—a central component of the signature—suppressed the proliferation, migration, and invasive capacity of LUAD cells. Conclusion The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD. It may offer a tool for further exploration of individualized therapy and sheds light on the interplay between metabolic dysregulation and antitumor immunity.
Chengyang Wu, Rui Jiao, Han Yan et al.· Frontiers in Medicine· 0 citations
BACKGROUND
Diffuse large B-cell lymphoma (DLBCL) presents a complex etiology and challenging diagnosis. This study aims to investigate potential pathogenic genes.
METHODS
We identified DLBCL risk genes (DRGs) through expression quantitative trait loci-Mendelian randomization (eQTL-MR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore biological functions. Single-cell RNA sequencing (scRNA-seq) data were analyzed to delineate the subcellular localization. Immune infiltration analyses examined the role of genes in the DLBCL immune microenvironment. Finally, drug sensitivity analyses were performed to predict potentially sensitive drugs.
RESULTS
Following eQTL-MR and prognostic analyses, we identified 15 genes associated with both the pathogenesis and prognosis of DLBCL. These genes were successfully integrated into a risk gene model, achieving an Area Under the Curve (AUC) of 0.787. GO and KEGG enrichment analyses of genes localized significant pathways, including NF-κB signal transduction. ScRNA-seq analysis suggested that DRGs may be linked to the immune microenvironment of DLBCL. Further immune infiltration analysis confirmed the pivotal role of immune infiltration in the malignant progression of DLBCL.
CONCLUSION
This study unveils 15 risk genes as potential pathogenic and therapeutic biomarkers for DLBCL. These findings provide novel insights and targets for understanding the pathogenesis, diagnosis, and treatment of DLBCL.
BACKGROUND
FMR1, a gene classically linked to Fragile X syndrome, functions as a multifunctional RNA-binding protein regulating RNA metabolism. Emerging evidence indicates its role in modulating cancer progression and tumor immunity, yet a comprehensive pan-cancer analysis to confirm its viability as a biomarker for cancer screening, prognosis, and precision therapy remains lacking.
METHODS
We first validated FMR1 expression in renal cell carcinoma cell lines via qRT-PCR and assessed its functional role using colony-formation, wound-healing, and Transwell assays. Subsequently, we performed pan-cancer analyses by mining multi-omic data from public databases including UCSC Xena, TCGA, GTEx, TIMER, and TISIDB, integrating expression, prognosis, immune infiltration, and genetic alteration data.
RESULTS
FMR1 was downregulated in renal cancer cell lines, and its overexpression inhibited cell proliferation and migration. Pan-cancer analysis revealed dysregulated FMR1 expression across multiple tumors. Low FMR1 expression correlated with poor prognosis in cancers like KIRC and SKCM. It also correlated with RNA methylation genes, immune/molecular subtypes, immune checkpoint genes, and immune cell infiltration. FMR1 amplification was the most frequent genetic alteration, and it demonstrated possibly improved predictive value compared to some traditional biomarkers.
CONCLUSION
FMR1 acts as a tumor suppressor in renal cancer and represents a promising pan-cancer biomarker for screening, prognostic evaluation, and immunotherapy response prediction, providing new insights for precision cancer therapy.
Huaqing Yan, Fan Xu, Xiaobo Cui et al.· Discover Oncology· 0 citations