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A novel immune-related gene signature stratifies prognosis and characterizes the tumor immune microenvironment in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) exhibits heterogeneous responses to immunotherapy, primarily determined by the tumor immune microenvironment (TME). Accordingly, there is an urgent need for reliable biomarkers that can effectively distinguish between immune "hot" and "cold" tumors and predict prognosis and immunotherapy response. METHODS: We integrated transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts to identify immune-related genes (IRGs) associated with TME phenotypes. Unsupervised clustering was used to define immune-hot and immune-cold subtypes. A six-gene prognostic signature (PYGL, SFRP1, FGD3, OLR1, DUSP9, and MASP1) was developed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, which was validated across four cohorts and evaluated for its association with immune infiltration, tumor mutation burden (TMB), and immunotherapy response. The functional role of PYGL was assessed via siRNA knockdown in HNSCC cell lines. RESULTS: The signature demonstrated good performance in stratifying patients into high- and low-risk groups, characterized by distinct survival, immune landscapes, and genomic mutation profiles. High-risk patients showed an immunosuppressive TME, higher TMB, and poorer response to immune checkpoint blockade. PYGL knockdown suppressed proliferation, migration, and clonogenicity. Transcriptomic analysis revealed downregulation of MYC/E2F targets and EMT pathways, alongside concurrent activation of interferon and immune response programs. CONCLUSION: This IRG-based signature offers a clinically translatable tool for prognostication and immunotherapy selection in HNSCC. PYGL represents a key oncogenic driver and potential therapeutic target linking cellular metabolism to tumor progression and immune evasion.
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Multi-omics analysis of macrophage-associated receptor and ligand reveals a strong prognostic signature and subtypes in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a significant contributor to morbidity and mortality worldwide. The interaction between receptors and ligands is the primary mode of intercellular signaling and plays a vital role in the progression of HCC. This study aimed to identify the macrophage-related receptor ligand marker genes associated with HCC and further explored the molecular immune mechanisms attributed to altered biomarkers. Single-cell RNA sequencing data containing primary and recurrent samples were downloaded from the China National GeneBank. Cell types were first identified to explore differences between immune cells from different sample sources. CellChat analysis was used to infer and analyze intercellular communication networks quantitatively. Three molecular subtypes were constructed based on the screened twenty macrophage-associated receptor ligand genes. Bulk RNA-Seq data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the screening, the minor absolute shrinkage and selection operator (LASSO) regression model was employed to identify key markers. After collecting peripheral blood and clinical information from patients, an enzyme-linked immunosorbent assay (ELISA) was used to detect the correlation between key markers and IL-10, one of the macrophage markers. After developing a new HCC risk adjustment model and conducting analysis, it was found that there were significant differences in immune status and gene mutations between the high-risk and low-risk groups of patients based on macrophage-associated receptor and ligand genes. This study identified SPP1, ANGPT2, and NCL as key biological targets for HCC. The drug-gene interaction network analysis identified wortmannin, ribavirin, and tarnafloxin as potential therapeutic drugs for the three key markers. In a clinical cohort study, patients with immune checkpoint inhibitor (ICI) resistance had significantly higher expression levels of OPN, ANGPT2, NCL, and IL-10 than patients with ICI-responsiveness. These three key markers were positively correlated with the expression level of IL-10. The signature based on macrophage-associated receptor and ligand genes can accurately predict the prognosis of patients with HCC and the sensitivity to immunotherapy. These results may help guide the development of targeted prevention and personalized treatment of HCC.
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A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma.
BACKGROUND: This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy. METHODS: We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC. RESULTS: Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration. CONCLUSION: The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.
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Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.
BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.
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Elucidating the molecular and immune interplay between head and neck squamous cell carcinoma and diffuse large B-cell lymphoma through bioinformatics and machine learning.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, and larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity and aggressive behavior, leading to high mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL and certain types of HNSCC. The purpose of this study is to elucidate the molecular and immune interplay between HNSCC and DLBCL using bioinformatics and machine learning (ML) to identify shared biomarkers and potential therapeutic targets. METHODS: Differentially expressed genes (DEGs) were identified using the "limma" package in R from the HNSCC dataset in The Cancer Genome Atlas (TCGA) database, and relevant modules were selected through weighted gene co-expression network analysis (WGCNA) from a DLBCL dataset in the Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses were conducted using Gene Ontology (GO), Disease Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks and ML algorithms were employed to screen for biomarkers. The prognostic value of these biomarkers was evaluated using Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analyses. The Human Protein Atlas (HPA) database facilitated the examination of messenger RNA (mRNA) and protein expressions. Further analyses of mutations, immune infiltration, drug predictions, and pan-cancer impacts were performed. Additionally, single-cell RNA sequencing (scRNA-seq) data analysis at the cell type level was conducted to provide deeper insights into the tumor microenvironment. RESULTS: From 2,040 DEGs and 1,983 module-related genes, 85 shared genes were identified. PPI analysis with six algorithms proposed 21 prospective genes, followed ML examination yielded 16 candidates. Survival and ROC analyses pinpointed four hub genes-ACACB, MMP8, PAX5, and TNFAIP6-as significantly associated with patient outcomes, demonstrating high predictive capabilities. Evaluations of mutations and immune infiltration, coupled with drug prediction and a comprehensive cancer analysis, highlighted these biomarkers' roles in tumor immune response and treatment efficacy. The scRNA-seq data analysis revealed an increased abundance of fibroblasts, epithelial cells and mononuclear phagocyte system (MPs) in HNSCC tissues compared to lymphoid tissues. MMP8 showed higher expression in five cell types in HNSCC tissues, while TNFAIP6 and PAX5 exhibited higher expression in specific cell types. CONCLUSIONS: Leveraging bioinformatics and ML, this study identified four pivotal genes with significant diagnostic capabilities for DLBCL and HNSCC. The survival analysis corroborates their diagnostic accuracy, supporting the development of a diagnostic nomogram to assist in clinical decision-making.
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A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma.
Background: Renal clear cell carcinoma (ccRCC) is one of the most prevailing type of malignancies, which is affected by chemokines. Chemokines can form a local network to regulate the movement of immune cells and are essential for tumor proliferation and metastasis as well as for the interaction between tumor cells and mesenchymal cells. Establishing a chemokine genes signature to assess prognosis and therapy responsiveness in ccRCC is the goal of this effort. Methods: mRNA sequencing data and clinicopathological data on 526 individuals with ccRCC were gathered from the The Cancer Genome Atlas database for this investigation (263 training group samples and 263 validation group samples). Utilizing the LASSO algorithm in conjunction with univariate Cox analysis, the gene signature was constructed. The Gene Expression Omnibus (GEO) database provided the single cell RNA sequencing (scRNA-seq) data, and the R package "Seurat" was applied to analyze the scRNA-seq data. In addition, the enrichment scores of 28 immune cells in the tumor microenvironment (TME) were calculated using the "ssGSEA" algorithm. In order to develop possible medications for patients with high-risk ccRCC, the "pRRophetic" package is employed. Results: High-risk patients had lower overall survival in this model for predicting prognosis, which was supported by the validation cohort. In both cohorts, it served as an independent prognostic factor. Annotation of the predicted signature's biological function revealed that it was correlated with immune-related pathways, and the riskscore was positively correlated with immune cell infiltration and several immune checkpoints (ICs), including CD47, PDCD1, TIGIT, and LAG-3, while it was negatively correlated with TNFRSF14. The CXCL2, CXCL12, and CX3CL1 genes of this signature were shown to be significantly expressed in monocytes and cancer cells, according to scRNA-seq analysis. Furthermore, the high expression of CD47 in cancer cells suggested us that this could be a promising immune checkpoint. For patients who had high riskscore, we predicted 12 potential medications. Conclusion: Overall, our findings show that a putative 7-chemokine-gene signature might predict a patient's prognosis for ccRCC and reflect the disease's complicated immunological environment. Additionally, it offers suggestions on how to treat ccRCC using precision treatment and focused risk assessment.
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Single-Cell and Bulk Transcriptomic Integration Reveals an Adverse SPP1 Inflammatory TAM State and INHBA as a Candidate Biomarker in Non-Small Cell Lung Cancer Immunotherapy.
BACKGROUND: Biomarkers that capture the microenvironmental basis of response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) remain limited. We integrated single-cell and bulk transcriptomic data to identify response-associated immune programs with patient-level relevance. METHODS: Single-cell RNA-seq and clinical annotations from GSE207422 were used to construct an NSCLC tumor microenvironment atlas and refine myeloid and T cell states. UCell scoring, sample-level summaries, pseudobulk differential analysis, Monocle2 trajectory inference, and targeted macrophage-T cell interaction analysis were applied. A posttreatment SPP1 inflammatory TAM-derived NR-TAM signature was projected to pretreatment bulk RNA-seq from the same cohort. INHBA was evaluated in pretreatment bulk data, TCGA-LUAD/TCGA-LUSC, and lung cancer cell line assays. RESULTS: SPP1 inflammatory TAM was enriched in nonmajor pathologic response (NMPR) samples and co-occurred with exhausted CD8 T cell abundance, malignant cell hypoxia, and EMT programs. The NR-TAM signature was partially recapitulated in pretreatment bulk RNA-seq, with higher scores in NMPR but substantial group overlap. Exploratory ROC analysis showed modest discrimination for NR-TAM score (AUC = 0.60), INHBA (AUC = 0.64), and the combined model (AUC = 0.64; LOOCV AUC = 0.37). INHBA was higher in NMPR cases, correlated with residual tumor burden and selected TAM-related genes, showed less favorable survival trends particularly in LUSC, and its knockdown reduced proliferation and migration in lung cancer cells. CONCLUSIONS: An SPP1 inflammatory TAM program is associated with unfavorable pathologic response in NSCLC neoadjuvant immunotherapy. INHBA is a prioritized exploratory candidate within this program. Because the evidence is associative and predictive performance is modest, these markers require prospective validation before clinical use.
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Sialyltransferase gene signature as a predictor of prognosis and therapeutic response in kidney renal clear cell carcinoma.
BACKGROUND: Sialyltransferases are enzymes involved in the addition of sialic acid to glycoproteins and glycolipids, influencing various physiological and pathological processes. The expression and function of sialyltransferases in tumors, particularly in kidney renal clear cell carcinoma (KIRC) remained underexplored. This study aimed to develop a prognostic model based on sialyltransferase-related genes (SRGs) to predict the prognosis and treatment response of patients with KIRC. METHODS: We utilized RNA-Seq data of KIRC from The Cancer Genome Atlas (TCGA) database, selecting samples with survival data and clinical outcomes. Somatic mutation and neoantigen data were analyzed using the "maftools" package, and genes involved in the sialylation process were identified through the Molecular Signatures Database. Validation cohorts of KIRC samples were obtained from the International Cancer Genome Consortium (ICGC) database. Single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) platform, and preprocessing, normalization, and dimensionality reduction analyses were conducted using the "Seurat" package. Differentially expressed sialylation genes were identified using the "limma" package, and their functional enrichment was assessed via Gene Ontology GO and KEGG analyses. Consensus clustering analysis was performed to identify molecular subtypes of KIRC based on sialylation, and drug sensitivity of different subtypes was evaluated using the "pRRophetic" package. A risk signature model comprising 5 SRGs was constructed through univariate and multivariate Cox regression analyses and validated in both the TCGA and ICGC cohorts. The "estimate" package was utilized to calculate immune and stromal scores for each KIRC sample, assessing the tumor immune microenvironment characteristics of different subtypes. RESULTS: Analysis of scRNA-seq data identified 25 cell subtypes, categorized into 9 cell types. CD4 + memory cells exhibited the highest potential interactions with other cell subtypes. We identified 14 differentially expressed sialylation genes and confirmed their enrichment in various biological pathways through GO and KEGG analyses. Consensus clustering analysis based on sialylation identified 2 molecular subtypes: C1 and C2. The C2 subtype demonstrated higher sialylation scores and poorer prognosis. Drug sensitivity analysis indicated that the C1 subtype had better responses to Dasatinib and Lapatinib, whereas the C2 subtype was more sensitive to Epothilone B and Vinorelbine. The risk signature model, constructed with five distinct SRGs, exhibited strong predictive accuracy, as indicated by Area Under the Curve (AUC) values of 0.68, 0.69, and 0.70 for 1-, 3-, and 5-year survival, respectively, across both the TCGA and ICGC validation cohorts. Immune microenvironment analysis revealed that the C1 subtype exhibited higher immune and stromal scores, while the C2 subtype showed significantly enhanced expression of immune checkpoint genes. CONCLUSION: This study successfully developed a prognostic model based on SRGs, effectively predicting the prognosis and drug response of KIRC patients. The model demonstrated significant predictive performance and potential clinical application value. Furthermore, the study highlighted the critical role of sialylation in KIRC, offering new insights into its underlying mechanisms in tumor biology. These findings could guide personalized treatment strategies for KIRC patients, emphasizing the importance of sialylation in cancer prognosis and therapy.
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Single-cell RNA-sequencing of cellular heterogeneity and pathogenic mechanisms in paraquat-induced Parkinson's disease with depression.
Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases, and approximately one third of patients with PD are estimated to have depression. Paraquat (PQ) exposure is an important environmental risk factor for PD. In this study, we established a mouse model of PQ-induced PD with depression to comprehensively investigate cellular heterogeneity and the mechanisms underlying the progression of depression in the context of PD. We utilized single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of individual cells from model mice and characterize the gene expression profiles in each differentially expressed cell type. We identified a specific glutamatergic neuron cluster responsible for the development of heterogeneous depression-associated changes and established a comprehensive gene expression atlas. Furthermore, functional enrichment and cell trajectory analyses revealed that the mechanisms underlying the progression of PD with depression were associated with specific glutamatergic neurons. Together, our findings provide a valuable resource for deciphering the cellular heterogeneity of PD with depression. The suggested connection between intrinsic transcriptional states of neurons and the progression of depression can provide insight into potential biomarkers and specific targets for anti-depression treatment in patients with PD. SYNOPSIS: Our results obtained using model mice confirm the core effects of PQ exposure on glutamatergic neurons and their potential role in the development of PD with depression.
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Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression.
PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, largely due to its highly immunosuppressive and fibrotic tumor microenvironment (TME). However, the spatial and functional organization of its cellular components remains poorly understood. METHODS: We present an integrated transcriptomic atlas of the PDAC TME by combining single-cell RNA sequencing (n = 88; 187,520 cells), Visium spatial transcriptomics (n = 20; 67,933 spots), bulk RNA sequencing (n = 1,383), and high-resolution Xenium spatial transcriptomics (n = 2; 307,679 cells). Key findings were validated using scRNA-seq, bulk datasets, multiplex immunohistochemistry, and spatial imaging. RESULTS: POSTN⁺ fibroblasts and SPP1⁺ macrophages consistently co-infiltrated across 12 independent bulk RNA-seq cohorts, and showed spatial correlation in both Visium and Xenium platforms. Those tumor-promoting cell states were enriched in hypoxic, angiogenesis, and epithelial-mesenchymal transition, and were linked to poor prognosis. In contrast, CCL4⁺ CD8⁺ effector T cells and IGHG1⁺ plasma cells co-occurred within immune-active niches, were enriched for cytotoxic and activation-related pathways, and were associated with improved patient survival. Notably, these protective immune subsets remained detectable despite the immunosuppressive nature of the PDAC TME. Expression specificity of POSTN, SPP1, CCL4, and IGHG1 was validated at the transcriptomic and protein levels. CONCLUSIONS: We delineate two opposing cellular programs in the PDAC TME-tumor-promoting stromal remodeling and anti-tumor immune activation-spatially organized in distinct niches. Those findings suggest that targeting POSTN⁺ fibroblasts and SPP1⁺ macrophages-mediated stromal interactions while promoting CCL4⁺ T cell and IGHG1⁺ plasma cell immunity, may offer new therapeutic strategies for PDAC.