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Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma.
Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.
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A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.
Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multiomics data (The Cancer Genome Atlas, Gene Expression Omnibus, European Nucleotide Archive) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. Cox regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A metabolism-related prognostic model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across 4 cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene that promotes melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1- and PSMD14-mediated stabilization of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth through SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.
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MARVELD1 as a tumor stem cell marker and therapeutic target in visceral obesity-associated ovarian cancer.
BACKGROUND: Visceral obesity-related genes (VORGs) have been implicated in cancer progression, but their biological and clinical relevance in ovarian cancer (OC) remains uncertain. METHODS: Prognostic modeling of VORGs was carried out using the The Cancer Genome Atlas (TCGA) and GSE53963 datasets. This model was further investigated in its association with tumor immunity, immune checkpoint inhibitor (ICI) response, and chemotherapy outcomes. Additionally, single-cell data were analyzed to explore gene expression within the tumor microenvironment of this model, specifically identifying high expression of the key gene MARVELD1 in tumor stem cells. The functionality of MARVELD1 was validated experimentally through Cell Counting Kit-8 (CCK-8), transwell, and colony formation assays. RESULTS: OC patients were stratified into four molecular subtypes based on overall survival (OS)-associated VORG expression profiles. Differentially expressed genes (DEGs) between subtypes with prognostic relevance were identified and subsequently analyzed using least absolute shrinkage and selection operator (LASSO) regression and stepwise Cox modeling, resulting in a robust four-gene prognostic model comprising CXCL9, IGF2, FCGBP, and MARVELD1. This model effectively classified patients into high- and low-risk groups, with the high-risk group demonstrating significantly poorer outcomes (P < 0.001). In addition, risk scores showed strong correlations with immune checkpoint gene expression and predicted chemotherapy sensitivity, underscoring their utility in forecasting therapeutic responses. Single-cell transcriptomic analysis revealed heterogeneous expression of model genes across subpopulations, with MARVELD1 prominently enriched in tumor stem cells. MARVELD1 was found to modulate early cellular processes through pathways such as reactive oxygen species. Finally, in vitro experiments confirmed that MARVELD1 knockdown significantly inhibited OC cell proliferation, invasion, and colony formation, further validating its oncogenic role. CONCLUSION: A VORG-based prognostic model was established that correlates with OC prognosis, tumor immune microenvironment, and treatment sensitivity. These findings suggest that MARVELD1 and related pathways may serve as potential targets to improve prognostic stratification and guide therapy in OC.
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Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients.
BACKGROUND: Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear. OBJECTIVE: To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets. METHODS: Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro. RESULTS: We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway. CONCLUSION: This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.
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Identification of characteristic markers correlated with Th2 cell infiltration and metabolism molecular subtype in pancreatic adenocarcinoma.
BACKGROUND: Pancreatic adenocarcinoma, the deadliest malignant cancer, has gradually become the third leading cause of cancer-related death. Multidisciplinary therapy has been difficult to implement because of the particularity of pancreatic adenocarcinoma. Research has increasingly indicated the significance of metabolic adaption in pancreatic adenocarcinoma. The difference in metabolism may influence immune cell infiltration in pancreatic adenocarcinoma. Novel immune-related metabolism biomarkers are needed to improve the therapeutic outcomes of existing targeted therapies. METHODS: We enrolled whole-genome sequencing data and clinical information about 168 pancreatic adenocarcinoma samples from The Cancer Genome Atlas (TCGA) database, other pancreatic adenocarcinoma samples, and clinical information from other cohorts. We used the gene set variation analysis (GSVA) package to calculate feature score, the weighted gene co-expression network analysis (WGCNA) and randomSurvivalForest package to screen hub genes, the ConsenClusterPlus package to classify subtypes, the pRRopthetic package to evaluate drug sensibility, the maftools package to analyze mutation information and the Seurat package to analyze single cell sequencing data. RESULTS: We revealed the prognosis significance of Th2 cell infiltration, classified two subtypes based on hub genes, compared immune cell infiltration, substance metabolism, cellular processes, gene mutation, and copy number variation (CNV) between subtypes and explored the clinical and biological features of Th2 cell infiltration. CONCLUSIONS: We displayed the poor prognosis significance of Th2 cell infiltration and the significant difference of simple nucleotide polymorphism, CNV, natural killer (NK) CD56 bright cell infiltration, substance metabolism, autophagy and necroptosis between subtypes. Additionally, we discovered the sensitivity difference of chemotherapy drug and the Th2 cell infiltration changes after chimeric antigen receptor T cells (CAR-T) cell therapy and radiotherapy and explored the differences between normal liver and metastatic liver tissues of pancreatic adenocarcinoma patients.
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Diversity of ribosomes at the level of rRNA variation associated with human health and disease.
Ribosomal DNA and RNA (rDNA and rRNA) sequences are usually discarded from sequencing analyses. But with hundreds of copies of rDNA genes it is unknown whether they possess sequence variations that form different types of ribosomes that affect human physiology and disease. Here, we developed an algorithm for variant-calling between paralog genes (termed RGA) and compared rDNA variations found in short- and long-read sequencing data from the 1,000 Genomes Project (1KGP) and Genome In A Bottle (GIAB). We additionally developed a novel protocol for long-read sequencing full-length rRNA (RIBO-RT) from actively translating ribosomes. Our analyses identified hundreds of rDNA variants, most of which, surprisingly, are short insertion-deletions (indels) and dozens of highly abundant rRNA variants that are incorporated into translationally active ribosomes. To visualize variant ribosomes at the single cell level, we developed an in-situ rRNA sequencing method (SWITCH-seq) which revealed that variants are co-expressed within individual cells. Strikingly, by analyzing rDNA, we found that variants assemble into distinct ribosome subtypes. We discovered that these subtypes acquire different rRNA structures by successfully employing dimethyl sulfate (DMS) probing of full length rRNA. With this atlas we investigated rRNA variation changes across human tissues and cancer types. This revealed tissue-specific rRNA subtype expression in endoderm/ectoderm-derived tissues. In cancer, low abundant rRNA variants can become highly expressed, which suggests the presence of cancer-specific ribosomes. Together, this study identifies and comprehensively characterizes the diversity of ribosomes at the level of rRNA variants which is dominated by indel variants, their chromosomal location and unique structure as well as the association of ribosome variation with tissue-specific biology and cancer.
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Expression Analysis and Significance of PD-1, LAG-3, and TIM-3 in Human Non-Small Cell Lung Cancer Using Spatially Resolved and Multiparametric Single-Cell Analysis.
PURPOSE: To determine the tumor tissue/cell distribution, functional associations, and clinical significance of PD-1, LAG-3, and TIM-3 protein expression in human non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: Using multiplexed quantitative immunofluorescence, we performed localized measurements of CD3, PD-1, LAG-3, and TIM-3 protein in >800 clinically annotated NSCLCs from three independent cohorts represented in tissue microarrays. Associations between the marker's expression and major genomic alterations were studied in The Cancer Genome Atlas NSCLC dataset. Using mass cytometry (CyTOF) analysis of leukocytes collected from 20 resected NSCLCs, we determined the levels, coexpression, and functional profile of PD-1, LAG-3, and TIM-3 expressing immune cells. Finally, we measured the markers in baseline samples from 90 patients with advanced NSCLC treated with PD-1 axis blockers and known response to treatment. RESULTS: PD-1, LAG-3, and TIM-3 were detected in tumor-infiltrating lymphocytes (TIL) from 55%, 41.5%, and 25.3% of NSCLC cases, respectively. These markers showed a prominent association with each other and limited association with major clinicopathologic variables and survival in patients not receiving immunotherapy. Expression of the markers was lower in EGFR-mutated adenocarcinomas and displayed limited association with tumor mutational burden. In single-cell CyTOF analysis, PD-1 and LAG-3 were predominantly localized on T-cell subsets/NKT cells, whereas TIM-3 expression was higher in NK cells and macrophages. Coexpression of PD-1, LAG-3, and TIM-3 was associated with prominent T-cell activation (CD69/CD137), effector function (Granzyme-B), and proliferation (Ki-67), but also with elevated levels of proapoptotic markers (FAS/BIM). LAG-3 and TIM-3 were present in TIL subsets lacking PD-1 expression and showed a distinct functional profile. In baseline samples from 90 patients with advanced NSCLC treated with PD-1 axis blockers, elevated LAG-3 was significantly associated with shorter progression-free survival. CONCLUSIONS: PD-1, LAG-3, and TIM-3 have distinct tissue/cell distribution, functional implications, and genomic correlates in human NSCLC. Expression of these immune inhibitory receptors in TILs is associated with prominent activation, but also with a proapoptotic T-cell phenotype. Elevated LAG-3 expression is associated with insensitivity to PD-1 axis blockade, suggesting independence of these immune evasion pathways.
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Identification of cancer-associated fibroblast subpopulation and construction of an immunotherapy signature for gastric cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are thought to contribute to malignant tumor progression. Although it has been reported that signatures based on CAFs-specific genes could be used to evaluate the prognosis of gastric cancer (GC), as a heterogeneous subgroup, the CAFs subpopulations and their characteristics in the GC microenvironment were still ambiguous. This study aimed to identify CAFs subgroups in GC and evaluate their prognostic and immunotherapy relevance. METHODS: We utilized the Gene Expression Omnibus (GEO) database to acquire single-cell sequencing data of CAFs associated with GC, and The Cancer Genome Atlas (TCGA) database to obtain GC patient messenger RNA (mRNA) expression profiles. All CAFs were classified using unsupervised clustering based on the expression of 235 characteristic genes. The unsupervised clustering results indicated that the CAFs in the GC microenvironment were divided into four clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed the intrinsic differences of these clusters and the CIBERSORT algorithm was used to perform immune cell infiltration analysis. RESULTS: Cluster1, Cluster2 and Cluster4 were partly related to extracellular matrix production, while Cluster3 was mainly related to the TGF-β pathway. In addition, the CAF-related score based on Cluster3 (CRS3) could significantly distinguish the prognosis of patients. Moreover, patients with low CRS3 scores had a higher tumor mutation burden and higher levels of immune checkpoints (CTLA-4, LAG3, MAGE-A3, PD-1 and PD-L1), which might indicate a higher immunotherapy response. CONCLUSIONS: All in all, our study revealed four heterogeneous subpopulations of CAFs in the GC microenvironment, and highlighted that Cluster3 was associated with prognosis and response to immunotherapy.
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Programmed Cell Death 10 (PDCD10) Is a Candidate Tumor-associated Gene in Esophageal Squamous Cell Carcinoma.
BACKGROUND/AIM: Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy with poor survival rates. Effective molecular-targeted therapies are urgently needed. This study aimed to identify novel candidate tumor-associated genes in ESCC by analyzing chromosomal amplification regions. MATERIALS AND METHODS: DNA copy number variation (CNV) and mRNA expression data were obtained from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE). Single-cell RNA sequencing data from Gene Expression Omnibus (GEO) were analyzed using Scanpy. Immunohistochemistry was performed on formalin-fixed, paraffin-embedded (FFPE) ESCC tissues. PDCD10 was identified as a potential tumor-associated gene, and its association with clinicopathological factors and prognostic impact was evaluated using Kaplan-Meier survival and Cox regression analyses. Pathway analysis was performed to investigate the biological processes, and drug sensitivity profiling was conducted to identify compounds whose efficacy correlated with PDCD10 expression in ESCC cell lines. RESULTS: PDCD10, a signaling protein involved in cell proliferation and vascular development, showed significant amplification and over-expression in ESCC cases. High PDCD10 expression was associated with poor prognosis. Single-cell RNA sequencing confirmed its tumor-specific expression. GSEA revealed enrichment of mTORC1 signaling, E2F, and Myc target pathways in high PDCD10-expressing tumors. Drug sensitivity analysis identified Azelaic acid and Rebamipide as compounds whose efficacy correlated with PDCD10 expression in ESCC cell lines. CONCLUSION: PDCD10 could be a novel tumor-associated gene associated with tumor progression and poor prognosis in ESCC. Azelaic acid and Rebamipide are candidate therapeutic agents targeting PDCD10.
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Single-cell analysis highlights the role of CD4+ IFN-I-related T cells in Behcet's uveitis during Interferonα-2a therapy.
BACKGROUND: Behcet's disease (BD) involves multiple immune cells, but the mechanism by which interferon α-2a (IFNα-2a) exerts therapeutic effects on BD through immune cell modulation remains unclear. This study aimed to investigate the role of CD4 + IFN-I-related T cells in BD with active uveitis during IFNα-2a therapy. METHODS: A single-cell atlas of peripheral blood mononuclear cells (PBMCs) was constructed from BD patients with active uveitis, post-4-month IFNα-2a therapy BD patients, active BD patients, and healthy controls (HCs) by integrating in-house and public scRNA-seq data. Bulk mRNA sequencing of CD4 + T cells from active BD patients and HCs was performed for validation. Cell-cell interaction, in vitro coculture, and inhibitor experiments were used to explore the underlying mechanisms. RESULTS: CD4 + IFN-I-related T cells (characterized by high interferon-related gene expression) were significantly decreased in active BD patients but restored after IFNα-2a therapy. The LLT1-CD161 interaction intensity between CD4 + IFN-I-related T cells and NK cells was reduced in active BD and recovered post-therapy. CD4 + IFN-I-related T cells inhibited NK cell activation and IFN-γ secretion via the CD161 receptor. CONCLUSIONS: IFNα-2a therapy reverses the decreased frequency of CD4 + IFN-I-related T cells in active BD, which in turn inhibits the inflammatory phenotype of NK cells through LLT1-CD161 interaction, providing new insights into the therapeutic mechanism of IFNα-2a in BD.