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Mitotic catastrophe heterogeneity: implications for prognosis and immunotherapy in hepatocellular carcinoma.
BACKGROUND AND AIMS: The mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear. METHODS: Based on 94 previously published highly correlated genes for MC, HCC patients' data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model. RESULTS: Identification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes. CONCLUSION: MC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.
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Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma.
BACKGROUND: Pancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods. METHODS: Based on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis. RESULTS: Platelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC. CONCLUSIONS: In this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.
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RANBP1 promotes oral cancer progression through activating YAP1 and NDUFB3 derived from the database of single-cell sequencing.
Oral cavity squamous cell carcinoma (OSCC) is the most common head and neck cancer, thriving in microenvironments composed of cancer cells, stromal tissue, and extracellular matrix. Extensive local invasion and cervical nodal metastasis can lead to poor treatment outcomes for OSCC. We created a single-cell transcriptomic database of OSCC using biopsies from 10 individuals to identify candidate genes. Ran-specific binding protein 1 (RANBP1) was identified as a gene with differential expression between malignant and normal epithelial cells. Analysis of the Cancer Genome Atlas Program (TCGA) OSCC and Taiwanese cohorts confirmed that higher RANBP1 levels are significantly associated with worse prognosis. Functional tests showed that knocking down RANBP1 reduced cell proliferation and invasion by decreasing proteins involved in focal adhesion, invadopodia formation, and epithelial-mesenchymal transition. Inhibiting RANBP1 also decreased vascular spread in zebrafish tumor xenografts, while overexpressing RANBP1 had the opposite effect. The overexpression of RANBP1 significantly increased tumor growth in NOD/SCID xenografts. RANBP1 was positively correlated with the oxidative phosphorylation pathway. Cells with reduced RANBP1 showed lower levels of NADH ubiquinone oxidoreductase subunit B3 (NDUFB3) and had impaired mitochondrial function. Additionally, RANBP1 depletion decreased activation of Yes1 associated transcriptional regulator (YAP1). Increased NDUFB3 expression in RANBP1-overexpressing cells was reversed by YAP1 inhibitor verteporfin treatment or knocking down YAP1. RANBP1 enhances a more invasive microenvironment of OSCC.
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T-cell exhaustion: A potential target biomarker of the tumour microenvironment affecting oesophageal adenocarcinoma.
BACKGROUND: Oesophageal adenocarcinoma (EAC) is one of the most common malignant tumours, and the number of patients is increasing year by year. T-cell exhaustion (TEX) is an important risk factor for tumour immunosuppression and invasion, but its underlying mechanism in the pathogenesis of EAC is not clear. METHODS: Unsupervised clustering was performed to screen relevant genes based on Gene Set Variation Analysis scores of the three pathways of the HALLMARK gene set IL2/IFNG/TNFA. Multiple enrichment analyses and data combinations were used to depict the relationship between TEX-related risk models and CIBERSORTx immune infiltrating cells. In addition, to explore the impact of TEX on EAC therapeutic resistance, we assessed the impact of TEX risk models on the therapeutic sensitivity of various novel drugs using single-cell sequencing and searched for their potential therapeutic targets and cellular communication. RESULTS: Four risk clusters of EAC patients were identified by unsupervised clustering and searched for potential TEX-related genes. Based on this, LASSO regression and decision trees were used to construct risk prognostic models containing a total of three TEX-associated genes in EAC. The results showed that TEX risk scores were significantly associated with the survival prognosis of EAC patients in both the Cancer Genome Atlas dataset and the independent validation set of Gene Expression Omnibus. Immune infiltration and cell communication analyses identified mast cell resting as a protective factor in TEX, and pathway enrichment analyses showed that the TEX risk model was highly associated with multiple chemokines as well as inflammation-associated pathways. In addition, higher TEX risk scores were associated with a weak responsiveness to immunotherapy. CONCLUSION: We describe the immune infiltration, prognostic significance and potential possible mechanisms of TEX in the EAC patient population. This is a novel attempt to promote the development of novel therapeutic modalities and immunological target construction for oesophageal adenocarcinoma. It is expected to make a potential contribution to advancing the exploration of immunological mechanisms and the opening of target drugs in EAC.
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Clinical Implications of the Novel Genetic Polymorphism Associated With Retinal Vein Occlusion: The East Asian GWAS Survey in a Single-Center Taiwanese Cohort.
PURPOSE: To explore the single-nucleotide polymorphic features in an East Asian cohort of retinal venous occlusion (RVO) patients, to map pathogenic genes, and to analyze potential pathophysiological associations. METHODS: This was a cross-sectional monocentric cohort study involving 172 patients with RVO and 1720 control participants from the Taiwanese population from a single center, Taichung Veterans General Hospital, under the framework of the Taiwan Precision Medicine Initiative (TPMI). DNA samples were collected from a Taiwanese population under the framework of the TPMI. Single nucleotide polymorphism (SNP) exploration was performed using PLINK 2.0. Gene mapping for the selected SNPs and functional analysis were conducted via the SNPXplorer online platform with the R packages biomaRt and clusterProfiler. Single-cell transcriptome mapping was achieved using the published Human Eye Transcriptome Atlas. RESULTS: We report several novel RVO-associated SNPs with suggestive genome-wide association (P < 1 × 10-5), including rs16938701 (STAU2), rs76802456 (FAM189A1), rs995014 (N6AMT1), rs933813, rs6991412 (STAU2), rs79040889, and rs12524176. Among them, the STAU2 single-nucleotide variant, rs16938701, had the most remarkable association (P = 3.19 × 10-7). After mapping to the published human retina single-cell transcriptome atlas, STAU2 exhibited relatively high expression levels in retinal pigment epithelium/choroid complex, microglia, and hyalocyte among all ocular cell types. CONCLUSIONS: We identified several novel risk loci significantly associated with the occurrence of RVO and examined potential mechanistic links between specific biological processes and the pathophysiology of RVO. TRANSLATIONAL RELEVANCE: These findings provide novel insights into the genetic basis of RVO and highlight key pathways that may inform future research and therapeutic strategies.
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Integrated bioinformatics, machine learning, and experimental validation identify a four-gene diagnostic signature for cervical cancer associated with PI3K/AKT signaling.
Early and accurate diagnosis remains a major challenge in cervical cancer management. This study aimed to identify reliable diagnostic biomarkers for cervical cancer by integrating bioinformatics and machine learning approaches and to further validate their biological relevance experimentally. Transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas were analyzed using differential expression analysis, weighted gene co-expression network analysis, and three machine learning algorithms to identify core genes. Diagnostic performance was evaluated using receiver operating characteristic curves and a nomogram model. Functional relevance was explored by drug sensitivity analysis, ssGSEA, immune infiltration analysis, and single-cell RNA sequencing. RT-qPCR validation was performed in 10 paired cervical cancer and adjacent normal tissues, while Western blotting was performed in three paired tissue samples. In vitro validation was conducted using SiHa and HeLa cells. Four genes, CCND1, TRIP13, MYBL2, and GNB4, were identified as potential diagnostic biomarkers, and the combined model showed superior diagnostic performance compared with any single gene (AUC = 0.989). Treatment with 3-methyladenine altered the expression of these genes, suggesting their potential association with PI3K/AKT-related pathway activity. Moreover, siRNA-mediated GNB4 knockdown suppressed cervical cancer cell proliferation and reduced PI3K and AKT phosphorylation, providing preliminary evidence for the functional involvement of GNB4 in PI3K/AKT pathway activation. CCND1, TRIP13, MYBL2, and GNB4 may serve as promising diagnostic biomarkers for cervical cancer. Their dysregulation was associated with PI3K/AKT pathway activity and may reflect molecular alterations involved in cervical cancer progression. In particular, GNB4 showed potential diagnostic relevance and preliminary functional significance, suggesting that it may represent a candidate biomarker and molecular target for further investigation.
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New insight into RNA biomarkers in neuropathic pain: a clinician-neuroscientist roadmap to translational testing and treatment monitoring a clinical review.
Neuropathic pain affects an estimated 7%-10% of the global population and imposes an annual economic burden exceeding $600 billion in the United States alone. It lacks robust objective biomarkers; current diagnosis relies heavily on subjective reporting and heterogeneous phenotypes. Currently utilized pain assessment tools include the brief pain inventory (BPI), numerical rating scales (0-10 pain scores), and the visual analog score (VAS), which depend on patient-reported outcomes and are influenced by social, psychological and contextual factors. This subjectivity contributes to heterogeneous phenotyping and variability (>30%) towards the treatment response. Emerging transcriptomic and epitranscriptomic evidence suggests that RNA-based biomarkers may offer a biologically sound and objective approach to understanding and managing pain by capturing underlying molecular mechanisms. Therefore, the present clinical review focused on RNA biomarker classes (mRNA, miRNA, lncRNA, RNA editing, RNA modifications) and proposes a clinically deployable testing system for diagnosis, stratification, and treatment monitoring, since there are no FDA-approved RNA-based biomarkers for pain. Therefore, this review synthesizes evidence from immune-cell transcriptomic meta-analysis (TCL1A/ERAP2), dorsal root ganglion (DRG) and central nervous system gene expression patterns (EFNB2, GABBR1, NCAM1, SCN11A)/brain genetic architecture via single-cell omics integration, and atlas-driven frameworks, like iPain single-cell atlas of pain chronification and nociceptor senescence. Additional sources include studies on RNA editing mediator adenosine deaminase acting on RNA2 (ADAR2), clinical and translational evidence supporting miRNA biomarkers, and lncRNA axes (NEAT1/miR-183-5p; H19/miR-141) as tissue-specific regulatory nodes. Additionally, m6A epitranscriptomic modifications regulated by the METTL3/METTL14 writer complex and FTO/ALKBH5 erasers, with site-specific methylation of GRIN2B mRNA shown to upregulate GluN2B in dorsal horn neurons and augment central sensitization. These biomarkers also demonstrate potential utility as pharmacodynamic readouts in drug and neuro-modulation trials. Additionally, an emerging RNA workflow technology pathway leveraging rapid low-input RNA based assays was also explained. All evidence supports the idea that these biomarkers can provide complementary insight into the mechanisms underlying pain. Although current evidence supports the feasibility of RNA-based biomarkers as indicators of key biological processes, however, the current pain biology score remains at the theoretical model stage and has not been validated through in vivo, in vitro or clinical trials.
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IL18BP links antigen processing and presentation programs to a dendritic cell-enriched immunoregulatory state in colorectal cancer.
BACKGROUND: Antigen processing and presentation (APP) is required for tumor immune recognition, but its relationship with the immune landscape in colorectal cancer (CRC) remains incompletely defined. In this study, APP-related transcriptional programs were used as the starting point to identify immune-cell-associated biomarkers with prognostic and immunotherapeutic relevance in CRC. METHODS: Six public CRC bulk cohorts were analyzed to define APP-related molecular states. Weighted gene co-expression network analysis and four machine-learning frameworks, including Boruta, LassoLR, Pamr, and XGBoost, were applied to prioritize genes linked to APP activity. Single-cell analysis was performed with an atlas of 121 samples and 169,427 cells to identify the cellular origin of the candidate gene. Immune infiltration, cancer-immunity-cycle activity, immune checkpoint blockade (ICB) response, predicted drug response, somatic mutation patterns, and in vitro function were then evaluated. RESULTS: Two APP-associated patterns were identified and showed different survival outcomes and immune landscapes. Network- and machine-learning-based screening prioritized IL18BP as the main APP-associated candidate. Higher IL18BP expression was associated with longer survival and an immune-active phenotype, including stronger antigen-presentation signals, increased immune infiltration, checkpoint-related signatures, and cancer-immunity-cycle activation. Single-cell profiling showed that IL18BP was preferentially enriched in dendritic cells (DCs), linking the APP-derived bulk signature to a DC-centered immunoregulatory compartment. Across nine external ICB-treated cohorts, IL18BP showed response-classification performance. Drug-response prediction and somatic mutation analyses identified additional features associated with IL18BP-defined groups. In CRC cell lines, IL18BP knockdown increased proliferation, migration, and invasion. CONCLUSION: These findings identify IL18BP as a DC-enriched, APP-associated biomarker in CRC. By connecting APP programs in bulk tumors with DC-level expression patterns at single-cell resolution, this study positions IL18BP as an APP-associated marker of a DC-enriched immune state. These findings support further evaluation of IL18BP for prognostic and immunotherapy stratification in colorectal cancer but do not establish a causal APP-DC-IL18BP pathway.
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High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration.
BACKGROUND: Many researches proved that non-coding RNAs are important in glioma development. We screened the differentially expressed genes through The Cancer Genome Atlas (TCGA) database and identified the molecule LYRM4-AS1 associated with prognosis. As a lncRNA, the expression level and role of LYRM4-AS1 in glioma are inconclusive. Therefore, we attempted to assess the clinical significance, expression and related mechanisms of LYRM4-AS1 in glioma by employing cell experiments and an integrative in silico methodology. METHODS: RNA-seq data were obtained from UCSC XENA and TCGA datasets. The Gene Expression Omnibus (GEO) database was used to download glioma-related expression profile data. The LYRM4-AS1 expression level was evaluated. Survival curves were constructed by the Kaplan-Meier method. Cox regression analysis was used to analyze independent variables. Patients were divided into high and low expression group base on the median LYRM4-AS1 expression value in glioma tissues. The DESeq2 R package was used to identify differentially expressed genes (DEGs) between two different expression LYRM4-AS1 groups. Gene set enrichment analysis (GSEA) was conducted. Next, the single-sample Gene Set Enrichment Analysis (ssGSEA) was done to quantify the immune infiltration of immune cells in glioma tissues. Gene expression profiles for glioma tumor tissues were used to quantify the relative enrichment score for each immune cell. Spearman correlation analysis was used to analyze the correlation between LYRM4-AS1 and biomarkers of immune cells as well as immune checkpoints in glioma. Finally, assays for cell apoptosis, cell viability and wound healing were conducted to evaluate the function on U87 MG and U251 cells after knocking down LYRM4-AS1. RESULTS: We found that LYRM4-AS1 was upregulated and related to the grade and malignancy of glioma. Survival analyses showed that high expression LYRM4-AS1 patients had poor clinical outcomes (P < 0.01). Cox regression analyses demonstrated that LYRM4-AS1 was an independent risk factor for overall survival (OS) in glioma (HR: 274 1.836; CI [1.278-2.639]; P = 0.001). Enrichment and immune infiltration analysis showed interferon signaling and cytokine-cytokine receptor interaction enriched in the LYRM4-AS1 high-expression phenotype, and LYRM4-AS1 showed significantly positively related to immune infiltration as well as immune checkpoints (P < 0.01). The knockdown of LYRM4-AS1 in U87 MG and U251 cells can inhibit migration and proliferation of cells (P < 0.05). CONCLUSIONS: These findings indicated that the increased LYRM4-AS1 may be useful for the diagnosis and prognosis of glioma and might participate in the immune infiltration.
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Lactylation prognostic signature identifies DHCR7 as a modulator of chemoresistance and immunotherapy efficacy in bladder cancer.
BACKGROUND: Bladder cancer (BLCA), the 10th most common cancer worldwide, presents a worsening prognosis as the disease progresses. Reliable tools for predicting BLCA prognosis and treatment efficacy remain urgently needed. METHODS: Expression profiles of lactylation related genes were analyzed utilizing the Cancer Genome Atlas (TCGA) database and BLCA data from the GSE13507 dataset. Two distinct clusters were identified through unsupervised clustering analysis. Lactylation associated gene signatures were established and subsequently validated using training cohort and different validation cohorts. Immune cell infiltration patterns and drug response profiles were systematically evaluated. Parallel analyses of lactylation related genes were conducted at the single-cell resolution. A series of in vivo and in vitro experiments were subsequently performed to validate the findings. RESULTS: We examined the mRNA expression profiles of 22 lactylation related genes in BLCA tissues. Through comprehensive analysis, we identified two distinct lactylation clusters that exhibited significantly different clinical outcomes and tumor immune microenvironment characteristics. Building upon these findings, we subsequently stratified patients into two molecular subtypes according to the lactylation clusters and established a robust genetic signature for predicting survival outcomes in BLCA patients. The lactylation risk score showed a strong connection with survival outcomes and correlated with the tumor microenvironment (TME) immunosignature and predicted immunotherapy efficacy. DHCR7 emerged as a pivotal prognostic gene from the nine gene model, prompting subsequent focused analyses. Single-cell analysis confirmed that DHCR7 reached peak expression in tumor epithelial cells, whereas TCGA data and single-cell data demonstrated strong associations between DHCR7 and diverse immune-cell populations. For the first time, we identified that knockdown of DHCR7 enhances the efficacy of both cisplatin chemotherapy and immunotherapy, highlighting DHCR7 as a key player in cisplatin resistance and its influence on immunotherapy effectiveness in BLCA. These findings offer valuable insights into potential combined therapeutic strategies. CONCLUSIONS: We developed a robust lactylation risk prediction model for accurately forecasting BLCA prognosis and identified DHCR7 as a pivotal biomarker involved in cisplatin resistance and influencing immunotherapy efficacy in BLCA.