Integrative Multiomics Analysis Reveals a Cancer Stem Cell–Driven Prognostic Signature and Nominates Belinostat for Targeted Therapy in Hepatocellular Carcinoma
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.
Abstract
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.
Background Colorectal cancer (CRC) is characterized by profound molecular heterogeneity and complex tumor–microenvironment interactions, which contribute to invasion, metastasis, and variable clinical outcomes. More effective prognostic biomarkers and therapeutic targets are still needed. Methods We integrated single-cell RNA sequencing, spatial transcriptomics, multi-cohort bulk transcriptomic data, drug response prediction, and in vitro experiments to characterize malignant epithelial states in CRC and identify clinically relevant biomarkers and candidate therapeutics. Stemness, copy number variation, pathway activity, and cell–cell communication were analyzed at single-cell resolution. A prognostic model was established using epithelial marker genes and validated across TCGA and five GEO cohorts through a consensus machine-learning framework. Drug sensitivity was evaluated using CTRP and PRISM datasets, and candidate compounds were further prioritized through a network-based drug repositioning strategy. Spatial transcriptomics was used to define the tissue localization of key genes, and functional assays were performed to assess the role of TRIP6 in CRC cells. Results Malignant epithelial cells exhibited increased stemness, frequent aneuploidy, enhanced glycolysis, along with upregulation of the Wnt/β-catenin and PI3K-AKT-mTOR signaling cascades. Cell–cell communication analysis revealed prominent extracellular matrix-related interactions linking cancer-associated fibroblasts and epithelial cells, suggesting a microenvironmental contribution driving epithelial–mesenchymal transition (EMT). The prognostic model showed stable predictive performance across independent cohorts. Among the model genes, TRIP6 was identified as a risk-associated candidate and was enriched at the tumor–stroma invasive boundary by spatial transcriptomic analysis. In vitro, TRIP6 knockdown suppressed CRC cell proliferation and migration, promoted apoptosis, and partially reversed EMT-associated marker expression, supporting a potential role for TRIP6 in malignant progression. In addition, drug prediction analyses identified several compounds with potential therapeutic relevance for high-risk patients. Conclusion This study provides a multi-omic framework for understanding CRC progression and suggests that TRIP6 may be involved in linking microenvironmental signaling to invasive phenotypes, with potential value for prognostic stratification and therapeutic exploration.
Qiwei Cui, Xiao-Hong Gao, Xiaoqing Wu et al.· Frontiers in Cell and Develo...· 0 citations
It is suggested that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD, and the proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.
Qian Zheng, Yunxiao Liu, Tianli Li et al.· Frontiers in Oncology· 0 citations
: Backgrounds: Colorectal cancer (CRC) prognosis remains difficult due to molecular heterogeneity and interaction between tumor cells and the immune microenvironment. This study aimed to identify transcriptomic and immune-cell patterns associated with overall survival (OS) and to develop an integrated prognostic model to improve risk stratification. Methods: RNA-sequencing was performed on 131 primary CRC samples and matched normal tissues. Differentially expressed genes (DEGs) were identified and functionally characterized through gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein–protein interaction (PPI) network analysis. Immune-cell composition was estimated using CIBERSORTx deconvolution and evaluated for its association with OS. Prognostic DEGs were screened using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk score. The model was validated in the Cancer Genome Atlas Database (TCGA)-COAD (colon cancer) and READ (rectal cancer) cohorts. A nomogram integrating molecular and clinicopathological variables were generated. Results: A total of 5589 DEGs were identified between CRC and normal tissues, enriched in pathways related to cell cycle, Tumor Protein P53 (TP53), WNT Family Member (WNT), Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT), calcium signaling, metabolism, and immune regulation. PPI analysis highlighted ten upregulated hub genes involved in mitotic spindle formation and chromosomal stability. Immune infiltration analysys indicated that higher proportions of plasma cells ( p = 6.9 × 10 − 4 ), naïve B cells ( p = 0.019), resting CD4 + memory T cells ( p = 0.02), M0 macrophages ( p = 0.0077), and activated dendritic cells ( p = 1.89 × 10 − 5 ) were associated with improved OS, whereas monocytes ( p = 0.012), neutrophils ( p = 0.041), activated mast cells ( p = 0.0066), and M2 macrophages ( p = 0.014) were linked to poorer survival. A seven-gene signature including aspartate beta-hydroxylase (ASPH), bradykinin receptor B1 (BDKRB1), calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1), C-C motif chemokine receptor 8 (CCR8), cyclic nucleotide gated channel subunit alpha 3 (CNGA3), microtubule associated protein 1A (MAP1A) and oxytocin receptor (OXTR) stratified patients into high-and low-risk groups with significant OS differences. The model demonstrated strong predictive performance (AUC: 0.84 at 1 year) and was validated in TCGA cohorts. Multivariate analysis confirmed the risk score as an independent prognostic factor. The integrated nomogram accurately predicted 1, 3-, and 5-year survival (C-index = 0.757; 95% CI 0.724–0.791). Conclusions: We developed and validated a seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients.
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
Cancer remains a major cause of global mortality, necessitating the identification of novel biomarkers to improve prognosis and guide therapy. ABCA1, an ATP‐binding cassette transporter involved in cholesterol efflux, has been implicated in tumorigenesis, yet its pan‐cancer roles and clinical relevance are not fully understood. We conducted a comprehensive multi‐omics analysis of ABCA1 across diverse cancers, evaluating its expression, prognostic significance, genomic alterations, tumor immune microenvironment interactions, and regulatory mechanisms. ABCA1 expression was dysregulated in multiple malignancies and associated with poor prognosis in STAD, STES, and LGG, though favorable outcomes were observed in KIRC. It correlated with genomic instability markers (TMB, MSI, HRD), immune cell infiltration, and cancer stemness. Pathway analyses revealed enrichment in cholesterol metabolism and efferocytosis‐related pathways. Among the cancer types examined, esophageal squamous cell carcinoma (ESCC) was selected for exploratory functional validation due to its prognostic relevance. In vitro, ABCA1 knockdown inhibited proliferation, invasion, and migration in ESCC cell lines. In silico drug sensitivity analysis suggested potential associations with dasatinib response and panobinostat resistance, warranting further experimental validation. Collectively, these findings highlight the context‐dependent associations of ABCA1 with cancer progression, immune modulation, and genomic integrity, suggesting its potential utility as a prognostic biomarker that warrants further investigation for therapeutic targeting.
Chenyang Wang, Hanbing Wang, Xi Chen et al.· Cell Biochemistry and Functi...· 0 citations
A READ-derived response-associated gene signature for recurrence stratification and exploratory cross-cohort evaluation in additional colorectal cancer cohorts, while further exploring its association with treatment-response phenotypes.
Shuai Li, Jingxian Li, Xianyue Bu et al.· Human Cell· 0 citations