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Metabolic reprogramming-associated genomic instability drives colorectal cancer progression via the UBXN1-NF-κB axis.
Colorectal cancer (CRC) is a common malignancy in clinical practice, and its treatment is greatly challenged by tumor heterogeneity. The most prominent features of CRC heterogeneity are differences in metabolic states and genomic instability, which ultimately lead to unfavorable clinical outcomes. Based on this, the present study aimed to investigate the association between metabolic reprogramming and copy number variation (CNV) in CRC using single-cell datasets. By integrating four publicly available single-cell RNA sequencing datasets, a comprehensive single-cell atlas of CRC was constructed. Subsequently, epithelial cells were specifically analyzed, and consensus non-negative matrix factorization (cNMF) was applied to identify six gene expression programs, covering functional modules such as cell cycle, metabolism, inflammatory stress, and immune interaction. Genomic instability was assessed using the inference of copy number variations (InferCNV) analytical tool, which identified malignant epithelial cells characterized by large-scale CNVs. Meanwhile, metabolic pathway activity at the single-cell level was evaluated using the area under the curve cell (AUCell) method, and predictive performance was further assessed using machine learning algorithms. The results demonstrated that metabolic features could effectively predict the malignant state defined by CNVs, achieving an area under the curve (AUC) of 0.985, with protein metabolism and TP53-related pathways contributing most significantly. Further integrative analysis identified 13 metabolism-related genes associated with clinical prognosis, among which UBXN1 was identified as a central node in the protein-protein interaction network. Functional analysis of UBXN1 revealed that it suppresses the NF-κB signaling pathway, thereby regulating the malignant phenotype of CRC cells. In conclusion, this study systematically elucidates the critical link between metabolic features and genomic instability in CRC, suggesting that UBXN1 may serve as a potential therapeutic target.
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Analysis of the potential biological value of pyruvate dehydrogenase E1 subunit β in human cancer.
BACKGROUND: The pyruvate dehydrogenase E1 subunit β (PDHB) gene which regulates energy metabolism is located in mitochondria. However, few studies have elucidated the role and mechanism of PDHB in different cancers. AIM: To comprehensive pan-cancer analysis of PDHB was performed based on bioinformatics approaches to explore its tumor diagnostic and prognostic value and tumor immune relevance in cancer. In vitro experiments were performed to examine the biological regulation of PDHB in liver cancer. METHODS: Pan-cancer data related to PDHB were obtained from the Cancer Genome Atlas (TCGA) database. Analysis of the gene expression profiles of PDHB was based on TCGA and Genotype Tissue Expression Dataset databases. Cox regression analysis and Kaplan-Meier methods were used to assess the correlation between PDHB expression and survival prognosis in cancer patients. The correlation between PDHB and receiver operating characteristic diagnostic curve, clinicopathological staging, somatic mutation, tumor mutation burden (TMB), microsatellite instability (MSI), DNA methylation, and drug susceptibility in pan-cancer was also analyzed. Various algorithms were used to analyze the correlation between PDHB and immune cell infiltration and tumor chemotaxis environment, as well as the co-expression analysis of PDHB and immune checkpoint (ICP) genes. The expression and functional phenotype of PDHB in single tumor cells were studied by single-cell sequencing, and the functional enrichment analysis of PDHB-related genes was performed. The study also validated the level of mRNA or protein expression of PDHB in several cancers. Finally, in vitro experiments verified the regulatory effect of PDHB on the proliferation, migration, and invasion of liver cancer. RESULTS: PDHB was significantly and differently expressed in most cancers. PDHB was significantly associated with prognosis in patients with a wide range of cancers, including kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, breast invasive carcinoma, and brain lower grade glioma. In some cancers, PDHB expression was clearly associated with gene mutations, clinicopathological stages, and expression of TMB, MSI, and ICP genes. The expression of PDHB was closely related to the infiltration of multiple immune cells in the immune microenvironment and the regulation of tumor chemotaxis environment. In addition, single-cell sequencing results showed that PDHB correlated with different biological phenotypes of multiple cancer single cells. This study further demonstrated that down-regulation of PDHB expression inhibited the proliferation, migration, and invasion functions of hepatoma cells. CONCLUSION: As a member of pan-cancer, PDHB may be a novel cancer marker with potential value in diagnosing cancer, predicting prognosis, and in targeted therapy.
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Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer.
OBJECTIVE: Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN: We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS: The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION: These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.
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WNT Signaling Driven by R-spondin 1 and LGR6 in High-grade Serous Ovarian Cancer.
BACKGROUND/AIM: R-spondins control WNT signaling and RSPO1 and LGR6, two of its receptors, are uniquely expressed at high levels in high-grade serous ovarian cancer (HGSOC). The aim of this study was to assess the interrelations between the expression of the RSPOs and LGRs in HGSOC and in the ovarian surface (OSE) and fallopian tube surface epithelium (FTSE) from which HGSOC arises. MATERIALS AND METHODS: Analysis of TCGA (HGSOC), CCLE (ovary), and other publicly accessed RNA-Seq data using UC San Diego Computational Cancer Analysis Library (CCAL) to perform differential expression analysis, association studies, and gene set inspection using the single-sample GSEA method. Additionally, we employed multiple publicly available databases including StringDB, Human Protein Atlas, and cBioPortal to aid the investigation. RESULTS: Among normal tissues, expression of RSPO1, LGR5 and LGR6 was highest in the fallopian tube. The relative levels of expression of the RSPOs and LGRs in the OSE and FTSE matched those in HGSOC. RSPO1 and LGR6 were highly co-expressed in all three tissues. Gene set enrichment analysis (GSEA) showed that expression of RSPO1 was strongly linked to the enrichment of three separate WNT-driven GO pathways. Analysis of genes that impacted overall survival identified two other immediately adjacent genes that control WNT signaling, KREMEN1 and ZNRF3 whose expression and copy number were coordinately linked. CONCLUSION: RSPO1 and LGR6 are coordinately expressed in HGSOC and the two normal tissues from which this tumor arises, and their expression is linked to WNT signaling pathways known the control cell fate and proliferation.
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OX40L and IL-2 combination strategy for gastric cancer immunotherapy.
BACKGROUND: Gastric cancer (GC) remains a major global health challenge with persistently low remission rates, largely because most patients are diagnosed at advanced stages. Although chemotherapy, targeted therapy, and immune checkpoint inhibitors have achieved progress, their benefits remain limited due to the highly immunosuppressive tumor microenvironment (TME), characterized by inadequate T-cell activation and impaired costimulatory signaling. Recent genomic and single-cell studies highlight the need for strategies that can more effectively enhance antitumor immunity. Given these challenges, this study aimed to evaluate whether dual stimulation with interleukin-2 (IL-2) and OX40 ligand (OX40L) could synergistically enhance T-cell activation in GC. Specifically, we analyzed OX40/OX40L expression patterns in GC tissues and tumor-infiltrating lymphocytes (TILs), assessed the immunostimulatory effects of IL-2 and OX40L in PBMCs and TILs, and tested a recombinant adenoviral vector co-expressing IL-2 and OX40L to determine its ability to potentiate T-cell-mediated cytotoxicity against primary GC tumor cells. METHODS: Based on preliminary analyses of The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing (scRNA-seq) data, we identified the costimulatory molecules IL-2 and OX40L as potential therapeutic targets. We collected tumor tissues and paired peripheral blood mononuclear cells (PBMCs) from 70 untreated GC patients, and used bioinformatics tools to analyze T cell subpopulations and OX40/OX40L expression in the TME. The immunostimulatory effects of IL-2 and OX40L, used alone or in combination, were evaluated in vitro on PBMCs and TILs. A recombinant adenoviral vector co-expressing IL-2 and OX40L was also constructed to assess its pro-apoptotic effect in primary GC tumor cell cultures. RESULTS: The combination of IL-2 and OX40L significantly enhanced T cell activation in both PBMCs and TILs, and increased the expression of antitumor effector molecules. The IL-2/OX40L adenoviral vector effectively activated TILs, which subsequently induced prominent apoptotic responses in primary GC tumor cells. CONCLUSIONS: Our study demonstrates that a costimulatory immunotherapy strategy combining IL-2 and OX40L significantly enhances antitumor responses in PBMCs and TILs from GC patients, showing strong therapeutic potential as a complementary approach for the treatment of GC.
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Tertiary lymphoid structure-related genes drive tumor microenvironment heterogeneity and prognostic disparities in left- vs. right-sided colon cancer.
BACKGROUND: Colorectal cancer (CRC) exhibits significant heterogeneity between left-sided (LCC) and right-sided (RCC) subtypes, influenced by tertiary lymphoid structures-related genes (TLSRGs). This study aimed to explore TLSRGs' roles in LCC/RCC differences, including expression, mutations, prognosis, and molecular pathways, while developing a prognostic risk model. METHODS: Data were integrated from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) (324 samples: 135 LCC, 189 RCC) and Gene Expression Omnibus (GEO) datasets. TLSRGs were analyzed for differential expression, mutations, and prognostic associations using Wilcoxon tests, Cox regression, and gene set variation analysis (GSVA). Consensus clustering identified molecular subtypes. Single-cell analysis involved Seurat/Harmony for clustering and annotation, single-sample gene set enrichment analysis (ssGSEA) for scoring, and CellChat for cell communication. A machine learning-based risk model was constructed by screening ten algorithms (including SuperPC, RSF, CoxBoost, etc.) using the Mime package. The SuperPC algorithm demonstrated the best performance and was built on TLSRGs and related pathways, validated via Concordance index (C-index), Kaplan-Meier (KM) curves, and receiver operating characteristic (ROC) analysis. RESULTS: Integrated analysis revealed significant heterogeneity between LCC and RCC, characterized by distinct somatic mutation profiles, differential expression of 27 TLSRGs, and subtype stratification via TLSRG-based consensus clustering (C1 enriched in RCC, P<0.001, with higher GSVA activity, P<0.001). Single-cell transcriptomics demonstrated reduced T-cell proportions, elevated epithelial cells, and lower tertiary lymphoid structure (TLS) scores in RCC, alongside weakened cell-cell communication strength (notably epithelial-T/B cell interactions). Prognostic genes exhibited location-specificity: FBLN7 [hazard ratio (HR) =7.09 for disease-specific survival (DSS)] and MS4A1 [HR =7.79 for disease-free interval (DFI)] predicted poor survival in LCC, while ICOS was protective in RCC [HR =0.42 for overall survival (OS)]. Drug sensitivity screening identified 22 agents with differential half-maximal inhibitory concentration (IC50) correlated to TLSRG expression, and immune infiltration analysis revealed reduced CD8+ T cells/follicular helper T cells in RCC. An 8-gene prognostic signature (HSPB1, NCOR2, STIL, etc.) derived via SuperPC effectively stratified high-risk patients in TCGA (HR =2.06, P=0.001) and GSE103479 (HR =1.86, P=0.02), with consistent performance across LCC/RCC subgroups. CONCLUSIONS: TLSRGs underpin LCC/RCC heterogeneity in mutations, expression, immunity, and cell interactions. The developed risk model offers robust prognostic value, highlighting potential therapeutic targets for sidedness-specific CRC management.
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Unveiling the role of Pleckstrin-2 in tumor progression and immune modulation: insights from a comprehensive pan-cancer analysis with focus on lung cancer.
Cancer remains a leading cause of mortality globally, highlighting the need for novel biomarkers to enhance prognosis and therapeutic strategies. Pleckstrin-2 (PLEK2), a member of the pleckstrin family, has been implicated in processes critical to tumor progression, but its role across cancers remains underexplored. This study systematically examined the expression patterns, prognostic relevance, and functional impact of PLEK2 across multiple cancer types. Using data from The Cancer Genome Atlas (TCGA), Genotype Tissue Expression Project (GTEx), and the Human Protein Atlas, we analyzed PLEK2 expression in both cancerous and normal tissues, revealing significant overexpression of PLEK2 in various cancers at the mRNA and protein levels. Single-cell RNA sequencing further indicated predominant expression of PLEK2 in tumor cells and macrophages within the tumor microenvironment. Survival analysis demonstrated that elevated PLEK2 expression correlated with poor prognosis in specific cancers, though its impact varied across cancer types. Functional assays showed that PLEK2 knockdown inhibited proliferation and migration in human cancer cell lines. In vivo studies using a Lewis lung carcinoma (LLC) model confirmed that PLEK2 knockdown suppressed tumor growth and enhanced the efficacy of PD-1 immunotherapy. Mechanistically, PLEK2 knockdown was associated with reduced AKT pathway activation, diminished tumor-associated macrophage infiltration, and increased CD8 T cell presence. Compounds like Navitoclax were also identified as potential PLEK2 inhibitors. In conclusion, PLEK2 played a multifaceted role in cancer progression and immune response modulation. Targeting PLEK2 might suppress tumor growth and overcome immunotherapy resistance, offering a promising biomarker and therapeutic target to improve cancer treatment outcomes.
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Deep learning and multi-omics reveal programmed cell death-associated diagnostic signatures and prognostic biomarkers in gastric cancer.
Gastric cancer (GC) is characterized by pronounced molecular and clinical heterogeneity, creating major challenges for therapeutic decision-making. Limitations in current molecular classification hinder the development of personalized therapies, underscoring the need for improved diagnostic and prognostic frameworks. we conducted an integrated multi-omics analysis of bulk, single-cell, and spatial transcriptomic data to systematically characterize three key programmed cell death pathways-pyroptosis, apoptosis, and necroptosis (collectively abbreviated as PAN). A scoring-based clustering framework integrating multiple machine learning algorithms was developed to define high-resolution molecular subtypes and construct a deep learning signature. A hybrid CNN+BiLSTM model with cross-fusion attention was applied for transcriptomic feature extraction and subtype classification, achieving superior performance compared with existing approaches. Validation in the TCGA cohort confirmed the robustness and biological relevance of our model. Among the identified subtypes, Subtype 2 showed the most favorable prognosis. We further established a nine-gene prognostic signature with strong predictive value. High-risk patients exhibited poor survival, enhanced immune infiltration, and potential sensitivity to AKT inhibitors, with several drugs, including gefitinib and paclitaxel, identified as promising candidates. Experimental validation was conducted using the Human Protein Atlas (HPA) and RT-qPCR in clinical samples. CFLAR and TNFSF13B were upregulated and PDK4 downregulated in GC, while UACA showed no significant change. Additional prognostic genes (DFFB, PSMB6, GLP1R, HDAC9, BACH2) displayed expression patterns largely consistent across HPA, TCGA, and RT-qPCR, with minor discrepancies likely due to sample size. This study integrates multi-omics and deep learning with experimental validation, providing insights into programmed cell death regulation and offering robust biomarkers and therapeutic targets for GC.
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Gene expression based inference of cancer drug sensitivity.
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.
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Values of a novel pyroptosis-related genetic signature in predicting outcome and immune status of pancreatic ductal adenocarcinoma.
BACKGROUND: Pyroptosis is an emerging form of programmed cell death associated with progression in malignancies. Yet, there are few studies reporting on the association between pancreatic ductal adenocarcinoma (PDAC) and pyroptosis. Therefore, we aimed to construct a pyroptosis-related genetic signature to predict the clinical outcome and immune status in PDAC patients. METHODS: RNA-seq data of 176 PDAC patients from The Cancer Genome Atlas (TCGA) and 167 PDAC patients from the Genotype-Tissue Expression Project were analysed for pyroptosis-related differentially expressed genes (DEGs) between PDAC and normal pancreas. The risk signature of DEGs was analysed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and its accuracy was validated in the Gene Expression Omnibus (GEO) cohort (n = 190). Functional enrichment analyses were performed to explore the mechanisms of the DEGs. The immune characteristics were evaluated using single-sample gene set enrichment analysis and ESTIMATE algorithms for each group. RESULTS: A nine-gene risk signature was generated from LASSO Cox regression analysis and classified PDAC patients into either a high- or low-risk group according to the median risk score. The high-risk group had significantly shorter overall survival than the low-risk group and it was verified in the external GEO database. A nomogram based on the risk signature was constructed and showed an ideal prediction performance. Functional enrichment analyses revealed that pyroptosis might regulate the tumor immune microenvironment in PDAC. Immune infiltration evaluation suggested that immune status was more activated in the low-risk group than in the high-risk group. CONCLUSION: The risk signature encompassing nine pyroptosis-related genes may be a prognostic marker for PDAC. Pyroptosis might affect the prognosis of PDAC patients via regulating the tumor immune microenvironment.