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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.
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Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy.
INTRODUCTION: The TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive. METHODS: Breast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays. RESULTS: At the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells. DISCUSSION: Our study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.
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Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma.
OBJECTIVE: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. METHODS: LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. RESULTS: A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. CONCLUSION: Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.
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TFPI-high myofibroblast states and a meta-program-related five-gene prognostic signature in breast cancer.
Intratumoral heterogeneity and tumor-microenvironment interactions limit prognostic stratification in breast cancer, but the prognostic relevance and cellular context of recurrent transcriptional meta-programs remain unclear. We aimed to derive a meta-program-related prognostic signature and characterize its component transcripts at single-cell resolution. Six paired institutional tumors and adjacent non-tumor tissues served as a proof-of-concept comparison. Univariable Cox screening and least absolute shrinkage and selection operator Cox regression were used to derive a five-gene score from a prespecified meta-program-related candidate set in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) training cohort; the score was tested internally and assessed in GSE20685 using fixed coefficients and cohort-specific median cutoffs. GSE161529 single-cell transcriptomic data were used to map signature transcripts across 136,526 quality-controlled cells, while donor-aware pseudobulk analysis compared upper- and lower-quartile TFPI expression states in annotated myofibroblasts. The score comprised TCN1, FOXJ1, PIGR, SLAIN1, and TFPI and was associated with overall survival in the training, testing, and external cohorts, with concordance indices of 0.782, 0.756, and 0.721, respectively. TFPI transcripts were detected across endothelial, fibroblast, and myofibroblast compartments. TFPI-high myofibroblasts showed transcriptional enrichment of extracellular matrix and collagen fibril organization, transforming growth factor beta signaling, epithelial-mesenchymal transition, and myogenesis, together with lower oxidative phosphorylation and fatty acid metabolism programs. In bulk TCGA-BRCA tissue, TFPI expression correlated positively with stromal (r = 0.48), immune (r = 0.25), and composite microenvironment scores (r = 0.40; all p < 0.001). These findings identify a hypothesis-generating five-gene bulk-tissue prognostic signature and an expression-associated TFPI-high myofibroblast state but do not establish a discrete lineage, the cellular source of bulk TFPI, a TFPI-dependent mechanism, or clinical utility. Independent prospective cohorts, spatial and protein-level validation, and functional perturbation studies are required.
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Single-vessel transcriptome map pathological landscapes and reveal NR2F2-mediated smooth muscle cell phenotype acquisition in capillary malformations.
BACKGROUND: Capillary malformation (CM) is a congenital vascular anomaly affecting the skin, mucosa, and brain, yet the understanding of its vascular pathogenesis remains limited. METHODS: We applied spatial whole-transcriptome profiling (GeoMx) and gene set enrichment analysis within CM lesions at single vasculature level. Differentially expressed genes were validated by immunofluorescence staining. Phosphoproteomics was profiled to uncover lesion-wide phosphorylation sites on proteins. Single-cell RNA sequencing was performed on CM-derived induced pluripotent stem cells (iPSCs) to determine differentiation trajectories of lesional vascular lineages. In silico gene perturbation was used to predict candidate genes for modulating vascular pathological progression, followed by functional validation in CM iPSC-derived endothelial cells (ECs) using a Tet-on system. RESULTS: A spatial transcriptomic atlas was constructed, and pathological landscape of individual CM vasculature was delineated. CM vessels exhibited hallmarks of endothelial-to-mesenchymal transition (EndMT), including disruption of adherens junctions (AJs), vascular identity transitions, and metabolic remodeling. Phosphoproteomics confirmed that differentially phosphorylated proteins were enriched in EndMT- and AJ-related pathways. Aberrant expression of venous transcriptional factor NR2F2 was observed in lesional ECs and correlated with progressive enlargement from capillaries to larger-caliber vessels containing multiple layers of smooth muscle cells (SMCs). In CM iPSCs, differentiation course yielded reduced ECs but increased SMCs. In silico knockout simulation predicted NR2F2 as a crucial regulator of facilitating SMC phenotype in CM. Consistently, enforced NR2F2 expression during iPSC differentiation suppressed endothelial markers while inducing SMC-associated genes. CONCLUSIONS: Single CM vasculature displays pathological hallmarks characterized by EndMT and AJ disruption, leading to progressive vascular remodeling. NR2F2 functions as a central regulatory factor orchestrating the acquisition of the SMC phenotype, thereby representing a potential therapeutic target in CM.
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HDAC2-mediated chromatin remodeling drives hepatocellular carcinoma progression: an integrative analysis of computational pathology and multi-transcriptomics.
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, characterized by high heterogeneity and poor prognosis. Chromatin remodeling regulates chromatin structure and function, and its dysregulation promotes tumor progression. Histone deacetylase 2 (HDAC2) is a key regulator involved in this process. This study links clinicopathological features with molecular mechanisms to elucidate the role of the chromatin-remodeling factor HDAC2 in hepatocellular carcinoma. METHOD: Based on whole-slide images (WSIs) from TCGA-LIHC, pathological features were extracted by combining ResNet-50 and CellProfiler to build a prognostic model. SHAP was subsequently applied to interpret the most influential predictive features. Through integrative multi-omics analysis, HDAC2 was identified as a core gene, and its association with malignant phenotypes was validated using the Human Protein Atlas (HPA), bulk transcriptomic data, and single-cell datasets. Malignant cells were further identified with inferCNVpy, while CellChat and Vector were used to characterize intercellular communication and developmental trajectories. Finally, spatial transcriptomics integrated with the Spotlight and MISTy algorithms revealed the spatial distribution of HDAC2 and its interactions within the tumor microenvironment. The pro-tumor capability of HDAC2 was verified by both cell-based assays and nude mouse tumorigenicity experiments. RESULTS: From WSIs, 630 features were extracted with CellProfiler and 2,048 with ResNet-50. the top nine prognostic features were used to build a Lasso + GBM model selected from 101 machine learning algorithm combinations. SHAP analysis revealed the contribution of each feature. Correlation analysis with a curated chromatin remodeling gene set identified 49 genes significantly associated with these 9 features. For HDAC2, integrative analyses across bulk, single-cell, and spatial transcriptomics revealed its biological roles and underlying mechanisms. ROC curve analysis confirmed strong predictive performance, with average AUCs of 0.84 and 0.77 in the training and validation sets, respectively. HDAC2 knockdown reduced IL-1β/IL-6/TNF-α transcripts, lowered colony formation by ~ 55%, curbed invasion/migration by ~ 30–50% (P < 0.001), and trimmed xenograft weight by ~ 35% (P < 0.01), confirming that HDAC2 drives proliferation, motility and in-vivo tumorigenicity. CONCLUSION: HDAC2 drives hepatocellular carcinoma (HCC) progression by strengthening intercellular communication and forming a spatially organized oncogenic axis. Further analyses indicate that HDAC2-high cells can be regarded as differentiation origin points of the tumor and are significantly associated with poor prognosis.
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Altered Atlas of Exercise-Responsive MicroRNAs Revealing miR-29a-3p Attacks Armored and Cold Tumors and Boosts Anti-B7-H3 Therapy.
Increasing evidence has shown that physical exercise remarkably inhibits oncogenesis and progression of numerous cancers and exercise-responsive microRNAs (miRNAs) exert a marked role in exercise-mediated tumor suppression. In this research, expression and prognostic values of exercise-responsive miRNAs were examined in breast cancer (BRCA) and further pan-cancer types. In addition, multiple independent public and in-house cohorts, in vitro assays involving multiple, macrophages, fibroblasts, and tumor cells, and in vivo models were utilized to uncover the tumor-suppressive roles of miR-29a-3p in cancers. Here, we reported that miR-29a-3p was the exercise-responsive miRNA, which was lowly expressed in tumor tissues and associated with unfavorable prognosis in BRCA. Mechanistically, miR-29a-3p targeted macrophages, fibroblasts, and tumor cells to down-regulate B7 homolog 3 (B7-H3) expression. Single-cell RNA sequencing (scRNA-seq) and cytometry by time-of-flight (CyTOF) demonstrated that miR-29a-3p attacked the armored and cold tumors, thereby shaping an immuno-hot tumor microenvironment (TME). Translationally, liposomes were developed and loaded with miR-29a-3p (lipo@miR-29a-3p), and lipo@miR-29a-3p exhibited promising antitumor effects in a mouse model with great biocompatibility. In conclusion, we uncovered that miR-29a-3p is a critical exercise-responsive miRNA, which attacked armored and cold tumors by inhibiting B7-H3 expression. Thus, miR-29a-3p restoration could be an alternative strategy for antitumor therapy.