Molecular Cancer

Colorectal cancer (CRC) screening remains limited by patient adherence and sub-optimal sensitivity for early-stage disease. While liquid biopsy has revolutionized cancer diagnostics, cfDNA-based methods often struggle with early-stage detection due to low analyte levels. Here, we present a robust cell-free RNA (cfRNA) platform for the early detection of CRC. Using a retrospective cohort of 255 healthy controls and 250 CRC patients, we implemented an optimized workflow featuring a RUVg-based normalization strategy to remove platelet-driven transcriptomic noise. We identified differentially expressed genes enriched in key CRC-associated biological pathways, including inflammation, EMT, and metabolic dysregulation. An XGBoost classifier trained on these features achieved a mean AUC of 0.92 in cross-validation and 0.89 in a validation cohort, demonstrating 67% sensitivity at 90% specificity. Notably, our platform showed particular efficacy in identifying early stage cancer (stage I and II), achieving 73.7% sensitivity at 90% specificity. These findings suggest that cfRNA profiling offers a powerful, non-invasive orthogonal approach to CRC screening, capable of overcoming the sensitivity limitations of DNA-based assays in early-stage disease.

Across gastrointestinal (GI) cancers, shared malignant programs are layered onto strong anatomical, lineage, and microenvironmental variation, making it difficult to distinguish disease-relevant long noncoding RNAs (lncRNAs) from context-dependent transcriptional signals. We developed a pan-GI integrative framework to classify lncRNAs across colorectal adenocarcinoma, gastric adenocarcinoma, and esophageal cancer using bulk and single-cell transcriptomic resources. This framework evaluates lncRNAs across four complementary dimensions: recurrent tumor-associated expression, clinical association with disease progression and overall survival, co-expression network context, and malignant epithelial expression at single-cell resolution. Paired tumor-normal RNA-seq analyses identified extensive tumor-associated lncRNA dysregulation and defined recurrent pan-GI lncRNAs consistently upregulated across cancer types. Clinical analyses further nominated transcripts linked to tumor extension, nodal involvement, metastatic dissemination, progression-linked expression, and adverse overall survival. Co-expression network analysis identified lncRNAs embedded within disease-associated transcriptional modules, providing functional context for otherwise poorly annotated transcripts. In parallel, single-cell-derived metacell analysis nominated malignant epithelial-associated and detection-supported lncRNAs, helping distinguish tumor-compartment-associated signals from stromal, immune, endothelial, and other microenvironmental contributions. Together, this study establishes an evidence-structured pan-GI lncRNA resource and a generalizable prioritization strategy for nominating disease-associated noncoding transcripts. More broadly, the framework provides a transferable strategy for systematic lncRNA prioritization across other cancers and heterogeneous disease contexts.

While recent therapeutic advances have extended the survival of patients with non-small cell lung cancer (NSCLC), overcoming metastatic progression in the CNS remains a significant challenge. Some patients with NSCLC may require concurrent management of CNS and extracranial metastases, while others develop isolated brain metastasis or leptomeningeal disease. These heterogenous clinical outcomes are difficult to predict and diagnose for early intervention with current surveillance modalities. Herein, we comprehensively analyzed gene mutations, copy number variations, and DNA methylation of NSCLC brain metastasis tissue collected at the time of craniotomy, combined with ctDNA sequencing of paired plasma and CSF liquid biopsies. We confirmed a high concordance between the molecular features of brain metastasis tissue with ctDNA from CSF which were largely distinct from ctDNA alterations in paired plasma samples. Plasma ctDNA tumor fraction and ctDNA hypermethylation were most significantly associated with extracranial metastasis and overall survival. Alternatively, we identified specific hypermethylated DNA loci in brain metastasis tissue and CSF ctDNA as significant correlates of brain metastasis progression and risk of leptomeningeal disease. Our findings support the utility of integrating ctDNA testing from CSF and plasma, while revealing distinct epigenetic features and biomarkers of brain metastasis or leptomeningeal disease.

Detection of reliable markers of therapy response and drug resistance remains a major unmet need in brain cancer, as serial tumor biopsy is often not feasible. This challenge is particularly acute in diffuse midline glioma (DMG), a fatal pediatric brain tumor for which new targeted therapies are entering clinical use, yet tools for real-time molecular analysis of tumor evolution during therapy remain lacking. Here, we demonstrate that plasma tumor-derived extracellular vesicle (EV) profiling provides a minimally invasive and complementary approach for diagnosis and longitudinal molecular monitoring in H3K27M-mutant DMG. Across patient-derived tumor models and clinical plasma samples, EV mRNA levels correlated strongly with parental tumor transcriptomes. EV H3K27M mRNA enabled discrimination of DMG from non-DMG controls, and exploratory EV response-associated mRNAs were linked to radiographic response and progression-free survival in patients receiving ONC201. To enable tumor-selective EV enrichment and multiplexed molecular profiling within a clinically practical workflow, we developed a new integrated platform supporting same-day EV analysis from small plasma volumes. This work represents the first proof-of-concept demonstration of the diagnostic and treatment response relevance of tumor-derived EV RNA in pediatric DMG and establishes a generalizable framework for minimally invasive longitudinal molecular monitoring in diseases where tissue access is inherently limited.

Leptomeningeal disease (LMD) is a rapidly fatal complication of systemic cancer for which sensitive diagnostic tools and informative biomarkers remain limited. Here, we introduce CSF-Seq, a method for whole-transcriptome sequencing of cell-free RNA (cfRNA) from human cerebrospinal fluid (CSF), designed to enable molecular profiling of LMD and other central nervous system (CNS) conditions. Using a prospectively collected CSF biobank, we analyzed 125 samples spanning multiple pathologies, including breast and lung LMD, glioblastoma, traumatic brain injury, and non-cancer neurological controls. Through optimized RNA extraction, library preparation, and deep sequencing, CSF-Seq generated robust and reproducible transcriptome-wide profiles despite the low abundance and fragmentation of cfRNA in CSF. CSF transcriptomes exhibited disease-specific expression, separating LMD from non-cancer controls and from non-LMD cancers, independent of CSF collection modality. Tumor-associated epithelial transcripts, including CEACAM6 and MUC1, were consistently enriched in LMD samples, whereas immune and CNS-associated transcripts were broadly detected across disease states, consistent with contributions from both tumor and non-tumor sources. Cross-site processing of matched samples demonstrated high concordance, indicating preservation of sample-specific transcriptional signatures across independent workflows. Importantly, we identified a collection method- independent LMD gene expression signature that was significantly associated with overall survival, supporting its potential prognostic relevance. Together, these findings establish CSF-Seq as a technically robust and clinically informative platform for transcriptomic biomarker discovery in CNS metastatic disease, offering a minimally invasive approach for disease characterization, risk stratification, and longitudinal monitoring in patients with LMD.

DNA methylation is informative for liquid biopsy, but low template abundance, distributed methylation signals and workflow complexity limit implementation. Here we present Delta-HLD, a PCR-compatible methylation assay platform that quantifies methylation directly in native DNA through sequential hybridization, ligation and methylation-sensitive digestion. The assay co-reports methylation-dependent signals from multiple loci through a shared amplification architecture, generating a single panel-level PCR readout. We established the chemistry, optimized panel size and composition through model-guided experiments, and implemented the assay as a triplex qPCR workflow with per-sample internal process controls. Plasma proof-of-concept analyses showed discriminatory signal in CRC and proof-of-concept transferability to hepatocellular carcinoma. Additional platelet-retaining experiments identified a strategy to increase recovery of analyzable circulating templates while reducing genomic DNA recognition. Delta-HLD provides a compact PCR-compatible framework for low-input methylation analysis without base conversion.

We previously reported that the murine lncRNA Epr is essential for maintaining colon mucosal integrity and permeability. Mice lacking Epr in the colon are more susceptible to colitis and tumor development. Additionally, we demonstrated that human EPR expression is reduced in ulcerative colitis and in a small cohort of colon adenocarcinoma patients. Here, we present evidence that human and mouse EPR share several key physiological features: preferential binding to the KH1 domain of their interacting protein, KSRP; specific expression in canonical and immature goblet cells of the large intestine; and a functional role in intestinal goblet cell development. The correlation between EPR levels and survival in large cohorts of metastatic colon adenocarcinoma patients, together with the capacity of human EPR to inhibit cell proliferation and induce apoptosis in two distinct human colon adenocarcinoma cell lines, suggests that EPR may serve as both a valuable prognostic marker for goblet cell-derived adenocarcinomas and a potential therapeutic target.

Long non-coding RNAs (lncRNAs) are increasingly recognised as effectors of oncogenic signalling, yet the transcriptional programmes through which driver mutations regulate lncRNA expression remain poorly defined. Here we identify LINC00941 as a direct transcriptional target of FOSL1, an AP-1 transcription factor downstream of the KRAS-MAPK pathway, establishing the first FOSL1-regulated lncRNA in lung adenocarcinoma (LUAD). LINC00941 is significantly upregulated in LUAD across multiple independent cohorts, and its depletion via siRNAs, shRNAs, and antisense oligonucleotides (ASOs) induces proliferative arrest and stress-induced premature senescence, accompanied by transcriptomic suppression of cell cycle and DNA damage response (DDR) genes. Mechanistically, LINC00941 operates through a dual-compartment mechanism engaging two STAR-family RNA-binding proteins in distinct subcellular contexts. In the nucleus, LINC00941 binds SAM68 through its 700-1300 nucleotide region and shields it from proteasomemediated degradation, thereby sustaining SAM68-dependent PARP1 activation and DDR competency; RNF123 is identified as a candidate E3 ligase mediating SAM68 turnover in the absence of LINC00941. In the cytoplasm, LINC00941 sequesters QKI, preventing its nuclear translocation; LINC00941 depletion releases QKI to the nucleus, driving alternative splicing dysregulation including validated NUMB exon 12 exclusion, and QKI co-depletion rescues the anti-proliferative phenotype both in vitro and in xenograft models. Multi-cohort survival analysis across three independent LUAD datasets (n=649) identifies LINC00941 as an independent prognostic factor for poor overall survival. Gymnotic ASO-mediated targeting of LINC00941 significantly suppresses xenograft tumour growth without systemic toxicity, providing preclinical proof-of-concept for therapeutic tractability. Together, these findings establish LINC00941 as a compartment-specific oncogenic scaffold within the KRAS-FOSL1 transcriptional axis and a tractable therapeutic target in LUAD.

Colorectal cancer (CRC) is highly heterogeneous at both clinical and molecular levels, and the integration of circulating biomarkers with comprehensive genomic profiling remains limited. In this study, we measured circulating urokinase-type plasminogen activator (uPA) and its receptor (uPAR) in 53 patients with colorectal neoplasms and performed whole-genome sequencing (WGS) on matched tumor-normal pairs from 51 patients to characterize somatic mutations, copy number alterations (CNAs), tumor mutational burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), and mutational signatures. Circulating uPAR levels were significantly elevated in patients with CRC compared with healthy controls, showing a stepwise increase across tumor stages and reaching the highest levels in stage IV disease. In contrast, circulating uPA levels showed only a non-significant trend toward elevation and did not vary significantly by stage. Despite the strong association between uPAR and tumor progression, circulating uPA and uPAR levels were not significantly correlated with TMB, MSI, HRD scores, or the mutational status of major CRC driver genes, including TP53, KRAS, FBXW7, BRAF, NRAS, and PIK3CA. Genomic analysis revealed a heterogeneous mutational landscape dominated by TP53 and APC, with only a minority of tumors exhibiting high TMB or MSI. Mutational signatures were primarily clock-like (SBS1, SBS5), with minimal contribution from MMR- or HRD-related processes. Together, these findings indicate that circulating uPAR is a robust marker of CRC progression that appears to operate largely independently of established genomic instability metrics. This supports uPAR potential utility in risk stratification and biological monitoring when integrated with molecular profiling.

Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality, partly because it is often diagnosed at advanced stages and frequently develops resistance to platinum-based chemotherapy. We previously showed that MAFG becomes derepressed following miR-7 hypermethylation, promoting platinum resistance in NSCLC and ovarian cancer cell lines. Although MAFG is a well-established regulator of oxidative stress, recent evidence in melanoma and colorectal cancer suggests an additional role as a regulator of methylator phenotypes. However, how MAFG reshapes the lung cancer epigenome remains unknown. Here, we investigated the contribution of MAFG to DNA methylation remodeling by combining CRISPR/Cas9-mediated MAFG deletion with CpG-Methyl-Array profiling, followed by expression (qPCR) and methylation (qMSP) validation in tumor cell lines. Our translational approach integrated aptahistochemistry using MAFG-specific aptamers in 127 NSCLC patients, methylation analysis in 35 fresh-frozen tumors and 40 FFPE samples, and interrogation of TCGA methylation datasets. MAFG loss reduced promoter methylation of LIF and MAFG itself. Importantly, these effects were subtype-specific, with MAFG expression and methylation displaying distinct transcriptional programs in LUAD versus LUSC, and prognostic associations restricted to KRAS-mutated adenocarcinomas. In NSCL in silico and in house cohorts, lower MAFG methylation and higher MAFG protein levels were both associated with worse prognosis. In summary, our findings identify MAFG as a regulator of DNA methylation in NSCLC and support the use of MAFG DNA methylation, or protein levels as clinically relevant prognostic biomarkers, particularly in lung adenocarcinoma.

Oncogene amplification is a key driver of tumorigenesis and a perpetuator of genomic instability. Oncogene amplification accelerates cancer cell proliferation and evolution, contributing substantially to the enhancement of adaptation mechanisms, such as treatment resistance, which pose a significant therapeutic challenge. However, previous studies have shown oncogene amplification to be a critical vulnerability, rendering cancer cells, but not normal cells, susceptible to targeted, CRISPR-Cas9 nickase - mediated DNA damage and cell death in vitro. Here, we demonstrate the initial framework for the translation of this potential therapeutic approach utilizing Cas9D10A - mRNA and functionalized lipid nanoparticles for the targeted delivery, and suppression of disseminated MYCN-amplified neuroblastoma in vivo.

Background: Despite epidemiological interest in aspirin's chemopreventive potential against glioma, the underlying multi-layered molecular mechanisms -- spanning COX-2/PGE2 signaling, iron metabolism, ferroptosis, epigenetic regulation, and the NEO1/hepcidin regulatory axis -- have not been systematically characterized at the multi-omics level. Methods: We conducted an integrative multi-omics analysis leveraging TCGA-GBM (n=172) and TCGA-LGG (n=534) transcriptomes, CPTAC GBM proteomics (n=99), TCGA HM450K DNA methylation data (GBM n=140, LGG n=516), GEO aspirin perturbation datasets, IEU OpenGWAS summary statistics, and independent single-cell RNA-seq data (GSE131928, 28 GBM patients). Eight analytical tracks were executed: (1) COX-2/PGE2 pathway profiling, (2) BBB tight junction characterization, (3) GEO-derived aspirin response signature projection, (4) gut-brain axis evaluation, (5) Mendelian randomization (MR) using PTGS2 cis-SNPs, (6) iron metabolism and ferroptosis pathway analysis, (7) NEO1/HFE2/BMP6/HAMP regulatory axis characterization with multi-omics validation, and (8) single-cell transcriptomic validation across GBM malignant cell states. Results: Transcriptomic analysis revealed profound reprogramming of the NEO1/hepcidin iron regulatory axis in GBM: HAMP (hepcidin) was massively upregulated (log2FC=+2.92, P=5.0e-37), accompanied by TFRC upregulation (log2FC=+1.38, HR=2.30, P=3.6e-42) and NEO1 downregulation (log2FC=-0.57, HR=0.59, P=4.6e-6). De novo HM450K methylation analysis revealed HAMP as the dominant epigenetic target in the iron network, exhibiting the strongest hypomethylation signal (DeltaBeta=-0.265, P=1.4e-48), while NEO1 and TFRC showed constitutively low baseline methylation (Beta<0.05). Gene set enrichment analysis identified ferroptosis driver genes (NES=+1.861, P=0.030) and the iron deficiency response pathway (NES=+1.698, P=0.010) as the most significantly enriched pathways in GBM. Molecular subtype analysis revealed that the mesenchymal GBM subtype exhibits the highest iron metabolism gene expression. Mendelian randomization established a causal relationship between PTGS2 expression and glioma risk (IVW OR=1.31, P=1.1e-4). Single-cell RNA-seq analysis validated that iron metabolism gene expression is heterogeneously distributed across malignant cell states, with the mesenchymal state exhibiting the highest HAMP expression and elevated ferroptosis vulnerability. GPX4 was universally highly expressed across all cell states, indicating pan-GBM dependence on GPX4-mediated ferroptosis suppression. Conclusions: This multi-omics investigation reveals that the NEO1/hepcidin iron regulatory axis is epigenetically reprogrammed in glioma, driving iron-dependent vulnerability that bridges COX-2 signaling with ferroptosis susceptibility. The convergent evidence from transcriptomics, proteomics, epigenomics, and causal inference provides a comprehensive mechanistic framework for aspirin's protective effects against glioma and identifies the NEO1/HAMP/TFRC axis as a promising therapeutic target.

RNAGAN (version 2.0, https://github.com/ZhaozhengHou-HKU/RNAGAN-2.0.git) is a published foundation model that analyzes single-cell and bulk-level RNA sequencing samples and enables multiple applications that enhance medical insights. Here we applied this model to Nasopharyngeal Carcinoma (NPC) as in-context few-short format (i.e., the model was never trained with any NPC data). We conducted all four supported functions, which include sample stratification, vectorization, pseudo data generation, and marker identification. The results were then used for identifying metastatic NPC and to investigate mechanisms associated with NPC metastasis. Examination with stratification showed that the accuracy of RNAGAN results for evaluating the metastasis risk in NPC patients are comparable to or outcompeted recently published risk estimation linear prediction model. Vectorization results present consistency across multiple cohorts and RNAGAN model versions. In the task of identifying markers and mechanisms related to NPC metastasis, incorporating pseudo data substantially enhanced the representativeness of single-cohort-based differential expression (DE) analysis. Moreover, RNAGAN identified metastasis-related marker genes based on single cohort, were concordant with the ground truth obtained across multiple cohorts (p=1.05e-9). Regarding biomedical mechanisms, RNAGAN enabled second-order feature extraction, unveiling a remarkable domination of the protective function of adaptive immune responses (as indicated by IL21R levels) over the hazardous function of chronic, non-resolving innate inflammation (as indicated by S100A8 levels) against NPC metastasis after first-line treatment. This association demonstrates a high degree of consistency with the external cohort. This study demonstrates the utility of the foundation model RNAGAN in uncovering therapeutic insights for novel cancer types without extra training. We reveal a critical spatial mechanism preventing distant metastasis via humoral anti-tumor immunity in NPC. High S100A8 expression by innate antigen-presenting cells (APCs) triggers an inflammatory cascade promoting epithelial-mesenchymal transition (EMT) and metastasis. However, when germinal center IL21R+ B cells simultaneously colocalize with these innate signals, they override this suppressive tissue stress. Spatial analysis shows that a high S100A8/IL21R intersection within tumor regions strictly distinguishes treatment responders, whereas non-responders display spatial mismatch or S100A8+ hyper-infiltration. This coordinated innate-adaptive cross-talk sustains functional tertiary lymphoid structures (TLS) that mature IgG-secreting plasma cells, which opsonize and eliminate emerging EMT tumor cells before systemic escape. Consequently, while S100A8 alone is an unreliable prognosticator, its spatial colocalization with IL21R is a robust protective indicator overlooked by conventional bulk analysis methods.

Peripheral whole-blood gene expression profiling offers a minimally invasive route to lung cancer detection, but high-dimensional transcriptomic data are prone to optimistic bias when preprocessing and model selection are not properly separated from performance evaluation. We applied L1-penalised (LASSO) logistic regression to 303 peripheral whole-blood microarray profiles (123 lung cancer cases and 180 healthy controls; Gene Expression Omnibus accession GSE252168; Illumina HumanHT-12 v4) within a leakage-free nested cross-validation framework (5 outer and 3 inner folds), in which all data-dependent steps (imputation, univariate feature screening by ANOVA F-test with k = 500, and standardisation) were confined strictly to training partitions. Statistical significance was assessed by permutation testing (B = 100), and feature selection stability was quantified across outer folds. LASSO was compared with ridge logistic regression, linear support vector machines, and random forest under the same framework. The LASSO model identified a sparse 29-probe signature with a pooled out-of-fold area under the ROC curve (AUC) of 0.990 (nested estimate 0.989 +/- 0.015), accuracy 97.4%, sensitivity 94.3%, and specificity 99.4% at a 0.50 threshold; permutation testing confirmed significance (p = 0.0099). Six probes, including CDC42, U2AF1, and RPS15A, were selected in all five outer folds, forming a stable core, and all classifiers exceeded AUC 0.987, indicating a strong, algorithm-independent signal. A leakage-free nested cross-validation framework enables unbiased performance estimation and reproducible feature selection in blood-based lung cancer classification. The 29-probe panel is an internally validated candidate requiring prospective, multicentre external validation before clinical use.

BackgroundFor pancreatic cancer, practical blood-based tests for early detection and postoperative surveillance remain elusive. We sought to develop a qPCR-measurable serum microRNA (miRNA) panel that robustly discriminates pancreatic cancer from non-cancer controls and other malignancies. MethodsWe profiled 255 serum miRNAs in batch 1 (n=72) and selected 27 candidates. Candidates were refined in batch 2 (n=552) and cross-batch evaluation was performed with batch 3 (n=391) to derive a miRNA model. Independent validation used batch 4 (n=616). Clinical relevance was assessed in an independent clinical cohort of resection patients with samples obtained preoperatively and at 1 and 12 months postoperatively. ResultsThe miRNA model trained on batches 2 and 3 achieved an area under the curve (AUC) of 0.91 and 0.83 for pancreatic cancer versus non-cancer controls and non-cancer plus other cancers, respectively, when independently validated in batch 4. Stage-wise AUCs in batch 4 were 0.91 (I), 0.94 (II), 0.86 (III) and 0.90 (IV). In the clinical batch, the score decreased postoperatively (preoperative vs month 1; p<0.01) and was higher in recurrence than non-recurrence (p<0.001). ConclusionsThe developed compact miRNA qPCR assay discriminated pancreatic cancer across independent assay batches and showed clinical relevance for postoperative surveillance. Clinical Trial RegistrationNot applicable.

Lung cancer is a highly heterogeneous disease and remains the leading cause of cancer-related mortality worldwide. While mouse models and patient-derived organoids have advanced our understanding of lung cancer, key interactions within the tumor microenvironment (TME) remain poorly characterized. We developed microtumor models from lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) using mouse and patient samples, including surgical resections and rapid autopsy specimens. Microtumors preserve structural, cellular, and molecular features of the native TME, enabling mechanistic studies of tumor progression ex vivo. Multi-omics analyses of LUAD microtumors revealed progression-associated changes, including increased epithelial-to-mesenchymal transition (EMT) and metabolic reprogramming toward fatty acid synthesis. Pharmacologic inhibition of fatty acid synthesis through ACC1/2 reduced proliferation in patient-derived microtumors, identifying a targetable vulnerability. This platform provides a robust system for studying tumor progression, therapeutic response, and resistance mechanisms in lung cancer, including culturing postmortem specimens that are not accessible in current models.

Background: Non-invasive diagnosis, reliable recurrence surveillance remain critical unmet needs in gliomas. Glioma induces profound systemic immune alterations despite its anatomical confinement to the central nervous system. Circulating immune cells, particularly monocytes, are key mediators of tumor-host crosstalk and may retain tumor-induced transcriptional imprints. However, their potential clinical utility as blood-based biomarkers for detection and monitoring, remain largely unexplored. Methods and findings: In this study, we performed integrated single-cell RNA sequencing of blood immune cells and demonstrated that circulating CD14+ monocytes are significantly expanded in glioma patients, exhibiting features of differentiation arrest and increased transcriptional plasticity. These cells harbor glioma-specific molecular signatures distinct from those observed in healthy controls and patients with other tumors. Leveraging these findings, we developed an ensemble machine learning diagnostic model based on transcriptomic profiles of circulating CD14+ monocytes (training cohort, n=107), which achieved a mean area under the receiver operating characteristic curve (AUC) of 0.971 during cross-validation. In an independent cohort of 567 participants, the model maintained high diagnostic accuracy, yielding an AUC of 0.877 for distinguishing glioma from controls and other tumors. And it achieved a recurrence detection AUC of 0.969 in 51 postoperative samples. Moreover, in a prospective follow-up study involving 30 glioma patients, lower model-derived scores of postoperation were significantly associated with prolonged progression-free survival (log-rank test, P=0.043), supporting its prognostic utility. Conclusion: We demonstrate circulating CD14+ monocytes undergo glioma-specific transcriptional reprogramming, generating systemic tumor-associated signal captured via transcriptomic profiling. This blood-based diagnostic model provides non-invasive, scalable approach for glioma detection, recurrence surveillance, outcome prediction.

Patients with non-small cell lung cancer (NSCLC) carrying activating EGFR mutations typically respond favorably to third-generation EGFR tyrosine kinase inhibitors (TKIs) such as osimertinib. Nevertheless, resistance almost inevitably emerges, ultimately limiting the durability of these treatments. We investigated non-genomic mechanisms enabling drug-tolerant persister cells to survive EGFR inhibition, likely co-opting compensatory HER3 activation, whose underlying mechanisms remain unclear. Using a combination of immortalized and patient-derived cellular models, together with single-cell RNA sequencing, we demonstrate that activation of EGFR/HER3 axis constitutes an early adaptive response to TKI exposure enriched in pulmonary alveolar type I and type II cancer cells. This response is driven by neuregulin-1 (NRG1), produced by stromal cells and by cancer cells undergoing NE-like/EMT dedifferentiation. Importantly, in vivo studies demonstrated that combining EGFR inhibition and NRG1 neutralization by monoclonal antibody successfully eradicated tumors. Together, these findings point to a therapeutic strategy to overcome TKI resistance in NSCLC through targeting HER3 signalling, its interplay with EGFR, and tumor microenvironment-derived cues.

Background & AimsColorectal cancer liver metastasis is the leading cause of mortality in affected patients, with liver sinusoidal endothelial cells playing a pivotal role in metastatic niche formation. Proprotein convertase subtilisin/kexin type 9 has emerged as a regulator of tumor biology, but its function in the hepatic microenvironment remains poorly defined. This study aimed to characterize the role and subcellular localization of PCSK9 in liver sinusoidal endothelial cells and to evaluate the therapeutic potential of its endothelial-specific inhibition in colorectal liver metastasis. MethodsIn vitro studies were performed using human and murine liver sinusoidal endothelial cells stimulated with conditioned media from metastatic colorectal cancer cells and cancer stem cells. Subcellular localization was assessed by immunofluorescence, immunogold electron microscopy, and biochemical fractionation. Protein interactions were investigated using co-immunoprecipitation and proteomic analyses. For in vivo validation, a murine model of colorectal liver metastasis was generated by intrasplenic injection of tumor cells, followed by systemic administration of chondroitin sulfate-targeted nanoparticles delivering PCSK9 siRNA every 5 days for 18 days. ResultsPCSK9 was consistently expressed in liver sinusoidal endothelial cells and displayed a predominant nuclear localization, which increased upon tumor-induced activation. Proteomic integration identified multiple candidate interacting proteins involved in metabolic and tumor-related pathways. Targeted nanoparticle-mediated delivery achieved efficient PCSK9 silencing in vitro. In vivo, endothelial-specific PCSK9 inhibition significantly reduced liver metastatic tumor burden compared with control groups, whereas free siRNA showed no significant effect. ConclusionsPCSK9 exhibits a novel nuclear localization in liver sinusoidal endothelial cells and potentially interacts with proteins implicated in tumor mediated pathways. Selective inhibition of endothelial PCSK9 using targeted nanoparticles significantly reduces colorectal liver metastasis, highlighting a novel therapeutic strategy focused on the hepatic microenvironment. Impact and ImplicationsThis study provides mechanistic insight into how PCSK9 contributes to colorectal liver metastasis by identifying its novel nuclear localization and potential function in liver sinusoidal endothelial cells. These findings are important for researchers and clinicians seeking to understand microenvironment-driven metastasis and resistance to current therapies. The demonstration that endothelial-specific targeting of PCSK9 reduces metastatic burden suggests a new avenue for therapeutic development beyond systemic inhibition. Such strategies could be translated into precision nanomedicine approaches to improve outcomes in patients with metastatic colorectal cancer while minimizing off-target effects.

Plasma-derived cell-free small non-coding RNAs are promising non-invasive biomarkers for cancer detection and monitoring. However, variability in sequencing output limits standardization, and cross-platform performance for plasma small RNA profiling has not been systematically evaluated. Illumina short-read sequencing is the current standard, whereas the newcomer, Ultima-Genomics platform, has been less extensively studied for circulating small RNA in plasma. To directly compare platform performance, we sequenced plasma cell-free RNA from 39 patients with pancreatic cancer and 39 matched controls on both platforms. After filtering, Ultima-Genomics retained more mature microRNA reads, whereas Illumina achieved slightly higher enrichment efficiency and mapping rates. Despite these technical differences, both platforms produced concordant expression profiles, with strong cross-platform correlations for shared microRNAs and clear separation of cases and controls within each dataset. Differential expression analysis identified 14 significant microRNAs on both platforms with concordant directions of change, most of which are supported by pancreatic cancer databases. Pathway enrichment analysis highlighted signaling pathways implicated in pancreatic cancer, supporting the biological relevance of both shared and platform-specific signatures. These findings indicate that both Illumina and Ultima Genomics platforms are suitable for plasma small RNA profiling and capture biologically relevant signals in pancreatic cancer.