While it is commonly observed that congenital malformations (CM), birth weight and neuroblastoma (NB) often co-occur clinically, there is a scarcity of studies investigating their relationship.

Liver diseases, including hepatitis B virus (HBV) infection, cirrhosis, and hepatocellular carcinoma (HCC), represent significant global health challenges with limited non-invasive biomarkers for early detection. Extracellular vesicles (EVs), enriched with disease-specific RNAs, offer a promising avenue for biomarker discovery. This study characterized EV-derived long non-coding RNAs (lncRNAs) across various stages of liver disease and explored their mechanistic roles. Serum EVs were isolated from 24 participants (5 healthy controls, 5 chronic hepatitis B patients, 5 liver cirrhosis patients, 4 hepatocellular adenoma patients, and 5 HCC patients) using ultracentrifugation and validated by transmission electron microscopy, nanoparticle tracking analysis, and Western blot. High-throughput transcriptome sequencing systematically analyzed RNA expression profiles in EVs across clinical stages, identifying 133 significantly differentially expressed lncRNAs in the HCC group. Multi-step screening and time-series analysis revealed 10 core lncRNAs associated with HCC progression, and a lncRNA-miRNA-mRNA regulatory network (62 nodes, 68 edges) was constructed. Functional enrichment analysis demonstrated involvement in cell proliferation regulation, transmembrane ion transport, cytosol/plasma membrane localization, protein binding, and autophagy/MAPK pathways, while PPI network analysis identified 10 hub genes (e.g., NTRK2, KCNJ10). Validation in an independent plasma cohort confirmed consistent expression patterns of core lncRNAs and downstream genes. The identified HCC-specific lncRNA biomarkers and regulatory network provide theoretical support for developing novel non-invasive liquid biopsy approaches, holding significant promise for early diagnosis and clinical management of HCC.

This network meta-analysis (NMA) aimed to indicate the most effective first-line therapeutic options for advanced EGFR-mutated NSCLC, particularly considering their specific clinicopathological characteristics.

To compare the risks of CBC and OC following a diagnosis of PBC in carriers of founder germline PV's of BRCA1 gene (c.5266dup and c.4035del), and to evaluate the impact of age at PBC diagnosis on subsequent CBC and OC risks.

In this study, we analyzed publicly available scRNA-seq data from primary tumor tissues of 10 Retinoblastoma (RB) patients to explore tumor microenvironment (TME) heterogeneity. Our findings revealed distinct subpopulations of cone precursor (CP) cells, with higher proportions in invasive RB. Differential gene expression and pathway analysis highlighted functional diversity among CP subpopulations, with CP4 showing elevated TGF-β signaling in invasive RB. Cell-cell interaction analysis further identified rewiring of communication networks, with increased fibroblast-CP interactions in invasive tumors. Bulk RNA-seq identified two molecular subtypes, with subtype 1 showing an immunosuppressive TME. Finally, DOK7 was identified as a key gene associated with invasion, with functional assays confirming its role in promoting tumor progression. These results provide valuable insights into RB progression and potential therapeutic targets.

Bladder cancer (BCa) presents a significant clinical challenge, with a pressing need for biomarkers to predict prognosis and guide immunotherapy. Aly/REF export factor (ALYREF) is a key regulator in various cancers, but its role in bladder cancer (BCa) remains unclear. This study aimed to investigate the clinical significance of ALYREF and its association with immunotherapy response and metastatic potential in BCa. We integrated pan-cancer bioinformatic analyses with clinical validation using RT-qPCR, immunohistochemistry, and Western blot on patient samples. We assessed ALYREF's correlation with clinicopathological features, survival, and response to immune checkpoint inhibitors (ICIs). Circulating tumor cell (CTC) counts were analyzed to link ALYREF to metastatic potential. Functional roles in migration and invasion were validated in vitro using the T24 cell line. ALYREF was significantly upregulated in BCa tissues, correlating with higher tumor grade and poorer overall survival. Paradoxically, high ALYREF expression was also associated with a better response to ICI therapy. Furthermore, elevated ALYREF levels in tumors corresponded to increased CTC counts. In vitro experiments confirmed that ALYREF promotes BCa cell migration and invasion. ALYREF is a dual-role biomarker in BCa. Its overexpression signifies aggressive tumor biology and metastatic risk yet also predicts a favorable response to immunotherapy. ALYREF holds promise for refining patient stratification and personalizing BCa treatment.

Revealing new lncRNA-disease associations (LDAs) is necessary to decipher pathological mechanisms and find new clues of diagnosis and therapy for complex diseases. However, experimental methods for LDA identification need a significant amount of time and cost. Here, we introduce a novel deep learning-based method, LDA-CAMF, to infer LDA candidates. LDA-CAMF first designs a cross-attention mechanism to dynamically decode high-order interdependencies between lncRNAs and diseases, presents a multi-level feature fusion strategy to aggregate hierarchical node representations learned from different layers, and then fuse the original features and the optimized representations to enhance the model expressive ability, finally captures novel LDAs using XGBoost. In comparison with six state-of-the-art methods (SDLDA, LDNFSGB, IPCARF, LDASR, LDA-VGHB, and GEnDDn), LDA-CAMF computed the highest AUCs of 0.9632 and 0.9759, and the best AUPRs of 0.9369 and 0.9783 on lncRNADisease and MNDR under 5-fold cross validation, respectively. Under "cold-start" scenarios for lncRNAs and diseases, LDA-CAMF outperformed the above six baselines under most conditions. Five ablation studies further validated better predictive performance of LDA-CAMF. Visualization of LDP feature distributions also demonstrated the effectiveness of the proposed LDP feature learning strategy. Case studies elucidated that HNF1A-AS1 and BCYRN1 associated with prostate cancer, and HAR1A linked with diabetes. We forecast that LDA-CAMF assists in biomarker identification and mechanism investigation of complex diseases.

Gastric cancer (GC) is a leading cause of cancer-related death, with poor prognosis due to metastasis. Despite improvements in early detection and treatment, effective therapies for metastatic GC are limited. Studies suggest that microRNAs play a key role in GC progression and metastasis. The expression of miR-5095, CEACAM5 and EMT-related proteins were determined by RT-qPCR and western blotting. The effects of miR-5095 on GC cell proliferation were assessed using CCK-8 and EdU assays, while the impact of miR-5095 on GC cell migration and invasion was evaluated using transwell assays. Bioinformatic tools were used to predict potential target genes of miR-5095, and the interaction between miR-5095 and CEACAM5 was confirmed through dual-luciferase reporter assays. Additionally, the expression of CEACAM5 was reversed by overexpressing plasmids to verify whether miR-5095 exerts its effects through CEACAM5. The in vivo effects of miR-5095 on tumor growth and metastasis were studied using a xenograft mouse model. miR-5095 expression was significantly reduced, and CEACAM5 expression was elevated in GC tissues and cell lines compared to adjacent normal tissues and cells. In vitro, miR-5095 overexpression notably inhibited CEACAM5 expression and suppressed GC cell proliferation, migration, invasion, and EMT in AGS and HGC-27 cells. Moreover, luciferase reporter assays confirmed that miR-5095 directly targets CEACAM5. The inhibitory effects of miR-5095 on GC cells were reversed by CEACAM5 overexpression. In vivo, miR-5095 significantly inhibits the proliferation and metastasis of gastric cancer.

The effects of therapeutic drugs that inhibit female hormone functions on uterine leiomyomas (ULMs) vary among individuals. ULMs are classified into two subtypes with different histological compositions: MED12 mutation-positive and -negative ULMs. The present study was undertaken to investigate the tumor regression effects of female hormone withdrawal and a selective progesterone (P) receptor modulator (ulipristal acetate, UPA) in the ULM subtypes using a xenograft mouse model. Smooth muscle cells (SMCs) isolated from each ULM subtype were transplanted and treated with estrogen (E) and P for 4 weeks to form the xenograft tumor. They were then divided into four groups: continued EP treatment (E(+)P(+)), EP withdrawal (E(-)P(-)), only P withdrawal (E(+)P(-)), and EP + UPA. Tumors were harvested 8 weeks after transplantation. In both subtypes, they were reduced in size in the E(-)P(-) compared with the E(+)P(+) and further reduced in the EP + UPA. Histological analysis showed that SMCs shrank in the E(-)P(-) and disappeared in the EP + UPA. Gene ontology and pathway analyses of the differentially expressed genes of the xenograft tumors suggested that regression occurred by different mechanisms in the two subtypes. Our results also suggested, for the first time, that UPA caused SMC death by a mechanism other than progesterone receptor inhibition.

Breast cancer (BC) is the most prevalent malignancy among women, and the steadily increasing disease burden has garnered considerable global attention. Post-translational modifications (PTMs) are critical in the initiation and progression of BC. This study aimed to elucidate the associations between diverse PTMs and the prognosis of patients with BC. We collected genes associated with multiple PTMs and evaluated the activity of each PTM using GSVA. We aggregated PTM scores to derive the PTMS and identified differentially expressed genes between the high- and low-PTMS groups. A PTM-related gene signature (PTMRS) was developed based on the optimal combination among 117 machine learning models, and its predictive performance was benchmarked against other published signatures. In addition, we investigated the associations between PTMRS, tumor immunity, and treatment response. Gene expression across different cell types was evaluated using single-cell and spatial transcriptomic analyses. Gene expression levels in cancerous and paired adjacent noncancerous tissues were validated by PCR. The results of GSVA showed that most of the PTMs were dysregulated in cancer. Tumor immunity levels were elevated in the low-PTMS group compared with the high-PTMS group. The PTMRS comprised five genes: SLC27A2, TNFRSF17, PEX5L, FUT3, and COL17A1. The predictive performance of the PTMRS exceeded that of the clinical profile and 14 other published gene signatures. Patients in the high-PTMRS group exhibited poorer prognosis and reduced anti-tumor immunoreactivity. In addition, patients in the low-PTMRS group showed improved responses to chemotherapy and immune checkpoint inhibitors. Spatial transcriptomics analysis revealed that SLC27A2 exhibited higher expression in malignant spots, whereas COL17A1 and TNFRSF17 showed lower expression in malignant spots. SLC27A2 mRNA expression was elevated in tumor tissues relative to adjacent noncancerous tissues, whereas the mRNA expression levels of the other four genes were decreased. This study reveals the important role of PTMs in BC prognosis and provides new perspectives for the prognostic assessment of BC patients as well as personalized treatment.

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder caused by inactivating mutations in TSC1 or TSC2 gene, leading to mTORC1 hyperactivation. However, mTORC1-independent mechanisms in this disorder remain poorly understood. In the study, excess glycogen storage was found in Tsc1-/- cells, Tsc1+/- and Tsc1c.2500-2503delAACA mice, as well as in Tsc2-/- cells, Tsc2+/- and Tsc2c.1113delA mice, with more pronounced accumulation in models with TSC2 defects. Mechanistically, the deficiency of TSC1 or TSC2 gene caused redundant uncomplexed-TSC2 or TSC1 protein, respectively. Strikingly, only uncomplexed-TSC1 downregulated the histone demethylase KDM5A, which in turn increased H3K4me3 levels at the METTL3 promoter to enhance its expression. The upregulated m6A "writer" protein METTL3 cooperated with the "reader" protein IGF2BP2 to stabilize GYS2 mRNA, causing the upregulation of GYS2 resulting in the glycogen storage. Thus, our study uncovered a novel mTORC1 independent pathway (TSC1-KDM5A-METTL3-IGF2BP2-GYS2) that underlies the excess glycogen storage, and that synergy of mTORC1-dependent and independent pathways leads to the more pronounced glycogen storage with TSC2 defects compared to those with TSC1 defects, reflecting the more severer clinical phenotypes in TSC patients with TSC2 mutations. Importantly, the restoration of glycogen homeostasis and significant amelioration of liver lesion in TSC2 defect models after the combination treatment of pharmacological inhibitors targeting mTORC1 and METTL3, unveil a potential clinic intervention for TSC patients to whom mTORC1 inhibitors are less effective or even ineffective.

Formalin-fixed paraffin-embedded (FFPE) samples are the gold standard for tissue preservation in clinical and research settings. Current single-cell chromatin accessibility technologies cannot resolve cell-type-specific epigenetic profiles in FFPE tissues due to extensive DNA damage. We present scFFPE-ATAC, a high-throughput single-cell chromatin accessibility assay for FFPE samples that integrates an FFPE-adapted Tn5 transposase, ultra-high-throughput DNA barcoding (>56 million barcodes per run), T7 promoter-mediated DNA damage repair, and in vitro transcription. We benchmark scFFPE-ATAC on FFPE mouse spleen and validate its performance against fresh tissue. We apply it to human lymph node samples archived for 8-12 years and to lung cancer FFPE tissues, revealing distinct regulatory trajectories between tumor center and invasive edge. Analysis of archived follicular lymphoma and transformed diffuse large B-cell lymphoma samples identifies relapse- and transformation-associated epigenetic dynamics. scFFPE-ATAC enables retrospective, spatial, and mechanistic epigenetic studies in long-term archived specimens.

Epigenetic dysregulation, including accumulation of Histone H3 lysine 27 acetylation (H3K27ac), is a hallmark of pVHL-deficient clear cell Renal Cell Carcinomas (ccRCCs). Using an in vivo positive selection ORF screen in poorly tumorigenic pVHL-proficient cells and mechanistic studies in pVHL-deficient cells, we discovered that the aspartate (Asp) and glutamate (Glu) transporter, SLC1A1/EAAT3, is a metabolic dependency in ccRCC. pVHL loss promotes Hypoxia Inducible Factor (HIF)-independent SLC1A1 expression via H3K27ac dysregulation. SLC1A1 inactivation, genetically or pharmacologically, depletes Asp/Glu-derived metabolites (e.g., Tricarboxylic acid cycle and nucleotide intermediates), impedes ccRCC growth, and sensitizes ccRCCs to anti-metabolite drugs (e.g., glutaminase blockers). In human tumors, higher SLC1A1 expression is associated with reduced immune infiltration, oncogenic metabolic programs, and advanced stage/metastatic disease. Finally, in ccRCC animal models, SLC1A1 inactivation diminishes lung metastasis and the outgrowth of established renal tumors. Altogether, our studies credential SLC1A1 as an actionable, HIF-independent, metabolic dependency in pVHL-deficient ccRCCs.

Actionable genomic alterations (AGA) are rare in large cell neuroendocrine carcinoma (LCNEC), and outcomes with small molecule inhibitors (SMI) are not well studied. We evaluated SMI response in LCNEC with AGAs, and also assessed molecular testing rates using the nationwide Netherlands Cancer Registry (NCR).

We performed spatial transcriptomics of estrogen receptor (ER)-negative, ER-low, and ER-high tumor regions of breast cancers that were intermediate (10%-60%) ER-positive by immunohistochemistry to better understand the intratumor heterogeneity in ER expression and to elucidate whether cells of different molecular subtypes (ie, Luminal A [LumA], Luminal B [LumB], human epidermal growth factor receptor 2-enriched, or Basal-like) can coexist in the same tumor.

Gastric cancer (GC) is a significant global health challenge, often diagnosed late, limiting treatment success. Early detection through biomarkers is critical for better outcomes. This study developed a noninvasive, cost-effective tool using circulating cell-free DNA (cfDNA) sequencing and advanced modeling for early GC detection.

Observational epidemiologic studies on the association of anthropometric traits and colorectal cancer (CRC) survival provide inconsistent results, and potential limitations prohibit the investigation of causality. We examined the associations between seven genetically predicted anthropometric traits [height, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-hip circumference ratio, birth weight and body fat percentage] and CRC-specific mortality among CRC cases using two-sample Mendelian randomization (MR).

Myocardial infarction (MI) and cancer are major global public health challenges. Research indicates that they share common risk factors and that physiological changes following MI may affect cancer incidence and progression. However, evidence defining the independent relationship between these conditions is still limited.

MicroRNAs (miRNAs) play crucial roles in cancer progression, invasion, and response to treatment, particularly in regulating anticancer drug resistance and sensitivity. Identifying potential human miRNA-drug associations (MDAs) that manifest as resistance or sensitivity relationships offers valuable insights for cancer treatment and drug development. With the growing availability of biological data, computational methods have emerged as powerful tools to complement experimental approaches. However, limited attention has been paid to computational prediction of MDAs. Furthermore, existing approaches typically rely on known MDA information, overlooking the valuable insights available from multi-source data related to miRNAs and drugs. In this study, we present a multi-view fusion-based graph convolutional network with attention mechanism (MGCNA) to predict miRNA-associated drug resistance/sensitivity. Specifically, MGCNA integrates macro- and micro- level information of miRNAs and drugs to construct multi-view node features from different perspectives. The proposed multi-view graph convolutional network (GCN) encoder obtains miRNA and disease features from different views and learns adaptive importance weights of the embedding using an attention mechanism. Extensive experiments on manually curated benchmark datasets demonstrate that MGCNA outperforms existing baseline methods. Case studies of two common drugs further establish MGCNA's effectiveness in discovering novel MDAs.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide, with cases expected to rise 60% by 2030, especially in Asia. Metastatic CRC (mCRC) has a poor 5-year survival rate of 14%, posing a major treatment challenge. Tumors with DNA mismatch repair deficiency (dMMR) and a high level of microsatellite instability (MSI-H) respond well to immune checkpoint inhibitors (ICIs), shifting treatment strategies. This systematic review and meta-analysis evaluate Pembrolizumab (PEM), Nivolumab (NIV), and Nivolumab plus Ipilimumab (NIV + IPI) for their promising antitumor efficacy in MSI-H/dMMR mCRC.