Colorectal carcinoma (CC) is a rare disease in the pediatric population, with an annual incidence of 1 in 10 million adolescents, and it accounts for approximately 1% of pediatric solid neoplasms. It is the most common primary gastrointestinal malignancy in children with the vast majority of CCs being adenocarcinoma (CA). Unfortunately, the proportion of poorly differentiated, mucinous type, signet-ring cell containing carcinomas is higher in younger patients than in adults. Moreover, due to the low awareness of the disease, diagnosis is usually delayed until advanced stages, resulting in an extremely poor prognosis. Surgery is the only curative modality for localized CAs, whereas adjuvant chemotherapy is the standard of care for patients with stage III cancer to eradicate micro-metastases.

We have recently isolated, and determined the structure of a cycloheptapeptide, Mallotumide A from the Mallotus spodocarpus root extract. Here we reported the anti-cancer activity of Mallotumide A in highly invasive colon cancer, HCT116 and triple-negative breast cancer, MDA-MB-231 cell lines. Mallotumide A, at concentrations of 1 nM and 10 nM, completely inhibited the clonogenic growth, migration, and invasion of HCT116 and MDA-MB-231 cells, respectively. While the compound interfered with cell cycle progression without inducing apoptosis, exposure to 10 nM Mallotumide A for 48 h reduced the expression of two key lipogenic enzymes, ACC1 and FASN, by approximately 50% in both cell lines. The downregulation of ACC1 and FASN was accompanied by a 50% reduction in intracellular triglyceride levels while the cholesterol levels remained unaffected. Mallotumide A also moderately decreased AMP-activated protein kinase (AMPK) and ATP levels. Extracellular flux analysis revealed that acute exposure of both cancer cell lines to 1 nM and 10 nM Mallotumide A for 24 h markedly lowered the oxygen consumption rate. This was accompanied by reductions in basal and ATP-linked respiration, maximal respiration, and mitochondrial spare respiratory capacity. Mallotumide A also decreased the extracellular acidification rate, affecting both basal glycolysis and the glycolytic reserve. These findings suggest that the anti-cancer effects of Mallotumide A are associated with disruptions in cellular energy metabolism and the de novo lipogenesis pathway in cancer cells. This study underscores the potential of Mallotumide A as a novel anti-cancer agent.

Early cancer diagnosis is crucial to improving disease prognosis. Although several studies have investigated the relationship between socioeconomic position (SEP) and stage at diagnosis, there is limited evidence from contexts with highly fragmented health systems and pronounced socioeconomic inequalities. This study analyzed the association between SEP and stage of cancer diagnosis. Data were obtained from the EquityCancer-LA baseline study. The sample included patients aged 18 or older with a confirmed cancer diagnosis within the 12 months prior to participation. Cancer stage was determined by the oncology committees of participating healthcare centers and logistic regression models were used to assess the association between SEP and cancer stage at diagnosis. A total of 343 individuals participated in the study, 39.1% of whom were diagnosed at a late stage. Two SEP indicators were associated with this outcome. After adjusting for covariates, participants without formal income had higher odds of late-stage diagnosis (OR = 2.14; 95% CI 1.02-4.53), and those who were non-head of household (OR = 1.83; 95% CI 1.11-3.02). When adjusting for all SEP variables, only non-head of household condition remained significantly associated (OR = 1.77; 95% CI 1.07-2.96). These results show that disadvantaged SEP was associated with higher odds of late-stage cancer diagnosis. The findings suggest the need for strategies that promote early diagnosis and address the socioeconomic inequities identified in this study.

Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, and morphological descriptors with deep features derived from an extended EfficientNetB0. A transformer-based attention fusion strategy is adopted to fuse these heterogeneous representations, facilitating robust multi-scale feature learning. To even better accommodate adaptability and curtail catastrophic forgetting, the model is trained with an adaptive incremental learning approach with stage-wise data augmentation. The suggested method is trained on the LC25000 dataset and tested on two public, independent datasets, NCT-CRC-HE-100K and HMU-GC-HE-30K, showing consistent performance with accuracies of 99.87%, 99.07%, and 98.4%, respectively. These findings are affirmations of the framework's generalizability, scalability, and clinical applicability in multi-class histopathological image classification. All source code and dataset access instructions are publicly made available to encourage reproducibility and extension.

Mitochondrial permeability transition (MPT)-driven necrosis is associated with kidney renal clear cell carcinoma (KIRC), but its role in prognosis remains unclear. This study develops a prognostic model for KIRC outcomes using MPT-driven necrosis-related genes (MPTDNRGs). Differentially expressed genes (DEGs) from TCGA-KIRC samples were analyzed and categorized based on MPTDNRGs scores. Three key genes-IL2RA, CD7, and CXCL13-were identified as significant prognostic markers and used to construct a risk model, validated through public datasets and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The risk score and age were the independent prognostic factors. A nomogram incorporating these factors demonstrated good clinical utility. The high-risk group was enriched in immune-related pathways, such as systemic lupus erythematosus, while the low-risk group showed enrichment in metabolism-related pathways, including butanoate metabolism. Significant differences in 25 immune cells were observed between the risk groups, with the high-risk group exhibiting higher TIDE scores, suggesting a greater likelihood of immune escape. Additionally, a ceRNA network revealed complex interactions, such as CXCL13-hsa-miR-670-5p-AL121985.1, and predicted 25 transcription factors for key MPTDNRGs. This study presents a novel prognostic model for KIRC based on three MPTDNRGs, offering valuable insights into KIRC prognosis and potential therapeutic targets.

Ecotropic viral integration site 5 (EVI5), a member of the Tre-2/Bub2/Cdc16 (TBC) domain-containing protein family, has been implicated in the initiation of various cancers. However, its precise role in lung adenocarcinoma (LUAD) remains unclear. This study aimed to elucidate the function of EVI5 in LUAD, with a focus on its regulation of the immune checkpoint molecule PD-L1. In the present study, we found that EVI5, Rab11 and PD-L1 were significantly overexpressed in LUAD tissues compared to adjacent normal tissues. In vitro experiments demonstrated that EVI5 knockout suppressed the expression of Rab11 and PD-L1 in LUAD cells. Notably, EVI5 overexpression promotes the expression of Rab11 and PD-L1 in LUAD cells. EVI5 was also shown to interact with Rab11 to upregulate PD-L1 expression in LUAD cells. Mechanistically, our findings identify a novel EVI5-Rab11-PD-L1 axis and suggest that EVI5 is a potential regulator of the tumor immune microenvironment, which may have implications for the efficacy of immunotherapy.

Precision therapy for glioma remains a major challenge due to tumor heterogeneity. The Origin Recognition Complex Subunit 6 (ORC6) is a crucial regulator of DNA replication initiation. This study aims to investigate the expression of ORC6 in gliomas and its relationship with survival rates and malignancy, while screening potential drugs targeting its functional network. By integrating multiple bioinformatics approaches with structure-based virtual screening, retrospective RNA sequencing data analysis was performed using patients from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. A protein-protein interaction (PPI) network was constructed from ORC6-coexpressed genes to identify core hubs. Molecular docking was employed to screen a library of natural compounds and known drugs against these hub targets. Research has revealed that ORC6 is significantly upregulated in high-grade gliomas, with its elevated expression associated with poor survival outcomes and immune inflammatory responses. Network analysis identified five core hub genes (ORC1, ORC2, MCM2, MCM6, CDC45) central to DNA replication. Molecular docking revealed that several compounds, including the natural flavonoid Baicalein and the FDA-approved drug Palbociclib, exhibited high binding affinity to these hub targets. ORC6 represents a highly promising novel target for precision therapy in glioma. Potential approaches to target this pathway include disrupting the ORC6-replication axis using existing drugs (such as palbociclib) or natural products (such as baicalin).

Methyltransferase-like protein 3 (METTL3) functions as the primary "writer" of N6-methyladenosine (m6A), the most prevalent internal mRNA modification in eukaryotes. Functionally, METTL3 regulates a broad spectrum of cellular processes, including cell cycle progression, proliferation, apoptosis, invasion, migration, and differentiation. In colorectal cancer, METTL3 expression is frequently upregulated and has been correlated with poor prognosis. Therefore, this review attempts to organize and describe the main molecular and cellular mechanisms through which METTL3 drives colorectal cancer. Mechanistically, METTL3 contributes to tumor growth, metastatic dissemination, and the development of resistance to chemotherapy and radiotherapy. Special emphasis is placed on the clinical relevance of METTL3, not only as a biomarker, but also as a potential therapeutic target. Less explored topics are also addressed, such as the interaction between METTL3 and gut microbiota. Altogether, this review underscores the multifaceted role of METTL3 in colorectal cancer and the importance of further investigation to translate these insights into clinical applications.

Uterine sarcoma is an aggressive malignancy that is difficult to distinguish from benign leiomyomas pre-operatively. This study aimed to identify circulating biomarkers to improve differential diagnosis and prognostication. The serum and tissue levels of growth differentiation factor-15 (GDF15), progranulin (PGN), and osteopontin (OPN) were measured in patients with uterine sarcoma (n = 38) and leiomyoma (n = 67). The levels were correlated with diagnosis and patient survival. The serum and tissue levels of GDF15 and OPN were significantly higher in uterine sarcomas than in leiomyomas (p < 0.001). High GDF15 levels were associated with significantly poorer progression-free survival (PFS) (p < 0.001). In a multivariate analysis including established markers cancer antigen 125 (CA125) and lactate dehydrogenase (LDH), only GDF15 remained an independent predictor of PFS [hazard ratio (HR) 3.01, 95% confidence interval (CI) 1.04-8.67, p = 0.042]. The prognostic power of GDF15 was confirmed by an analysis that excluded carcinosarcomas. GDF15 and OPN are promising biomarkers for pre-operatively differentiation of uterine sarcoma from leiomyoma. Furthermore, GDF15 is a strong, independent prognostic factor for PFS in patients with uterine sarcoma and has the potential to improve diagnostic accuracy and clinical management.

Bladder tumours (BTs) pose significant clinical challenges due to their high recurrence rates and risk of progression to invasive malignancies, which emphasises the need for early and accurate detection. Magnetic resonance imaging (MRI), with its superior soft tissue contrast, is a potential modality for BT detection. To analyse the MRI scans, artificial intelligence (AI) models are increasingly being leveraged. However, these models are often limited by a scarcity of annotated datasets, challenges in pixel-level tumour prediction, and insufficient transparency in predictions. This study introduces the Explainable and Likelihood-Aware AI (ELAAI) framework, designed to address these limitations. Trained solely on annotated normal bladder MRI scans, ELAAI integrates three novel modules: MFA-Net, a robust multi-scale feature aggregation network for bladder segmentation; a refinement step employing adaptive tolerance technique to enhance segmentation of irregularities; and a single-step likelihood prediction network (SLIP-Net), which is a vision transformer with a novel multi-scale deterministic uncertainty (MSDU) head for tumour likelihood prediction. Rigorous evaluation against state-of-the-art (SOTA) models highlights ELAAI's superior performance, enhancing transparency, and reliability in clinical settings by fostering trust in AI-assisted decision-making.

Fibroblast-driven deposition of extracellular matrix, particularly type III collagen, is linked to poor outcomes in patients with liver fibrosis, fibrotic solid tumors such as hepatocellular carcinoma and pancreatic cancer as well as in patients with other fibrotic diseases. The pro-peptide of type III collagen (PRO-C3) serves as a serum biomarker of active collagen formation. PRO-C3 is elevated and prognostic in patients with liver fibrosis and pancreatic cancer, suggesting that targeting mechanisms behind PRO-C3 elevation could benefit these individuals. In this study, we sought to identify genetic variants associated with circulating PRO-C3 levels as potential therapeutic targets for fibrotic diseases and cancer. A genome-wide association study (GWAS) of the PERF cohort (n = 4968) identified variants in the gene TGFBI (BIGH3/βigH3), a known risk factor for tumor fibrosis, as associated with elevated PRO-C3. Further analyses of serum samples from patients with pancreatic ductal adenocarcinoma and liver fibrosis confirmed significant correlations between PRO-C3 and BIGH3 levels. Functional studies in a fibroblast model demonstrated that BIGH3 induces PRO-C3 in a dose-dependent manner, which was blocked by an anti-BIGH3 antibody, indicating a causal role. BIGH3 expression was observed in macrophages and fibroblasts, especially under TGFβ stimulation, positioning BIGH3 as a downstream mediator of TGFβ-induced fibrosis. Finally, blocking BIGH3 in a TGFβ-induced fibrosis model led to reduced PRO-C3 levels, demonstrating that BIGH3 is local mediator of pathological TGFβ signaling. This highlights that BIGH3 may be implicated in TGFβ-induced fibrosis and underscore the potential for treatment of pathological fibrotic processes by inhibiting BIGH3 in patients with elevated PRO-C3 levels.

Acromegaly, caused by growth hormone-secreting pituitary tumors, often causes significant challenges in its management due to poor surgical outcomes and resistance to pharmacological treatment. The present study aims to explore the expression of Filamin A (FLNA), a cytoskeletal protein involved in somatostatin receptor signaling, and its clinical relevance in acromegaly. We conducted immunohistochemical (IHC) study on 34 GH-secreting pituitary tumors to evaluate FLNA expression intensity and its associations with somatostatin receptors (SSTR2, SSTR5), E-Cadherin, tumor characteristics obtained through imaging studies, and pharmacological treatment responses. Our findings revealed a 100% FLNA positivity rate, with moderate to strong FLNA expression correlating significantly with SSTR5 expression and the presence of suprasellar tumor extension, indicating a potential role in tumor invasiveness. Moreover, patients with macrodenomas presented significantly higher FLNA intensity compared to the ones with microadenomas. FLNA expression showed no significant association with SSTR2, E-Cadherin, surgical cure rate or first-generation somatostatin receptor ligand (fgSRL) responses. However, the series of patients treated with Pasireotide (n = 4) demonstrated a trend suggesting better biochemical control with higher FLNA expression. In conclusion, our results suggest that FLNA may be associated with tumor invasiveness in GH-secreting pituitary tumors. While data on Pasireotide-treated patients are exploratory, further studies are needed to assess FLNA's potential as a treatment response marker in acromegaly.

Robot-assisted radical prostatectomy in kidney transplant recipients presents unique technical challenges owing to anatomical alterations. However, Hugo™ robotic-assisted surgery system, with its independently mounted robotic arms, may offer advantages in such complex cases. Herein, we report the first case of robot-assisted radical prostatectomy using a Hugo robotic-assisted surgery system in a post-kidney transplant patient.

Cholangiocarcinoma is a very deadly epithelial cell cancer with poor clinical outcome. Cellular senescence plays a vital role in the oncogenesis and the aggressiveness of cholangiocarcinoma. Integrative machine learning procedure including 10 methods (random survival forest, elastic network, Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modelling, and survival support vector machine) was performed to construct a cellular senescence-related signature (CSS) for cholangiocarcinoma. Cellular experiment was used to verify the biological function of hub gene. The optimal prognostic CSS developed by Lasso method served as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in cholangiocarcinoma, with the AUC of 1-, 3-, and 5-year ROC curve being 0.957, 0.929 and 0.928 in TCGA cohort. Low CSS score indicated with a lower tumor immune dysfunction and exclusion score, lower tumor microsatellite instability score, lower immune escape score, lower MATH score, and higher tumor mutation burden score in cholangiocarcinoma. Down-regulation of EZH2 inhibited the proliferation, colony and promoted apoptosis of cholangiocarcinoma cell. Integrative machine learning analysis was performed to construct a novel CSS in cholangiocarcinoma. This CSS acted as an indicator for predicting the prognosis and immunotherapy benefits of cholangiocarcinoma patients.

Acute myeloid leukemia is an aggressive hematological malignancy frequently complicated by coagulation disorders, including thrombosis and hemorrhage, which contribute to poor outcomes. Here, we identify lactate-driven histone lactylation as a mechanism promoting thrombosis in acute myeloid leukemia. We demonstrate that hexokinase 2-mediated glycolysis in leukemic cells leads to lactate accumulation, which enhances histone H3 lysine 18 lactylation and upregulates plasminogen activator inhibitor-1 expression, impairing fibrinolysis. Lactate released by acute myeloid leukemia cells is internalized by vascular endothelial cells via monocarboxylate transporter 1, amplifying plasminogen activator inhibitor-1 expression and thrombotic risk. Inhibition of hexokinase 2-mediated lactate production or monocarboxylate transporter 1-mediated lactate uptake attenuates thrombosis. Our findings reveal a critical link between tumor metabolism, epigenetic modifications, and coagulation dysfunction in acute myeloid leukemia.

Macroautophagy/autophagy is a stress-responsive lysosomal catabolic pathway that promotes cellular homeostasis and tumor cell survival, but its role in breast cancer progression and metastasis remains unclear. Here, we show that a brain-specific serine/threonine protein kinase, BRSK2, a marker of aggressive metastatic disease in breast cancer patients, is crucial in regulating autophagy. BRSK2 is overexpressed in aggressive cancer and is associated with reduced disease-specific survival. BRSK2 also regulates basal autophagy and activates AKT, STAT3, and NF-κB-mediated cancer cell survival pathways. In addition, BRSK2 overexpression increases the levels of inflammatory cytokines and chemokines in breast cancer cells. Downregulation of BRSK2 using specific siRNAs or the BRSK2 kinase small-molecule inhibitor GW296115 markedly reduced nutrient-deprivation stress-mediated autophagy, cell growth, and metastatic potential, and enhanced breast cancer cell apoptosis. Endogenous BRSK2 is associated with the Vps34-class III PI3K-Beclin-1-ATG14 autophagy signaling complexes that could protect cancer cells from nutrient-deprivation stress. Our findings demonstrate the key role of the BRSK2-mediated protective autophagy and cell growth and survival under nutrient deprivation stress via survival signals, e.g., PI3K/AKT or STAT3-NF-kB, in aggressive breast cancer cells.

Understanding the characteristics of the tumor microenvironment (TME) associated with aggressive prostate cancer (PCa) is essential for accurate diagnosis and treatment. We interrogated spatially resolved multi-omics data to find molecular stratifiers of aggressive PCa. We report an aggressive prostate cancer (APC) gene expression signature predictive of increased risk of relapse and metastasis in a cohort of 1,588 patients. Further, we present a chemokine-enriched-gland (CEG) signature specific to non-cancerous prostatic glands from patients with aggressive cancer. The CEG signature is characterized by upregulated expression of pro-inflammatory chemokines, club-like cell enrichment, and immune cell infiltration of surrounding stroma. The activity of both signatures is correlated with reduced citrate and zinc levels and loss of normal prostate secretory gland functions. In summary we report that an increased inflammatory status linked to chemokine production, club-like cell enrichment, and metabolic changes in normal-appearing prostatic glands is associated with the subsequent development of aggressive PCa.

Cancer remains one of the leading causes of global mortality, with lung, colon, skin, and breast cancers contributing significantly to the disease burden. Accurate and timely classification of histopathological images is critical for effective diagnosis and treatment planning. However, existing deep learning models for histopathology often achieve strong results but remain limited to single-cancer classification, lack generalizability across datasets, and provide little transparency for clinical use. To address these gaps, we propose CancerDet-Net, a comprehensive unified framework capable of classifying nine histopathological subtypes across four major cancer types. CancerDet-Net integrates separable convolutional layers, Vision Transformer (ViT) blocks with local-window sparse self-attention, and a Hierarchical Multi-Scale Gated Attention Mechanism (HMSGA), combined through Cross-Scale Feature (CSF) Fusion. Unlike prior approaches, our model not only achieves top-performing accuracy (98.51%) but also incorporates explainable AI (XAI) visualizations and is deployed via both a web-based platform and Android app for real-time clinical use. This combination of multi-cancer generalization, interpretability, and deployment readiness establishes CancerDet-Net as a distinctive contribution to AI-driven digital pathology.

The dataset (Elsafty_Reports_of_Myeloid_Neoplasms_2024) consists of 14,441 comprehensive clinicopathologic correlations (CPCs) for myeloid neoplasms (MN) with corresponding laboratory results. Specialized online platform utilizing these laboratory results automatically generated the diagnostic and prognostic CPCs. These results include complete blood counts (CBC), peripheral blood smear (PBS) findings, blast/promyelocyte count with dysplasia screening by flow cytometry, and molecular results by NGS/PCR. Two Hematopathologists, who performed the CBC and PBS review, collected their 10,794 real-world reports of cases served in Egypt with molecular studies performed in the USA and Europe. These newly-diagnosed cases included 243 chronic myeloid leukemia (CML) cases with 257 mutations/aberrations; 4,567 non-CML MN cases with 7,883 Tiers I/II mutations/aberrations, amino acid changes, and allele frequencies; and 5,984 benign/inconclusive cases with negative NGS for myeloid mutations/aberrations in 66 DNA/RNA genes. Additionally, there are 3,647 CPCs with synthetic genomics simulating all new/follow-up CML cases and complex/rare non-CML MN cases. Stringent validation by 51 international professors/consultants/specialists from multiple medical centres confirmed 100% medical and clerical accuracy, referencing the WHO 5th edition and relevant esteemed publications.

Esophageal squamous cell carcinoma (ESCC) is associated with variable clinical outcomes, even among tumours with similar characteristics, complicating treatment. Here, we identify acetyl-CoA carboxylase 1 (ACC1), a key lipogenic enzyme, as a factor influencing this variability. Specifically, ACC1 knockdown in ESCC cells induces histone acetylation, activating c-Fos and C-X-C motif chemokine ligand 8 (CXCL8) transcription, which in turn promotes epithelial-mesenchymal transition (EMT) and enhances migration and invasion via CXCL8-C-X-C motif chemokine receptor 1/2 (CXCR1/2)-mediated PI3K/AKT and MEK/ERK signalling. Additionally, ACC1 knockdown stimulates neutrophil recruitment and neutrophil extracellular trap (NET) formation through CXCL8-dependent paracrine signalling, further enhancing tumour cell migration and invasion. In ESCC xenografts, ACC1 knockdown increases neutrophil infiltration and NET formation, accelerating metastasis. Clinically, low ACC1 expression and high CXCL8 levels are linked to poor prognosis in patients with ESCC, while NET formation further correlates with reduced survival. These findings highlight the ACC1-CXCL8-NET axis as a potential therapeutic target and prognostic marker in ESCC.