Colorectal cancer (CRC) is a leading cause of cancer-related mortality, with stage II and III CRC patients at significant risk of metachronous distant metastasis despite curative resection. While prior studies have developed models to predict specific types of distant metastasis, such as metachronous liver metastasis and peritoneal metastasis, there remains a need for a comprehensive model addressing any distant metastasis in stage II-III proficient mismatch repair (pMMR)/microsatellite stability (MSS) CRC. This study aims to develop a novel prediction model for metachronous distant metastasis in stage II-III pMMR/MSS CRC. We performed a retrospective study of data from 110 hospitalized stage II-III MSS CRC patients who underwent radical resection between June 2017 and February 2024. Univariate and stepwise multivariate logistic regression analyses were conducted to screen predictive factors. The variance inflation factor was employed to investigate multicollinearity among the final predictors. Model performance was evaluated through the receiver operating characteristic curves, calibration curve, and decision curve analysis. Internal validation was performed using 500 bootstrap iterations. The optimal model incorporated 3 key features: pT stage, pN stage, and vascular invasion. The model demonstrated good discriminatory ability with an area under curve of 0.830 (95% CI 0.749-0.911). Using a cutoff value of 0.226, sensitivity and specificity were 0.794 and 0.697, respectively. Internal validation confirmed the model' robustness, with an area under curve of 0.809 (95% CI 0.711-0.907). The calibration curve demonstrated a high level of consistency between the predicted and actual probabilities. Decision curve analysis demonstrated the model's high clinical utility. This nomogram including pT stage, pN stage, and vascular invasion provided a practical tool for predicting metachronous distant metastasis in patients with stage II-III pMMR/MSS CRC undergoing radical resection, exhibiting excellent discrimination, accuracy, and clinical applicability.

Glioblastoma (GBM) is the most common primary malignant intracranial tumor in adults. GBMs with primitive neuronal components (GBM-PNC) represent a rare subtype of GBM, accounting for 0.5% of cases, often characterized by cerebrospinal fluid (CSF) dissemination and extracranial metastasis (ECM). Among the primitive neuronal components (PNC), MYC gene amplification is found in 43% of cases. Once diagnosed, treatment becomes extremely challenging; therefore, early identification and timely intervention are crucial.

Although systemic lupus erythematosus (SLE) and clear cell renal cell carcinoma (ccRCC) are clinically distinct, emerging evidence suggests they may share underlying genetic and immunoregulatory mechanisms. However, the specific molecular basis of their potential comorbidity has not been clearly defined, and no prior study has comprehensively investigated their shared genomic architecture. To fill this critical knowledge gap, we conducted the first integrative genome-wide analysis to identify common susceptibility loci, pleiotropic genes, and convergent biological pathways between SLE and ccRCC. Utilizing large-scale genome-wide association studies summary data from European populations, we employed a combination of methods including LDSC, PLACO, MAGMA, Summary data-based Mendelian randomization , and HyPrColoc, alongside functional annotation and pathway enrichment analyses via FUMA and Metascape. We identified a significant positive genetic correlation between SLE and ccRCC (Rg = 0.287, P = 1.5 × 10-4), along with 11 pleiotropic loci, notably including colocalization signals at 3q26.2 and 11q22.3. We further identified 63 pleiotropic genes, with AKT1, ERBB2, and STAT1 functioning as key network hubs associated with the PI3K/Akt, NF-κB, and JAK-STAT pathways. Immunological colocalization analysis indicated that CD24⁺IgD⁺CD38⁻ B cells and unswitched memory B cells may constitute potential comorbid subpopulations. Summary data-based Mendelian randomization analysis further revealed CASP9 as a promising therapeutic target for comorbidity intervention. Overall, this study identified pleiotropic loci and key hub genes enriched in immune-oncogenic pathways, including PI3K/Akt, NF-κB, and JAK-STAT, and proposed potential therapeutic targets, thereby providing a concrete basis for understanding the comorbidity mechanisms of SLE and ccRCC and informing future precision interventions.

The potential causal relationship between autoimmune diseases (AIDs) and breast cancer (BC) remains a topic of debate. In this study, we conducted Mendelian randomization (MR) analysis between common AIDs and BC using publicly available genome-wide association studies data from the MR Base platform. Initially, we conducted a two-sample MR analysis of 10 AIDs and BC using the inverse variance weighted method. Subsequently, a series of sensitivity analyses were performed to validate the robustness of our findings. Lastly, we utilized the reverse MR analysis to evaluate the potential for reverse causality. Among the 10 types of AID studied, 3 were found to be associated with an elevated risk of BC. These include ankylosing spondylitis (odds ratio [OR]: 1.21; 95% confidence interval [CI]: 1.04-1.41; P = .013), inflammatory bowel disease (OR: 1.05; 95% CI: 1.01-1.09; P = .012), and rheumatoid arthritis (OR: 1.04; 95% CI: 1.01-1.07; P = .023). Additionally, BC was found to be linked with a heightened risk of inflammatory bowel disease (OR: 1.112; 95% CI: 1.022-1.210; P = .014). Our study's findings support a bidirectional causal relationship between AIDs and BC, offering novel insights into the developmental mechanisms underlying the interaction between AIDs and BC.

Emerging evidence from recent pathological investigations has demonstrated that chronic inflammation plays a pivotal role in the pathogenesis of intestinal diseases, including inflammatory bowel disease (IBD), ulcerative colitis (UC), Crohn disease (CD), colorectal adenocarcinoma (CAC), and colorectal cancer (CRC). However, the precise regulatory mechanisms of inflammatory cytokines remain incompletely elucidated, and the causal relationships between inflammatory responses and intestinal diseases require further validation. This study employed a two-sample Mendelian randomization (MR) approach to comprehensively evaluate potential causal associations between 91 circulating inflammatory factors and these 5 intestinal diseases. MR analysis revealed a significant causal relationship between the levels of inflammatory factors C-C motif chemokine 19 and CD40L receptor and the risk of IBD. Furthermore, CD, UC and CRC respectively showed potential causal relationships with inflammatory factors C-C motif chemokine 20, Programmed cell death 1 ligand 1 and interleukin-5. No inflammatory factor showed a causal relationship with CAC. Reverse MR Analysis indicated that the progression of CRC could significantly regulate the expression level of the inflammatory factor Macrophage colony-stimulating factor 1. Unlike most previous studies that merely focused on the association between a certain inflammatory factor and intestinal diseases, this study was the first to systematically identify the relationships between 91 inflammatory factors and intestinal diseases based on the MR method. These results not only deepen our understanding of the inflammatory regulatory mechanism of intestinal diseases, but also provide a theoretical basis for the development of future clinical early diagnosis and targeted treatment strategies.

Given that homocysteine (Hey) may be a viable target for intervention and that there is uncertainty regarding the causal relationship between plasma Hcy levels and colorectal cancer (CRC), this study used Mendelian randomization (MR) to investigate the relationship between Hey and CRC. We summarized the data in this work using genome-wide association studies, identified single nucleotide polymorphisms that were strongly correlated with plasma Hcy levels as instrumental variables, and ran MR analysis on 2 separate sets of outcome data. To make sure the results were stable, a meta-analysis was carried out. MR-Egger, weighted median MR analysis, and the inverse variance method were among the specific analysis techniques used. The leave-one-out method, MR-Egger intercept, MR-PRESSO, and Cochran Q test were also used to assess the stability and dependability of the MR analysis results. In 2 separate European population-based datasets (UK Biobank: OR = 0.9992, 95% CI = 0.9963-1.0021, P = .5951, FinnGen: OR = 0.9771, 95% CI = 0.8370-1.1408, P = .7698), inverse variance method analysis did not reveal any significant causal connection between plasma Hcy levels and CRC. The MR-Egger and weighted median analyses yielded nonsignificant relationships. Both the Cochran Q test and the MR-Egger intercept indicated the absence of considerable heterogeneity and horizontal pleiotropy. The conclusions were further corroborated by the results of the MR-PRESSO analysis and leave-one-out. The pooled results of the Meta analysis also failed to demonstrate significant causality (OR = 0.9992, 95% CI = 0.9963-1.0021, P = .5914), therefore providing additional confirmation of the findings from the individual studies. There was no discernible causal relationship between plasma Hcy levels and CRC risk, according to MR analysis. The findings of this study indicate that while Hcy is a possible target for intervention, there may not be a direct causal relationship between it and the risk of CRC. This finding requires further validation in larger sample sizes and in different populations.

Cisplatin resistance is a major contributor to treatment failure in ovarian cancer (OC). This study investigates the mechanisms of action and therapeutic targets of Stephania tetrandra S. Moorefor cisplatin-resistant OC. OC datasets were obtained from the gene expression omnibus database, and differentially expressed genes were identified through weighted gene co-expression network analysis. Cisplatin resistance-related targets were screened using the GeneCards, OMIM, and MsigDB databases, while active components of S tetrandra were retrieved from the TCMSP, ETCM, and BATMAN databases. Commonly shared genes between these 2 sets were selected for further analysis. A protein-protein interaction network was constructed using the STRING database, and 4 machine learning algorithms were integrated to identify core targets. Binding affinities were evaluated by molecular docking with AutoDock Vina. Molecular dynamics simulations were then conducted to assess the stability of the ligand-target complexes. We performed ADMET analysis to assess the pharmacokinetic properties and drug-likeness of S tetrandra. Machine learning algorithms further identified 8 core targets including threonine tyrosine kinase (TTK), AURKA, B-cell lymphoma 2, vitamin D receptor, NFKB1, cyclin dependent kinase 1, DNMT1, and SMAD7. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that these targets were significantly enriched in pathways such as the PI3K-AKT, cell cycle regulation, p53 signaling pathway, and platinum resistance pathway. Receiver operating characteristic curve analysis demonstrated diagnostic potential for all genes except SMAD7 (AUC = 0.603 < 0.7). Immune infiltration analysis indicated a positive correlation between AURKA/TTK expression and M0/M1 macrophage infiltration (P <.05). Molecular dynamics simulations demonstrated that hesperidin, cissamine and tetrandrine exhibited strong binding affinities toward AURKA, vitamin D receptor, and TTK. Future studies are encouraged to focus on the experimental validation of these compounds and delve deeper into the possible mechanisms of drug resistance, aiming to improve their therapeutic effectiveness and real-world applicability.

Ovarian cancer (OC) is one of the most pervasive malignancies in women and is recognized for its high recurrence and metastasis rates, resulting in a poor prognosis. Regrettably, reliable indicators for the timely detection and prognosis of OC are currently insufficient. Our research aimed to evaluate the immune therapeutic and prognostic possibilities of laminin α5 (LAMA5) in OC using bioinformatics analysis.

Molecular classifications enhance prognostic accuracy in meningiomas; however, their predictive significance following stereotactic radiosurgery (SRS) remains unclear. This study aimed to assess whether molecular characteristics identified by whole-exome sequencing (WES), specifically driver mutations and copy number alterations (CNAs), are prognostic indicators of outcomes after SRS. This retrospective cohort study included 95 patients (median age 61; 66% female) with 97 surgically resected meningiomas treated with SRS between 1999 and 2023. Primary outcomes were progression-free survival (PFS) and disease-specific survival (DSS). Tumors were molecularly classified using WES into Group A (NF2-wildtype), Group B (NF2 mutation/22q loss without high-risk CNAs), and Group C (high-risk CNAs including 1p loss, 1q gain, 6p/6q loss, 10p/10q loss, 14q loss, 18p/18q loss, and CDKN2A/B homozygous deletion). Group C exhibited significantly inferior PFS at 5 years (49.7%) compared with Groups A (88.5%, p < 0.001) and B (100%, p = 0.002). DSS at 10 years was also significantly reduced in Group C (60.4%) relative to Groups A and B (100%, p < 0.001 and p = 0.016, respectively). Within Group C, 1q gain correlated strongly with poorer outcomes, with significantly lower 5-year PFS (15.9% vs. 64.3%, p < 0.001) and DSS (51.3% vs. 90.4%, p < 0.001). Furthermore, even WHO grade 1 tumors with 1q gain demonstrated significantly worse outcomes (5-year PFS: 33.3% vs. 76.5%, p = 0.023; DSS: 44.4% vs. 89.3%, p = 0.011). Molecular classification utilizing WES-derived CNAs substantially improves prognostic prediction after SRS for meningiomas. Chromosome 1q gain was a critical biomarker indicating elevated risk for tumor progression and mortality, even among WHO grade 1 tumors.

Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated with the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.

Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine disorders in women of reproductive age and is characterized by a chronic state of excessive inflammation. Increased expression of WNT5A in a variety of diseases is thought to be related to inflammatory states. The purpose of this study was to explore the potential effects of WNT5A on ovarian granulosa cells and its potential role in the pathogenesis of PCOS.

Olutasidenib is an oral, selective inhibitor of mutant isocitrate dehydrogenase 1 (mIDH1), FDA-approved for relapsed/refractory (R/R) acute myeloid leukemia (AML) based on a registrational, phase 2, open-label, multicenter trial.

The limb development membrane protein 1-like gene (LMBR1L) encodes a transmembrane protein involved in cellular signaling and molecular transport. LMBR1L plays a critical role in immune system development by negatively regulating the Wnt/β-catenin pathway in lymphocytes. However, its role in the tumor immune microenvironment and its potential as a predictor of immunotherapy response remain poorly understood. We analyzed LMBR1L expression in normal and tumor tissues using data from public databases and clinical samples. Prognostic significance and immune associations were assessed using survival analysis, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and immune infiltration profiling tools. Additionally, both in vitro and in vivo experiments were conducted to assess the effects of LMBR1L knockdown in colorectal cancer (COAD) cells. Elevated LMBR1L expression was observed in multiple cancers. Single-cell sequencing revealed its primary expression in CD8 + T cells, and higher expression was correlated with poorer outcomes. Increased LMBR1L levels were associated with immune cell infiltration, including CD4 + and CD8 + T cells, and correlated with programmed death-ligand-1 (PD-L1) expression, suggesting its involvement in immune checkpoint regulation. LMBR1L knockdown suppressed the proliferation, migration, and invasion of COAD cells, a finding further corroborated by in vivo experiments. Our findings suggest that LMBR1L may be a prognostic biomarker and an immunotherapy predictor, emphasizing its potential as a therapeutic target in cancer treatment. This study provides a foundation for future work on targeted therapy and immunotherapy strategies in cancer.

This study investigated the mechanism of methyltransferase-like 14 (METTL14)-mediated epigenetic transcriptome modification of RAD21 mRNA in inhibiting thyroid cancer (TC) development.

To investigate the expression profile, clinical significance, and underlying molecular mechanisms of C1orf112 in endometrial cancer (EC).

Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a 5-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular classifiers have attempted to clarify this heterogeneity with moderate agreement. Recognizing the complexity introduced by this extensive array of taxonomies, this study aims to develop a consensus molecular classifier by including both tumor and stroma features.

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.