It has been observed that lung cancer patients are more susceptible to COVID-19. Establishing the causal relationships between lung cancer and COVID-19 susceptibility and severity is challenging due to numerous confounding factors. Mendelian randomization (MR) is an effective method to investigate the causal association between exposure and outcome variables. However, different studies have yielded conflicting conclusions regarding the role of lung cancer in COVID-19 susceptibility and severity. Lung cancer subtypes exhibit heterogeneity in genetic susceptibility, which may influence the assessment of the true relationship between lung cancer and COVID-19 susceptibility and severity. In this study, we utilized the most recent COVID-19 association data from The COVID-19 Host Genetics Initiative with more than two million samples in total, in combination with genetic data of different lung cancer subtypes with more than eighty-five thousand samples, and conducted a two-sample Mendelian randomization study. We examined the associations between lung cancer and its four subtypes with COVID-19 susceptibility, hospitalization, and severity. Our data indicates that lung cancer, overall, does not have a causal association with COVID-19 susceptibility, hospitalization, or severity. However, lung cancer in ever smokers is nominally associated with COVID-19 hospitalization p-value 0.035, false discover rate (FDR) [Formula: see text] and increased severity [Formula: see text], [Formula: see text]. Additionally, small cell lung carcinoma is associated with increased COVID-19 severity [Formula: see text], [Formula: see text].

Cancer is the leading threat to human health and lifespan. Every day, the number of deaths caused by cancer continues to rise. Therefore, accurately predicting survivability from cancer has become an important area in cancer research. In predicting survivability, multi-omics data is advantageous as it provides information from different molecular levels of human biological processes, encompassing different omics such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. In this article we introduce a novel method called Attention-Enabled Hybrid Deep Sequential Network (AHDSN) which utilizes Long Short-Term Memory, Bidirectional Gated Recurrent Unit, and the attention mechanism to extract latent features from multi-omics data and Dense layers with softmax activation function for classification. Unlike conventional approaches that predict survival at a fixed time point (e.g., 5-year survival), the proposed AHDSN method predicts overall survival across the complete follow-up period using each patient's survival time and censoring status. We evaluated the proposed AHDSN method against several state-of-the-art approaches to assess their relative performance in survivability prediction from multi-omics data. To address class imbalance, both Random Oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied during preprocessing to ensure a more balanced distribution of samples across classes. The experimental results show that the proposed AHDSN method surpassed other state-of-the-art methods in terms of accuracy, precision, recall, and [Formula: see text]-score across five multi-omics cancer datasets, Glioblastoma, Colon, Breast, Kidney, and Lung, achieving accuracies of 98.33%, 96.00%, 97.14%, 88.24%, and 80.00% when using ROS, and 97.12%, 96.00%, 96.22%, 85.18%, and 80.00% when using SMOTE respectively. Confidence Interval test also demonstrates the superiority of the proposed AHDSN method compared to other existing methods in producing the lowest error rate and the smallest error bound for all five multi-omics datasets. Additionally, SHapley Additive exPlanations analysis and heatmaps are employed to explain feature importance and illustrate how individual omics features contribute to model classification. Furthermore, the ablation study confirms the synergistic benefit of the proposed hybrid architecture and validates the importance of each component.

Lineage plasticity is a hallmark of pancreatic ductal adenocarcinoma (PDAC) and contributes to tumor heterogeneity and therapeutic resistance. Here, we identify KLF5 as a dynamic master regulator of epithelial lineage identity in PDAC, with dichotomous roles in promoting either classical or basal-like transcriptional programs. Through unbiased proteomic and genetic screens, we uncover the AAA+ ATPases RUVBL1 and RUVBL2 as essential coactivators of KLF5 across both lineage states. We demonstrate that ATP hydrolysis by RUVBL1/2 is required for the stable interaction with an intrinsically disordered region of KLF5, enabling its recruitment to lineage-specific enhancers and driving transcriptional regulation of identity-defining genes. Notably, small-molecule inhibitors of RUVBL1/2 ATPase activity, which have anti-PDAC activity in vivo, suppress KLF5-dependent transcription. These findings define a previously unrecognized mechanism of ATP hydrolysis-dependent transcriptional coactivation and highlight a potential therapeutic strategy for modulating aberrant lineage programs in cancer.

Prostate cancer is the second most common cancer in men. Although androgen deprivation therapy is initially effective, resistance inevitably develops. Most patients eventually progress to castration-resistant prostate cancer, a stage with limited treatment options and poor prognosis. Rho kinases (ROCK1 and ROCK2) have been implicated in cancer progression, but their therapeutic targeting remains limited. This study examined the pathological roles of ROCK1 and ROCK2 in epithelial-mesenchymal transition (EMT) and proliferation of prostate cancer cells. ROCK1 expression was comparable between human prostate epithelial cells (PrECs) and androgen-independent prostate cancer cells, PC-3 and DU145. In contrast, ROCK2 expression was higher in PC-3 cells than in PrECs and DU145 cells. EMT marker analysis revealed that PC-3 cells exhibited decreased E-cadherin and increased N-cadherin and Snail expression. ROCK2 knockdown reversed this EMT phenotype, reducing cell proliferation, migration, 3D tumor spheroid formation, and spheroid cell viability. Similar inhibitory effects were observed by the ROCK2-selective blocker KD025 (IC50 = 422 nM). Furthermore, ROCK2 deficiency attenuated the tumor growth of PC-3 cells in a xenograft mouse model. These findings indicate that ROCK2 promotes EMT process and tumor progression in PC-3 cells. Targeting ROCK2 may represent a promising therapeutic strategy for androgen-independent prostate cancer.

Neuroendocrine neoplasms (NEN) are heterogenous malignancies classified by morphology, site of origin, and Ki-67 proliferation indices. These complicated classification systems often fail to capture NEN outcome diversity. Molecular profiling advances have revealed distinct genomic portfolios that transcend conventional nosology schema. We highlight current classification limitations and suggest the need for individual molecular tumor portraits as a cornerstone of diagnostic/treatment paradigms.

ADAMTS1 (a disintegrin and metalloproteinase with thrombospondin motifs 1), a member of the ADAMTS family, is a critical molecule due to its dual roles as a potent angiogenesis inhibitor and an aggrecanase. The dysregulation of ADAMTS1 is a common feature in many pathophysiological conditions, making the understanding of its regulation vital. The primary focus of this investigation was to explore the regulatory role of SMAD transcription factors, the central mediators of the TGF-β signaling pathway, on ADAMTS1 gene expression in Hep3B cells.

The purpose of this study was to compare the perioperative efficacy and safety of neoadjuvant chemoradiotherapy (NCRT) alone versus short-course radiotherapy combined with immunochemotherapy (SCRT + ICT) in patients with proficient mismatch repair (pMMR) rectal cancer.

Pleural effusion (PE) is a common clinical manifestation associated with advanced stages of both malignant and non-malignant diseases. PE frequently occurs in advanced non-small cell lung cancer (NSCLC) and contributes to tumor progression. NSCLC accounts for more than 85% of the lung cancers and remains a problem worldwide due to its late diagnosis and low rate of response to treatment. Extracellular vesicles (EVs) present in PE are emerging as key mediators of intercellular communication, capable of transferring oncogenic signals through their molecular cargo. Among these molecules, microRNAs (miRNAs) are increasingly recognized as important drivers of cancer progression. miR-21 is a representative onco-miRNA, involved in lung cancer progression; moreover EV-miR-21 upregulation at the pre-dissemination stage promotes cancer cell survival in the pleural cavity. This study compares, for the first time, the functional role of EVs isolated from malignant PE in NSCLC patients (NSCLC-PE-EVs) with those isolated from PE in patients with congestive heart failure (CHF-PE-EVs), focusing on their ability to modulate lung cancer cell behavior. The effects of these EVs were evaluated on COLO699 lung adenocarcinoma cells with proliferation, migration, and gene expression assays. NSCLC-PE was found to contain approximately twice the amount of EVs compared to CHF-PE. NSCLC-PE-EVs were enriched in the oncogenic miR-21-5p, while CHF-PE-EVs had higher levels of the tumor-suppressive miR-126-3p. Only NSCLC-PE-EVs induced dose-dependent increases in COLO699 cell proliferation and migration, consistent with elevated miR-21-5p expression. Functional studies confirmed that miR-21-5p mediates these effects by downregulating PTEN and PDCD4, and by upregulating MMP9 expression. Our findings show that NSCLC-PE-EVs promote malignant phenotypes in lung cancer cells via the transfer of miR-21-5p.

To use determined predictors of Pediatric low-grade gliomas' molecular subtype via conventional radiology review to develop a predictive model for molecular subtype categorization.

Hemangioblastoma is an uncommon tumor of uncertain histogenesis, primarily found in the central nervous system. However, extraneural cases have been reported in visceral organs such as the kidneys, pancreas, peritoneum, and liver. Hemangioblastoma occurring in the gastrointestinal tract is extremely rare, with only 4 cases can be retrieved. Here, we presented a case of rectal hemangioblastoma.

Together with base editors, prime editors, and alternative nuclease platforms, CRISPR-Cas9 technology has transformed the field of genetic engineering by providing unprecedented precision in genome editing and creating new opportunities for therapeutic interventions. This technology comprises a versatile genome-editing toolkit for achieving a variety of therapeutic objectives. In the context of leukemia, a group of life-threatening hematologic malignancies, CRISPR-Cas9 has emerged as a transformative tool for immunotherapy. By enabling targeted modifications of immune cells, such as T cells, this technology enhances their ability to recognize and eradicate leukemic cells. CRISPR-Cas9 facilitates the disruption of immune checkpoint inhibitors, the insertion of chimeric antigen receptors (CARs), and the correction of genetic mutations that drive leukemia progression. These advancements have led to the development of more potent and personalized immunotherapies, such as CAR-T cell therapies, with improved efficacy and reduced off-target effects. Moreover, CRISPR-Cas9 allows researchers to model leukemia in vitro, providing deeper insights into disease mechanisms and accelerating the discovery of novel therapeutic targets. Despite challenges such as delivery efficiency and potential immunogenicity, the integration of CRISPR-Cas9 into leukemia immunotherapy represents a paradigm shift, offering hope for durable remissions and potentially curative outcomes. As clinical trials progress, this technology promises to redefine the standard of care for leukemia patients, ushering in a new era of precision medicine. This narrative review explores the revolutionary applications of CRISPR-Cas9 in redefining therapeutic strategies for leukemia.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. FOXO1 fusion indicates poor prognosis and lead to dysregulation of transcriptioanal network. This study aims to investigate clinical characteristics and therapeutic targets concerning FOXO1 fusion status.

Tumor tissues are composed of malignant subclones with diverse genetic profiles. Reconstructing the evolutionary trajectory of these subclones is crucial for understanding how tumors acquire malignant traits. However, current approaches to subclonal tree reconstruction are limited either by their reliance on single-cell DNA sequencing (scDNA-seq) that involve a small number of cells and thus yield low-resolution results, or using single-cell RNA sequencing (scRNA-seq) data, which despite including larger cell populations, remain susceptible to bias from high dropout rates and technical noise. Here, we introduce CluVar, an autoencoder-based framework for inferring the phylogeny of cancer subclones from scRNA-seq data using mutation profile analysis. To address the extensive missing variant information inherent in scRNA-seq datasets, CluVar incorporates a customized loss function and multiple hidden layers optimized for clustering. CluVar demonstrated superior performance in reconstructing phylogenetic trees of cancer subclones under a range of erroneous conditions. When applied to cancer scRNA-seq data, the phylogenetic tree predicted using CluVar aligned well with the transcriptomic profiles. These findings highlight its utility for tracing evolutionary trajectories and identifying novel variants associated with cancer progression.

Malignant transformation is not merely the consequence of stochastic genetic mutations but rather a convergence of complex interplay between genetic, epigenetic, and environmental factors that collectively reprogram normal cells into cancerous ones. Central to these processes is the loss of tumor suppressor genes, activation of oncogenes, chromosomal instability, and widespread epigenetic remodeling. A pivotal aspect of this transformation involves the contribution of ncRNAs that have been recurrently implicated in malignant transformation. While much attention is directed toward miRNAs, circRNAs, and lncRNAs, piRNAs have remained comparatively overlooked in cancer biology. This is despite a growing body of evidence implicating piRNAs in regulating tumorigenic pathways, genomic stability, and epigenetic gene silencing. Our review explores the emerging role of the piRNAs and their regulatory protein PIWI in modulating key malignant features, including hyperproliferation, EMT, tumor evasion, migration, angiogenesis, and others. We also examine how this axis influences the initiation and progression of cancer, highlighting its potential to reshape established paradigms in cancer studies.

Chronic obstructive pulmonary disease (COPD) commonly co-occurs with lung cancer, particularly lung adenocarcinoma (LUAD), suggesting a potential shared molecular mechanism and risk factors between the 2 conditions. This study aimed to explore the causal relationship between COPD and LUAD mediated by immune cells using a 2-step, 2-sample Mendelian randomization (MR) analysis. The random-effect inverse variance weighted method, which combines the Wald ratio of individual single-nucleotide polymorphisms, was employed as the primary approach for causal inference, with random-effects models utilized in the presence of heterogeneity. Mediation analysis was conducted to assess indirect effects in the pathway from COPD to LUAD. The MR analysis demonstrated that COPD increased the risk of LUAD (odds ratio = 1.180, 95% confidence interval [CI]: 1.004-1.387, P = .045). Furthermore, among 40 immune cell traits examined, 5 were associated with an elevated risk of LUAD, while 6 exhibited a detrimental effect. Importantly, the mediation MR analysis revealed that the indirect impact of COPD on LUAD was partially mediated by Activated & resting Treg cells (mediation effect: 0.010, 95% CI: 0.001-0.021; P = .047) and Activated & secreting Treg cells (mediation effect: 0.004, 95% CI: 0.001-0.008; P = .044). These findings suggest a positive association between COPD and LUAD, with a partial mediation effect through Activated & resting Treg cells and Activated & secreting Treg cells.

Pneumonic-type lung carcinoma is a rare radiological lung cancer subtype. Its clinical and imaging manifestations are easily confused with pneumonia, leading to frequent misdiagnosis and mistreatment in clinical practice.

Subhealth status (SHS), an intermediate state between health and disease, is increasingly prevalent, yet its potential causal role in cancer remains unclear. Leveraging publicly available genome-wide association data from the Integrative Epidemiology Unit, we conducted a two-sample Mendelian randomization (MR) study to estimate the causal effects of 4 SHS proxies - heart rate variability (HRV), health satisfaction, irritability, and mood swings - on risk of lung, breast, colorectal, prostate, cervical, pancreatic, and basal-cell carcinoma. Inverse variance-weighted (IVW) regression was the primary analysis, supplemented by sensitivity analyses (weighted median, MR-Egger, radial IVW, maximum likelihood). Genetically predicted higher HRV (standard deviation of normal-to-normal interbeat intervals and peak-to-valley respiratory sinus arrhythmia or high-frequency power) was associated with lower colorectal cancer risk. Higher irritability was causally linked to increased lung cancer risk. Mood swings showed a modest positive association with basal-cell carcinoma. Greater health satisfaction was associated with higher risks of cervical and pancreatic cancer. No directional pleiotropy or heterogeneity was detected for these associations. These findings suggest that selected SHS indicators may causally influence site-specific cancer risk, supporting early intervention on autonomic dysfunction and emotional dysregulation as plausible cancer-prevention strategies.

Lung adenocarcinoma (LUAD), a common type of non-small cell lung cancer, is associated with low survival rates and challenges in early detection. Therefore, identifying prognostic biomarkers is crucial for improving patient outcomes. This study utilized 2 datasets - the Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and GSE72094 - along with 182 immune escape-related genes and 597 cancer-associated fibroblast-related genes. Weighted gene co-expression network analysis was used to identify module genes. Differential expression analysis of TCGA-LUAD data revealed LUAD-associated differentially expressed genes, which were then intersected with module genes to identify LUAD-specific differentially expressed immune escape-cancer fibroblast-related genes. To identify potential biomarkers and develop a risk model, univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression were performed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used for enrichment analysis. Immune infiltration and immune cell-biomarker correlations were assessed using CIBERSORT, and the pRRophetic tool was employed to predict LUAD chemotherapeutic sensitivities. Reverse transcription-quantitative polymerase chain reaction was used to validate the expression of prognostic genes in non-small cell lung cancer. The results showed that 183 differentially expressed immune escape-cancer fibroblast-related genes were identified by intersecting 1460 module genes with 5439 differentially expressed genes. Six genes (KRT8, S100A16, COL4A3, SMAD9, MAP3K8, and CCDC146) were selected as potential biomarkers for risk modeling. Gene Ontology enrichment analysis highlighted the involvement of glucose metabolism, ion channel complexes, and channel activity-related genes. Kyoto Encyclopedia of Genes and Genomes analysis revealed pathways related to morphine addiction and protein digestion/absorption. Immune infiltration analysis identified significant differences in 9 immune cell types, including memory B cells and CD8 T cells, between risk groups. Sensitivity to chemotherapeutics, such as AZD6482, ABT-263, A-770041, and BMS-536924, was observed in LUAD. Reverse transcription-quantitative polymerase chain reaction validation results demonstrated that KRT8 and S100A16 were significantly upregulated in tumor tissues, while COL4A3 and SMAD9 expression was downregulated, which was consistent with the TCGA-LUAD database analysis. In conclusion, 6 genes (KRT8, S100A16, COL4A3, SMAD9, MAP3K8, and CCDC146) were identified as potential biomarkers, offering valuable insights into LUAD pathogenesis and therapeutic strategies.

Endometrial cancer (EC) is a major reproductive system tumor and a common cancer in women. Polycystic ovary syndrome (PCOS) is one of the most prevalent female reproductive endocrine disorders. The incidence of EC is significantly higher in individuals with PCOS. This study seeks to elucidate the organic correlations and interaction mechanisms between the 2 diseases through series of exploration of key genes with a bioinformatics analysis. The PCOS sample data and the EC single-cell dataset were downloaded from the gene expression omnibus database. The EC sample data were retrieved from the cancer genome atlas public database. The random survival forest method was employed to identify key genes associated with the prognosis of PCOS and EC comorbidity. Corresponding analyses on functional pathway enrichment, regulatory networks, and immune micro-environment are conducted. From a bioinformatics perspective, the association and interaction mechanisms between PCOS and EC comorbidity were explored to provide research and development references for the prevention and control of PCOS and EC comorbidity. Five key genes associated with the prognosis of PCOS and EC comorbidity were identified using the random survival forest method. The identified genes are SYTL1, PARVG, ID4, IL1RN, and S100A9. The abnormal expression of these key genes has impacted various enrichment pathways, including the TGF-β signaling-pathway, motif regulatory networks (such as motif cisbp__M4556), and miRNA regulatory networks, which encompass genes such as ATM, BARD1 and BRCA1. Furthermore, these also influence the immune cell microenvironment, such as T cells regulatory. Collectively, these key genes play a significant role in the occurrence and progression of comorbidities through the pathways mentioned above. The dysregulation of key genes (SYTL1, PARVG, ID4, IL1RN, S100A9) in the context of PCOS-EC comorbidities, along with their associated enrichment pathways, including the TGF-β signaling-pathway and immune microenvironment, plays a significant role in the occurrence and progression of EC.

In recent years, an increasing number of observational studies have reported the impact of air pollution on ovarian cancer. However, a Mendelian randomization (MR) study to explore the causal relationship in ovarian cancer has not yet been conducted. This study, based on 2-sample MR, conducts MR analysis by examining 5 air pollution indices with ovarian cancer data from 2 different sources. Subsequently, a meta-analysis of the primary inverse-variance weighted results is performed, followed by multiple corrections on the meta-analysis thresholds to ensure accuracy. Finally, a reverse causality verification of the positive air pollution indices with ovarian cancer is conducted through MR analysis. The MR analysis was conducted using 5 air pollution indices and ovarian cancer data from the Finngen R10 and OpenGWAS databases. A meta-analysis was then performed on the inverse-variance weighted results from both sets of analyses. Multiple corrections were applied to the significance threshold of the meta-analysis results, revealing an odds ratio value of 1.544 (95% confidence interval: 1.180-2.020, P = .0077). Additionally, the positive air pollution index, nitrogen dioxide, showed no reverse causality with ovarian cancer in both data sources. Nitrogen dioxide is a risk factor for ovarian cancer and may increase the risk, accelerating the onset and progression of the disease.