The Influence of ICAM1 3'UTR Gene Polymorphism on the Occurrence and Metastasis of Primary Liver Cancer

Contribution of Endothelial Dysfunction to Cancer Susceptibility and Progression: A Comprehensive Narrative Review on the Genetic Risk Component

Venous thromboembolism (VTE) is a challenging clinical obstacle in oncological settings, marked by elevated incidence rates and resulting morbidity and mortality. In the context of cancer-associated thrombosis (CAT), endothelial dysfunction (ED) plays a crucial role in promoting a pro-thrombotic environment as endothelial cells lose their ability to regulate blood flow and coagulation. Moreover, emerging research suggests that this disorder may not only contribute to CAT but also impact tumorigenesis itself. Indeed, a dysfunctional endothelium may promote resistance to therapy and favour tumour progression and dissemination. While extensive research has elucidated the multifaceted mechanisms of ED pathogenesis, the genetic component remains a focal point of investigation. This comprehensive narrative review thus delves into the genetic landscape of ED and its potential ramifications on cancer progression. A thorough examination of genetic variants, specifically polymorphisms, within key genes involved in ED pathogenesis, namely eNOS, EDN1, ACE, AGT, F2, SELP, SELE, VWF, ICAM1, and VCAM1, was conducted. Overall, these polymorphisms seem to play a context-dependent role, exerting both oncogenic and tumour suppressor effects depending on the tumour and other environmental factors. In-depth studies are needed to uncover the mechanisms connecting these DNA variations to the pathogenesis of malignant diseases.

Genetic Association Between ICAM-1 Gene Variants and Susceptibility to Ischemic Cardiomyopathy

ABSTRACT

The current work was aimed at exploring the association between single nucleotide polymorphisms (SNPs) in the ICAM-1 gene, along with the identification of additional haplotypes and their potential role in the susceptibility to ischemic cardiomyopathy (ICM). The control group underwent a Hardy-Weinberg equilibrium test. The associations of genotypes and alleles with susceptibility to ICM were then analyzed using logistic regression analysis. Subsequently odds ratios (ORs) along with 95% confidence intervals (95% CI) were calculated. Interaction analysis was conducted between these SNPs. Furthermore, linkage disequilibrium analysis and haplotype analysis were performed on SNPs that showed interactions with each other. The incidence of ICM was significantly higher among individuals carrying the T allele of rs3093032 (OR = 2.032, 95% CI, 1.275-3.241, P = 0.003) relative to those with the C allele. In addition, CT genotype carriers had a higher susceptibility to ICM than CC genotype carriers (OR = 2.490, 95% CI, 1.445-4.29, P = 0.001). Furthermore, 3 SNPs (rs3093032, rs923366, rs3093030) exhibited a strong interaction with each other, whereas rs281437 showed no interaction with the other 3 SNPs. Individuals carrying the C rs3093032 -T rs923366 -C rs3093030 haplotype had an elevated risk of ICM compared with those carrying the C rs3093032 -C rs923366 -C rs3093030 haplotype (OR = 2.280, 95% CI, 1.568-3.315, P < 0.001). Moreover, individuals carrying the T rs3093032 -C rs923366 -C rs3093030 haplotype were more susceptible to ICM than those carrying the C rs3093032 -C rs923366 -C rs3093030 haplotype (OR = 2.388, 95% CI, 1.469-3.880, P < 0.001). Regarding rs3093032, the minor alleles and haplotypes are associated with an increased ICM risk: 3 SNPs (rs3093032, rs923366, rs3093030) in ICAM-1 have strong interaction with each other.

Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients

Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

Background

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

Methods

In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

Results

Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

Conclusion

Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.