CDKN1A (p21 gene) polymorphisms correlates with age in esophageal cancer

Amyotrophic Lateral Sclerosis and Parkinson's Disease: Brain Tissue Transcriptome Analysis Reveals Interactions

This study utilises amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) human brain samples from the GEO database and employs differential expression gene (DEG) analysis to identify genes that are pivotal in both neurodegenerative diseases. Through in depth GO and KEGG enrichment analyses, we elucidated the biological functions and potential pathways associated with these DEGs. Furthermore, by constructing protein‒protein interaction networks, we highlight the significance of shared DEGs in both cellular physiology and disease contexts. Analysis of drug‒gene associations revealed potential therapeutic compounds linked to ALS and PD treatment. Additionally, we explored the interactions between transcription factors, miRNAs, and common DEGs, revealing aspects of gene regulatory networks. This study provides insights into the molecular mechanisms of ALS and PD, offering valuable contributions to ongoing research and potential therapeutic avenues.

Contribution of Cyclin Dependent Kinase Inhibitor 1A Genotypes to Childhood Acute Lymphocytic Leukemia Risk in Taiwan

BACKGROUND/AIM

The disruption of cell-cycle control can lead to an imbalance in cell proliferation, often accompanied by genomic instability, which in turn can facilitate carcinogenesis. This study aimed to examine the impact of CDKN1A rs1801270 and rs1059234 polymorphisms on the risk of childhood acute lymphocytic leukemia (ALL) in Taiwan.

MATERIALS AND METHODS

The genotypes of CDKN1A rs1801270 and rs1059234 in 266 childhood ALL cases and 266 controls were determined using PCR-RFLP techniques.

RESULTS

The genotypic and allelic frequencies for CDKN1A rs1801270 and rs1059234 did not significantly differ between childhood ALL cases and controls (all p>0.05). However, stratified analysis revealed that the CDKN1A rs1801270 AA variant was associated with a reduced risk of childhood ALL in males (OR=0.40, 95%CI=0.20-0.82, p=0.0178). Additionally, the AC and AA genotypes of rs1801270 were linked to a lower risk classification for childhood ALL and longer survival times (OR=0.57 and 0.31, 95%CI=0.33-0.97 and 0.18-0.56, p=0.0538 and 0.0001, respectively). No significant associations were found for rs1059234 in the stratified analyses (p>0.05 for all).

CONCLUSION

Although CDKN rs1801270 and rs1059234 genotypes were not associated with an overall risk of childhood ALL, CDKN1A rs1801270 polymorphism may serve as a protective predictor in males and as a potential marker for better prognosis of childhood ALL. Validation in larger and more diverse populations is necessary to confirm the feasibility of this predictor.

Single-cell analysis reveals landscape of endometrial cancer response to estrogen and identification of early diagnostic markers

BACKGROUND

The development of endometrial cancer (EC) is closely related to the abnormal activation of the estrogen signaling pathway. Effective diagnostic markers are important for the early detection and treatment of EC.

METHOD

We downloaded single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) data of EC from public databases. Enrichment scores were calculated for EC cell subpopulations using the "AddModuleScore" function and the AUCell package, respectively. Six predictive models were constructed, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and neural network (NK). Subsequently, receiver-operating characteristics with areas under the curves (AUCs) were used to assess the robustness of the predictive model.

RESULT

We classified EC cell coaggregation into six cell clusters, of which the epithelial, fibroblast and endothelial cell clusters had higher estrogen signaling pathway activity. We founded the epithelial cell subtype Epi cluster1, the fibroblast cell subtype Fib cluster3, and the endothelial cell subtype Endo cluster3 all showed early activation levels of estrogen response. Based on EC cell subtypes, estrogen-responsive early genes, and genes encoding Stage I and para-cancer differentially expressed proteins in EC patients, a total of 24 early diagnostic markers were identified. The AUCs values of all six classifiers were higher than 0.95, which indicates that the early diagnostic markers we screened have superior robustness across different classification algorithms.

CONCLUSION

Our study elucidates the potential biological mechanism of EC response to estrogen at single-cell resolution, which provides a new direction for early diagnosis of EC.