Admixture Mapping Links RACGAP1 Regulation to Prostate Cancer in African Americans

Influence of Admixture on Phenotypes

Individuals of European descent have historically been the focus of genetic studies and possess relatively homogenous genomes. As a result, analytical methods have been developed and optimized with such genomes in mind. African-descent and Latino individuals generally possess genomes of greater architectural complexity due to mosaic genomic ancestry, which can extensively and intricately impact phenotypic expression. As such, genetic analyses of admixed individuals require that genetic admixture be quantified to accurately model the impact of genetic variation on phenotypic expression. In this overview, we explore how fundamental genetic concepts such as linkage disequilibrium and differential allele frequency interact with genetic admixture to uniquely influence phenotypes in admixed individuals. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Identification of Prognostic Markers and Potential Therapeutic Targets in Gastric Adenocarcinoma by Machine Learning Based on mRNAsi Index

Background

Cancer stem cells (CSCs), characterized by self-renewal and therapeutic resistance, play important roles in stomach adenocarcinoma (STAD). However, the molecular mechanism of STAD stem cells is still unclear. In this study, our purpose is to explore the expression of stem cell-related genes in STAD.

Methods

The stemness index based on mRNA expression (mRNAsi) was used to analyze STAD cases in The Cancer Genome Atlas (TCGA). Firstly, mRNAsi was used and analyzed by differential expression, survival analysis, clinical stage, and gender in STAD. Then, weighted gene coexpression network analysis (WGCNA) was used to discover the fascinating modules and key genes. Enrichment analysis was carried out to annotate the functions and pathways of key genes. The gene expression comprehensive database (GEO) in STAD was used to verify the expression levels of key genes in all cancers. Protein-protein interaction networks is used to determine the relationships between key genes.

Results

The mRNAsi was obviously upregulated in tumor cases. With the increase of tumor stage and T stage, the mRNAsi score decreased, and the overall survival rate of high score group patients was better. According to the degree of association with mRNAsi, different modules and key genes were screened out. A total of 6,740 differential genes were found, of which 1,147 genes were downregulated and 5,593 genes were upregulated. 19 key genes (BUB1, BUB1B, KIF14, NCAPH, RACGAP, KIF15, CENPF, TPX2, RAD54L, KIF18B, KIF4A, TTK, SGO2, PLK4, ARHGAP11A, XRCC2, Clorf112, NCAPG, and ORC6) were screened due to significant upregulation in STAD. And they had been proven that enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Among them, 9 genes have been extensively associated to OS, and 3 genes had been associated to receive chemotherapy resistance. PPI protein network suggests that there is a sturdy correlation between these key genes.

Conclusion

A total of 19 key genes were found to play an essential position in retaining the traits of STAD stem cells. These genes can be used to evaluate the prognosis of STAD patients or become specific therapeutic targets.

Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

Genetic Polymorphisms in IL-10 Promoter Are Associated With Smoking and Prostate Cancer Risk in African Americans

BACKGROUND/AIM

Even though prostate cancer (PCa) has good prognosis, there is a discrepancy in the risk among ethnic groups, with high morbidity in African American men. Single nucleotide polymorphisms (SNPs) in interleukin 10 (IL-10) have been associated with inflammation and cancer risk. We investigated the association of five SNPs in the IL-10 promoter with clinical features such as Gleason score and smoking.

MATERIALS AND METHODS

A total of 413 DNA samples were obtained from a nested case-control study of African American males who were genotyped for 5 SNPs utilizing pyrosequencing. Multiple and binary logistic regression models were applied to analyze the clinical and genotypic data.

RESULTS

rs12122923 and rs1800871 were associated with PCa risk. Smoking was also found to increase the risk of PCa by 1.6-fold. rs1800893 was found to be associated with lower grades for prostate cancer.

CONCLUSION

IL-10 promoter polymorphisms might be a risk factor for PCa development in smoking subjects and PCa progression.