Novel prostate cancer susceptibility gene SP6 predisposes patients to aggressive disease

Genetic Variants and Haplotype Structures in the CASC Gene Family to Predict Cancer Risk: A Bioinformatics Study

Background and Aims

The cancer susceptibility (CASC) gene family of long noncoding RNAs (lncRNAs) plays an important role in cancer. The aim of this study was to identify genetic variants and haplotype structures of CASC genes associated with cancer risk.

Methods

Genome-wide association studies (GWAS) significant variants (p ≤ 5 × 10-8) on CASC family genes were identified from the GWAS Catalog-EMBL-EBI, and then cancer-associated variants on CASC genes were extracted. These variants were functionally analyzed, including lncRNA:miRNA binding sites, Regulomedb scores, and eQTL. The 1000 Genome Project genotyping data Phase III were used to identify haplotypic blocks. Finally, the genes associated with them were examined for expression and gene-gene correlation analyses using OncoDB.

Results

There were six haplotypic blocks in four genes. The GC, TA, and AGAC haplotypes are located in the CASC8 gene and increase the risk of prostate cancer, breast cancer, and colorectal cancer, respectively. The CA haplotype in the CASC15 gene increases the risk of neuroblastoma, AA haplotype in the CASC16 gene increases the risk of breast cancer, and ACGATG haplotype in the CASC17 gene increases the risk of prostate cancer (p ≤ 5 × 10-8). Their genes are interrelated and their expression is increased in these cancers. The rs2294214 is associated with skin cancer and has positive effects on five CASC15:miRNA binding sites. The rs3803662 is located in CASC16:miRNA binding sites, which has positive effects on hsa-miR-4475 and hsa-miR-7845-5p and negative effects on hsa-miR-4524a-3p and hsa-miR-4524b-3p.

Conclusion

These haplotypic structures and lncRNA:miRNA:SNP interactions on CASC family lncRNAs reveal novel genetic associations between CASC genes and various cancers.

Elevated GRHL2 Imparts Plasticity in ER-Positive Breast Cancer Cells

Estrogen receptor (ER)-positive breast cancer is characterized by late recurrences following initial treatment. The epithelial cell fate transcription factor Grainyhead-like protein 2 (GRHL2) is overexpressed in ER-positive breast cancers and is linked to poorer prognosis as compared to ER-negative breast cancers. To understand how GRHL2 contributes to progression, GRHL2 was overexpressed in ER-positive cells. We demonstrated that elevated GRHL2 imparts plasticity with stem cell- and dormancy-associated traits. RNA sequencing and immunocytochemistry revealed that high GRHL2 not only strengthens the epithelial identity but supports a hybrid epithelial to mesenchymal transition (EMT). Proliferation and tumor studies exhibited a decrease in growth and an upregulation of dormancy markers, such as NR2F1 and CDKN1B. Mammosphere assays and flow cytometry revealed enrichment of stem cell markers CD44 and ALDH1, and increased self-renewal capacity. Cistrome analyses revealed a change in transcription factor motifs near GRHL2 sites from developmental factors to those associated with disease progression. Together, these data support the idea that the plasticity and properties induced by elevated GRHL2 may provide a selective advantage to explain the association between GRHL2 and breast cancer progression.

Common and novel haplotype structures between different types of cancer

BACKGROUND

Background: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with cancer risk. GWAS data are important for cancer prevention and understanding the underlying mechanisms of cancer.

AIMS

This study aimed to investigate the genetic association between different types of cancer using GWAS data and a bioinformatics approach.

METHODS AND RESULTS

The significant GWAS variants associated with more than one cancer type were identified. Common linkage disequilibrium (LD) variants between different types of cancer were identified by 1000 genomes phase 3 LD data. Haplotype blocks were identified by analyzing 1000 Genomes phase 3 genotyping data in the GWAS populations. Subsequent analyses included functional SNP analyses and TCGA gene expression. The results associated with significant GWAS variants (P<5E-8) showed the following haplotype associations in European population: GT rs4808075-rs8170 haplotype on BABAM1 with breast and ovarian cancers, GC rs16857609-rs11693806 haplotype on DIRC3 with breast and thyroid cancers, GCG rs380286-rs401681-rs31487 haplotype on CLPTM1L with skin and lung cancers, GGG rs4430796-rs11651052-rs11263763 haplotype on HNF1B with prostate and endometrial cancers, and GT rs10505477-rs6983267 haplotype on CASC8 associated with colorectal and prostate cancers. All these genes had significantly different expressions in tumor tissues (P<1E-3). In addition, the rs11693806 variant is located in the hsa-miR-873-5p binding site and has an enhancing effect on the hsa-miR-873-5p:DIRC3 interaction.

CONCLUSION

These novel haplotype structures and miRNA:lncRNA interactions are important for understanding the common genetic link between cancers. These results can potentially be used in genetic panels.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

Prostate Cancer: Genetics, Epigenetics and the Need for Immunological Biomarkers

Epidemiological data highlight prostate cancer as a significant global health issue, with high incidence and substantial impact on patients' quality of life. The prevalence of this disease is associated with various factors, including age, heredity, and race. Recent research in prostate cancer genetics has identified several genetic variants that may be associated with an increased risk of developing the disease. However, despite the significance of these findings, genetic markers for prostate cancer are not currently utilized in clinical practice as reliable indicators of the disease. In addition to genetics, epigenetic alterations also play a crucial role in prostate cancer development. Aberrant DNA methylation, changes in chromatin structure, and microRNA (miRNA) expression are major epigenetic events that influence oncogenesis. Existing markers for prostate cancer, such as prostate-specific antigen (PSA), have limitations in terms of sensitivity and specificity. The cost of testing, follow-up procedures, and treatment for false-positive results and overdiagnosis contributes to the overall healthcare expenditure. Improving the effectiveness of prostate cancer diagnosis and prognosis requires either narrowing the risk group by identifying new genetic factors or enhancing the sensitivity and specificity of existing markers. Immunological biomarkers (both circulating and intra-tumoral), including markers of immune response and immune dysfunction, represent a potentially useful area of research for enhancing the diagnosis and prognosis of prostate cancer. Our review emphasizes the need for developing novel immunological biomarkers to improve the diagnosis, prognosis, and management of prostate cancer. We highlight the most recent achievements in the identification of biomarkers provided by circulating monocytes and tumor-associated macrophages (TAMs). We highlight that monocyte-derived and TAM-derived biomarkers can enable to establish the missing links between genetic predisposition, hormonal metabolism and immune responses in prostate cancer.

Symphony in the crowd: Key genetic alterations in prostate cancer

Androgen receptor (AR) signaling have been frequently targeted for treating prostate cancer (PCa). Even though primarily patients receive a good therapeutic outcome by targeting AR signaling axis, eventually it emerges resistance by altering the genetic makeup of prostate cells. However, to develop an effective therapeutic regime, it is essential to recognize key genetic alterations in PCa. The most common genetic alterations that give rise to distinct androgen different differentiation states are gene fusion of TMPRSS2 with ETS family genes, deletion, or mutation of tumor suppressor PTEN and TP53 gene, amplification or splicing of AR, altered DNA repair genes. In this review, we describe key genes and genetic changes that have been recognized to contribute to altered prostate environment.

Variations in genomic regions encoding long non-coding RNA genes associated with increased prostate cancer risk

From a single restriction fragment length polymorphism analysis to next generation sequencing analysis that screens the entire human genome, testing for genomic variations provides a great and robust approach to cancer testing. Non-coding RNAs have been shown to have a major impact on the development and progression of human cancers, including prostate cancer. However, the low stability of these molecules under laboratory conditions has made their clinical utility challenging, as in the case of PCA3 long non-coding RNA. Since testing for variations in genomic regions encoding non-coding RNAs offers a promising approach for cancer testing, identification and interpretation of single nucleotide polymorphisms associated with prostate cancer susceptibility is of great interest. Accordingly, here, for the first time, we review and discuss current available knowledge about genomic variation of long non-coding RNA molecules in prostate cancer.

Human Genomics and Drug Development

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.