Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis

Omics-based novel strategies in the diagnosis of endometriosis

Endometriosis, an enigmatic and chronic disorder, is considered a debilitating condition despite being benign. Globally, this gynecologic disorder affects up to 10% of females of reproductive age, impacting almost 190 million individuals. A variety of genetic and environmental factors are involved in endometriosis development, hence the pathophysiology and etiology of endometriosis remain unclear. The uncertainty of the etiology of the disease and its complexity along with nonspecific symptoms have led to misdiagnosis or lack of diagnosis of affected people. Biopsy and laparoscopy are referred to as the gold standard for endometriosis diagnosis. However, the invasiveness of the procedure, the unnecessary operation in disease-free women, and the dependence of the reliability of diagnosis on experience in this area are considered the most significant limitations. Therefore, continuous studies have attempted to offer a noninvasive and reliable approach. The recent advances in modern technologies have led to the generation of large-scale biological data sets, known as -omics data, resulting in the proceeding of the -omics century in biomedical sciences. Thereby, the present study critically reviews novel and noninvasive biomarkers that are based on -omics approaches from 2020 onward. The findings reveal that biomarkers identified based on genomics, epigenomics, transcriptomics, proteomics, and metabolomics are potentially able to diagnose endometriosis, predict prognosis, and stage patients, and potentially, in the near future, a multi-panel of these biomarkers will generate clinical benefits.

Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning

Background

The presence of lymph node metastasis leads to a poor prognosis for prostate cancer (Pca). Recently, many studies have indicated that gene signatures may be able to predict the status of lymph nodes. The purpose of this study is to probe and validate a new tool to predict lymph node metastasis (LNM) based on alternative splicing (AS).

Methods

Gene expression profiles and clinical information of prostate adenocarcinoma cohort were retrieved from The Cancer Genome Atlas (TCGA) database, and the corresponding RNA-seq splicing events profiles were obtained from the TCGA SpliceSeq. Limma package was used to identify the differentially expressed alternative splicing (DEAS) events between LNM and non-LNM groups. Eight machine learning classifiers were built to train with stratified five-fold cross-validation. SHAP values was used to explain the model.

Results

333 differentially expressed alternative splicing (DEAS) events were identified. Using correlation filter and the least absolute shrinkage and selection operator (LASSO) method, a 96 AS signature was identified that had favorable discrimination in the training set and validated in the validation set. The linear discriminant analysis (LDA) was the best classifier after 100 iterations of training. The LDA classifier was able to distinguish between LNM and non-LNM with an area under the receiver operating curve of 0.962 ± 0.026 in the training set (D1 = 351) and 0.953 in the validation set (D2 = 62). The decision curve analysis plot proved the clinical application of the AS-based model.

Conclusion

Machine learning combined with AS data could robustly distinguish between LNM and non-LNM in Pca.

Cistrome Partitioning Reveals Convergence of Somatic Mutations and Risk Variants on Master Transcription Regulators in Primary Prostate Tumors

Thousands of noncoding somatic single-nucleotide variants (SNVs) of unknown function are reported in tumors. Partitioning the genome according to cistromes reveals the enrichment of somatic SNVs in prostate tumors as opposed to adjacent normal tissue cistromes of master transcription regulators, including AR, FOXA1, and HOXB13. This parallels enrichment of prostate cancer genetic predispositions over these transcription regulators' tumor cistromes, exemplified at the 8q24 locus harboring both risk variants and somatic SNVs in cis-regulatory elements upregulating MYC expression. However, Massively Parallel Reporter Assays reveal that few SNVs can alter the transactivation potential of individual cis-regulatory elements. Instead, similar to inherited risk variants, SNVs accumulate in cistromes of master transcription regulators required for prostate cancer development.

An exome-wide rare variant analysis of Korean men identifies three novel genes predisposing to prostate cancer

Since prostate cancer is highly heritable, common variants associated with prostate cancer have been studied in various populations, including those in Korea. However, rare and low-frequency variants have a significant influence on the heritability of the disease. The contributions of rare variants to prostate cancer susceptibility have not yet been systematically evaluated in a Korean population. In this work, we present a large-scale exome-wide rare variant analysis of 7,258 individuals (985 cases with prostate cancer and 6,273 controls). In total, 19 rare variant loci spanning 7 genes contributed to an association with prostate cancer susceptibility. In addition to replicating previously known susceptibility genes (e.g., CDYL2, MST1R, GPER1, and PARD3B), 3 novel genes were identified (FDR q < 0.05), including the non-coding RNAs ENTPD3-AS1, LOC102724438, and protein-coding gene SPATA3. Additionally, 6 pathways were identified based on identified variants and genes, including estrogen signaling pathway, signaling by MST1, IL-15 production, MSP-RON signaling pathway, and IL-12 signaling and production in macrophages, which are known to be associated with prostate cancer. In summary, we report novel genes and rare variants that potentially play a role in prostate cancer susceptibility in the Korean population. These observations demonstrated a path towards one of the fundamental goals of precision medicine, which is to identify biomarkers for a subset of the population with a greater risk of disease than others.

Genetic variation: effect on prostate cancer

The crucial role of androgens in the development of prostate cancer is well established. The aim of this review is to examine the role of constitutional (germline) and tumor-specific (somatic) polymorphisms within important regulatory genes of prostate cancer. These include genes encoding enzymes of the androgen biosynthetic pathway, the androgen receptor gene, genes that encode proteins of the signal transduction pathways that may have a role in disease progression and survival, and genes involved in prostate cancer angiogenesis. Characterization of deregulated pathways critical to cancer cell growth have lead to the development of new treatments, including the CYP17 inhibitor abiraterone and clinical trials using novel drugs that are ongoing or recently completed [1]. The pharmacogenetics of the drugs used to treat prostate cancer will also be addressed. This review will define how germline polymorphisms are known affect a multitude of pathways, and therefore phenotypes, in prostate cancer etiology, progression, and treatment.

Fusing transcriptomics to progressive prostate cancer

This commentary highlights the article by Yu et al, describing a set of novel fusion transcripts strongly associated with prostate cancer prognosis.

Das methodische Potenzial der neuen Sequenziertechnologien jenseits der Mutationssuche

In diesem Beitrag wird eine Reihe wichtiger Anwendungen der neuen Sequenziertechnologien bzw. des Next Generation Sequencing (NGS) vorgestellt. An ausgewählten Beispielen werden für jede Methode die Anwendungsmöglichkeiten in der humangenetischen Forschung dargestellt, jeweils das prinzipielle Vorgehen beschrieben und mögliche Quellen für ausführliche Arbeitsanweisungen vorgestellt. Die beschriebenen Techniken umfassen im Einzelnen: RNA-Sequenzierung mittels NGS („RNA-Seq“), Chromatinimmunpräzipitation in Kombination mit NGS („ChIP-Seq“), „ribosome profiling“, Immunpräzipitation methylierter DNA-Segmente in Kombination mit NGS („methylated DNA immunoprecipitation“ bzw. „MeDIP-Seq“) und die HiC-Technik, eine Weiterentwicklung der Chromosome-Conformation-Capture(3c)-Methode.