YRNAs and YRNA-Derived Fragments as New Players in Cancer Research and Their Potential Role in Diagnostics.

YRNAs are a type of short, noncoding RNAs. A total of four different transcripts can be distinguished, which are YRNA1, YRNA3, YRNA4 and YRNA5. All YRNAs are relatively small, made up of about 100 nucleotides each. YRNAs are characterized by a stem-loop structure and each part of that structure carries a different function. YRNAs are transcribed in the nucleus by RNA polymerase III. Then, the YRNA molecule is bound to the polyuridine tail of the La protein responsible for both its nuclear retention and protection from degradation. They also bind to the Ro60 protein, making the molecule more stable. In turn, YRNA-derived small RNAs (YsRNAs) are a class of YRNAs produced in apoptotic cells as a result of YRNA degradation. This process is performed by caspase-3-dependent pathways that form two groups of YsRNAs, with lengths of either approximately 24 or 31 nucleotides. From all four YRNA transcripts, 75 well-described pseudogenes are generated as a result of the mutation. However, available data indicates the formation of up to 1000 pseudogenes. YRNAs and YRNA-derived small RNAs may play a role in carcinogenesis due to their altered expression in cancers and influence on cell proliferation and inflammation. Nevertheless, our knowledge is still limited, and more research is required. The main aim of this review is to describe the current state of knowledge about YRNAs, their function and contribution to carcinogenesis, as well as their potential role in cancer diagnostics. To confirm the promising potential of YRNAs and YRNA-derived fragments as biomarkers, their significant role in several tumor types was taken into consideration.

Gene delivery methods and genome editing of human pluripotent stem cells.

Induced pluripotent stem cells derived from normal somatic cells could be utilized to study tumorigenesis through overexpression of specific oncogenes, downregulation of tumor suppressors and dysregulation of other factors thought to promote tumorigenesis. Therefore, effective approaches that provide direct modifications of induced pluripotent stem cell genome are extremely needed. Emerging strategies are expected to provide the ability to more effectively introduce diverse genetic alterations, from as small as single-nucleotide modifications to whole gene amplification or deletion, all with a high degree of target specificity. To date, several techniques have been applied in stem cell studies to directly edit cell genome (ZFNs, TALENs or CRISPR/Cas9). In this review, we summarize specific gene delivery strategies that were applied to stem cell studies together with genome editing techniques, which enable a direct modification of endogenous DNA sequences in the context of cancer studies.

Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin.

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregulation and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression constitutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcriptional and translational changes, shedding light on the tumor's epigenetic landscape and the role of its cell-of-origin.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of ß-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness.

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine.

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.