Identification of Somatic Mutations in Dementia-related Genes in Cancer Patients

Regulatory mechanism of circular RNAs in neurodegenerative diseases

BACKGROUND

Neurodegenerative disease is a collective term for a category of diseases that are caused by neuronal dysfunction, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Circular RNAs (circRNAs) are a class of non-coding RNAs without the 3' cap and 5' poly(A) and are linked by covalent bonds. CircRNAs are highly expressed in brain neurons and can regulate the pathological process of neurodegenerative diseases by affecting the levels of various deposition proteins.

AIMS

This review is aiming to suggest that the majority of circRNAs influence neurodegenerative pathologies mainly by affecting the abnormal deposition of proteins in neurodegenerative diseases.

METHODS

We systematically summarized the pathological features of neurodegenerative diseases and the regulatory mechanisms of circRNAs in various types of neurodegenerative diseases.

RESULTS

Neurodegenerative disease main features include intercellular ubiquitin-proteasome system abnormalities, changes in cytoskeletal proteins, and the continuous deposition of insoluble protein fragments and inclusion bodies in the cytoplasm or nucleus, resulting in impairment of the normal physiological processes of the neuronal system. CircRNAs have multiple mechanisms, such as acting as microRNA sponges, binding to proteins, and regulating transcription. CircRNAs, which are highly stable molecules, are expected to be potential biomarkers for the pathological detection of neurodegenerative diseases such as AD and PD.

CONCLUSIONS

In this review, we describe the regulatory roles and mechanisms of circRNAs in neurodegenerative diseases and aim to employ circRNAs as biomarkers for the diagnosis and treatment of neurodegenerative diseases.

Coexisting Biopsy-Diagnosed Dementia and Glioblastoma

Both glioblastoma (GBM) and dementia are devastating diseases with limited treatments that are usually not curative. Having clinically diagnosed dementia with an associated biopsy-proven etiology and a coexisting GBM diagnosis is a rare occurrence. The relationship between the development of neurodegenerative dementia and GBM is unclear, as there are conflicting reports in the literature. We present two cases of simultaneous biopsy-proven dementia, one with Alzheimer's disease (AD) and GBM, and one with cerebral amyloid angiopathy (CAA) and GBM. We discuss how these diseases may be associated. Whether one pathologic process begins first or develops concurrently is unknown, but certain molecular pathways of dementia and GBM appear directly related while others inversely related. Further investigations of these close molecular relationships between dementia and GBM could lead to development of improved diagnostic tools and therapeutic interventions for both diseases.

Presenilin-1 (PSEN1) Mutations: Clinical Phenotypes beyond Alzheimer's Disease

Presenilin 1 (PSEN1) is a part of the gamma secretase complex with several interacting substrates, including amyloid precursor protein (APP), Notch, adhesion proteins and beta catenin. PSEN1 has been extensively studied in neurodegeneration, and more than 300 PSEN1 mutations have been discovered to date. In addition to the classical early onset Alzheimer's disease (EOAD) phenotypes, PSEN1 mutations were discovered in several atypical AD or non-AD phenotypes, such as frontotemporal dementia (FTD), Parkinson's disease (PD), dementia with Lewy bodies (DLB) or spastic paraparesis (SP). For example, Leu113Pro, Leu226Phe, Met233Leu and an Arg352 duplication were discovered in patients with FTD, while Pro436Gln, Arg278Gln and Pro284Leu mutations were also reported in patients with motor dysfunctions. Interestingly, PSEN1 mutations may also impact non-neurodegenerative phenotypes, including PSEN1 Pro242fs, which could cause acne inversa, while Asp333Gly was reported in a family with dilated cardiomyopathy. The phenotypic diversity suggests that PSEN1 may be responsible for atypical disease phenotypes or types of disease other than AD. Taken together, neurodegenerative diseases such as AD, PD, DLB and FTD may share several common hallmarks (cognitive and motor impairment, associated with abnormal protein aggregates). These findings suggested that PSEN1 may interact with risk modifiers, which may result in alternative disease phenotypes such as DLB or FTD phenotypes, or through less-dominant amyloid pathways. Next-generation sequencing and/or biomarker analysis may be essential in clearly differentiating the possible disease phenotypes and pathways associated with non-AD phenotypes.

Atypical Cognitive Impairment and Recovery in Two Colorectal Cancer Patients

Cognitive impairment in cancer patients can be caused by various factors; in approximately 30% of cancer patients, the symptoms appear before starting treatment. Paraneoplastic limbic encephalitis (PLE) is a rare disease associated with an autoimmune response, and is characterized by memory loss, depression, and personality changes; it is one of the potential causes of cognitive dysfunction in cancer patients. Two patients were previously diagnosed with mild cognitive impairment and maintained clinical stability; after suffering a rapid change in personality and sudden cognitive decline, colorectal cancer was diagnosed within a few months. The patients did not meet the diagnostic criteria for PLE in several tests. The symptoms improved after the underlying cancer was treated, and the patients returned to their previous stable state. Sudden cognitive impairment may appear as an early cancer symptom, and PLE is considered an atypical cause for these symptoms. However, in patients with unexplained PLE-like symptoms who do not meet the diagnostic criteria for PLE, probable etiologies to be considered are the gut–brain connection, CD8+ T-cell-mediated limbic encephalitis, and somatic mutations in dementia-related genes. Currently, few studies have investigated these symptoms, and further research will offer significant therapeutic strategies for cognitive impairment in cancer patients.

NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.

No Association between Single Nucleotide Polymorphisms (SNPs) of the Interferon-Induced Transmembrane Protein 3 (IFITM3) Gene and the Susceptibility of Alzheimer’s Disease (AD)

Background and Objectives: Alzheimer’s disease (AD) is the most common progressive neurodegenerative disorder, characterized by the accumulation of amyloid-beta (Aβ) in the brain. A recent study reported that the interferon-induced transmembrane protein 3 (IFITM3) protein plays a pivotal role in Aβ processing by the γ-secretase complex. Since several single nucleotide polymorphisms (SNPs) of the IFITM3 gene are related to the function and expression levels of the IFITM3 gene, the relationship between genetic polymorphisms in the IFITM3 gene and susceptibility to AD needs to be investigated. Materials and Methods: We investigated the genotype and allele frequencies of IFITM3 polymorphisms in 177 AD patients and 233 matched healthy controls by amplicon sequencing. In addition, we compared the genotype, allele and haplotype frequencies between AD patients and matched controls and performed an association analysis. Results: There were no significant differences in the genotype, allele or haplotype frequency distributions of the IFITM3 polymorphisms between AD patients and matched controls. Conclusions: To the best of our knowledge, this is the first case-control association study of the IFITM3 gene in AD.

In-depth examination of PrPSc in Holstein cattle carrying the E211K somatic mutation of the bovine prion protein gene (PRNP)

Prion diseases are transmissible spongiform encephalopathies caused by deleterious prion protein (PrP^Sc ) derived from normal prion protein (PrP^C ), which is encoded by the prion protein gene (PRNP). We performed an in-depth examination to detect PrP^Sc by using enzyme immunoassay (EIA), real-time quaking-induced conversion reactions (RT-QuIC) and protein misfolding cyclic amplification (PMCA) in nine brain tissues derived from three Holstein cattle carrying the E211K somatic mutation of the bovine PRNP gene. The EIA, RT-QuIC and PMCA analyses were not able to detect the PrP^Sc band in any tested samples. To the best of our knowledge, this report is the first to describe an in-depth examination of PrP^Sc in cattle carrying the E211K somatic mutation of the bovine PRNP gene.