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Liver cancer, primarily hepatocellular carcinoma, remains a global health challenge with rising incidence and limited therapeutic options. Genetic factors play a pivotal role in the development and progression of liver cancer. This state-of-the-art paper provides a comprehensive review of the current landscape of genetic screening strategies for liver cancer. We discuss the genetic underpinnings of liver cancer, emphasizing the critical role of risk-associated genetic variants, somatic mutations, and epigenetic alterations. We also explore the intricate interplay between environmental factors and genetics, highlighting how genetic screening can aid in risk stratification and early detection via using liquid biopsy, and advancements in high-throughput sequencing technologies. By synthesizing the latest research findings, we aim to provide a comprehensive overview of the state-of-the-art genetic screening methods for liver cancer, shedding light on their potential to revolutionize early detection, risk assessment, and targeted therapies in the fight against this devastating disease.

Colorectal cancer (CRC) is one of the most frequent types of cancer, with high incidence rates and mortality globally. The extended timeframe for developing CRC allows for the potential screening and early identification of the disease. Furthermore, studies have shown that survival rates for patients with cancer are increased when diagnoses are made at earlier stages. Recent research suggests that the development of CRC, including its precancerous lesion, is influenced not only by genetic factors but also by epigenetic variables. Studies suggest epigenetics plays a significant role in cancer development, particularly CRC. While this approach is still in its early stages and faces challenges due to the variability of CRC, it shows promise as a potential method for understanding and addressing the disease. This review examined the current evidence supporting genetic and epigenetic biomarkers for screening and diagnosis. In addition, we also discussed the feasibility of translating these methodologies into clinical settings. Several markers show promising potential, including the methylation of vimentin (VIM), syndecan-2 (SDC2), and septin 9 (SEPT9). However, their application as screening and diagnostic tools, particularly for early-stage CRC, has not been fully optimized, and their effectiveness needs validation in large, multi-center patient populations. Extensive trials and further investigation are required to translate genetic and epigenetic biomarkers into practical clinical use. biomarkers, diagnostic biomarkers.

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, surface-enhanced Raman spectroscopy (SERS)-based detection of biomarkers has attracted growing interest in this area. Oligonucleotides are an important type of genetic biomarkers as their alterations can be linked to the disease prior to symptom onset. We propose a machine-learning (ML)-enabled framework to analyze complex direct SERS spectra of short, single-stranded DNA and RNA targets to identify relevant mutations occurring in genetic biomarkers, which are key disease indicators. First, by employing ad hoc-synthesized colloidal silver nanoparticles as SERS substrates, we analyze single-base mutations in ssDNA and RNA sequences using a direct SERS-sensing approach. Then, an ML-based hypothesis test is proposed to identify these changes and differentiate the mutated sequences from the corresponding native ones. Rooted in \"functional data analysis,\" this ML approach fully leverages the rich information and dependencies within SERS spectral data for improved modeling and detection capability. Tested on a large set of DNA and RNA SERS data, including from miR-21 (a known cancer miRNA biomarker), our approach is shown to accurately differentiate SERS spectra obtained from different oligonucleotides, outperforming various data-driven methods across several performance metrics, including accuracy, sensitivity, specificity, and F1-scores. Hence, this work represents a step forward in the development of the combined use of SERS and ML as effective methods for disease diagnosis with real applicability in the clinic.

Parkinson\'s disease (PD) is a complex neurological, degenerative clinical condition depicted by the advancing loss of dopaminergic neurons in the substantia nigra pars compacta, which manifests itself as a myriad of sensorimotor and non-motor signs in patients. The disease occurs due to the reduced levels of the neurotransmitter dopamine in the brain, which is primarily associated with functional characteristics regarding mobility and cognition. The basal ganglion is mainly involved in the generation of cognitive functions and therefore is the most significantly associated area in PD. Since the classical diagnosis and assessment of PD depends majorly on the appearance of motor characteristics, which only arise when ~60-80% of the dopamine neuronal cell death has already occurred, it is imperative we focus on identifying biomarkers that can help us assess and diagnose PD in the earlier stages of disease progression, thus providing a better prognosis for the patients. This review article will focus on the different biomarkers that are currently available and in use, divided under the headings of clinical, biological, imaging, and genetic biomarkers, and assess their specificity and sensitivity toward providing an early assessment of Parkinson\'s for the patients and the future of preclinical diagnostics using molecular biomarkers. PD affects over 1% of the population worldwide and only ranks second to Alzheimer\'s disease in the context of its incidence and consequent socioeconomic burden. While recent breakthroughs in biomarkers have dramatically improved patients\' odds of survival and prognosis, it still remains primarily a symptomatic diagnostic tool. It is an area of research that requires to focus on creating more advanced approaches toward diagnosing PD early, involving clinical diagnostics, neuroimaging technology, and molecular biology collaborations to provide the highest degree of care and quality of life that a Parkinson\'s patient deserves.

UNASSIGNED: Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.
UNASSIGNED: Through three methodological approaches, 122 genes and 184 Single Nucleotide Polymorphisms (SNPs) associated with PTSD were compiled into a single gene repository for PTSD. Using PharmGKB and DrugTargetor, 323 drug candidates were identified to target these 122 genes. The top 17 drug candidates were selected based on the statistical significance of the genetic associations, and their promiscuity (number of associated genestargets) and were further assessed for their suitability in terms of bioavailability and drug-like characteristics. Through functional analysis, insights were gained into the biological processes, cellular components, and molecular functions involved in PTSD. This formed the foundation for the next aspect of this study which was to propose an efficient treatment for PTSD by exploring drug repurposing methods.
UNASSIGNED: The main aim was to identify the drugs with the most favorable profile that can be used as a pharmacological approach for PTSD treatment. More in particular, according to the genetic variations present in each individual, the relevant biological pathway can be identified, and the drug candidate proposed will specifically target said pathway, accounting for the personalized aspect of this work. The results showed that the drugs used as off-label treatment for PTSD have favorable pharmacokinetic profiles and the potential drug candidates that arose from DrugTargetor were not very promising. Clozapine showed a promising pharmacokinetic profile and has been linked with decreased psychiatric symptoms. Ambrucin also showed a promising pharmacokinetic profile but has been mostly linked with cancer treatment.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as \"presynapse,\" \"behavior,\" and \"modulation of chemical synaptic transmission\" in autism spectrum disorder\'s brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Platinum-based chemotherapy (PBC) is a widely used treatment for various solid tumors, including non-small cell lung cancer (NSCLC). However, its efficacy is often compromised by the emergence of drug resistance in patients. There is growing evidence that genetic variations may influence the susceptibility of NSCLC patients to develop resistance to PBC. Here, we provide a comprehensive overview of the mechanisms underlying platinum drug resistance and highlight the important role that genetic polymorphisms play in this process. This paper discussed the genetic variants that regulate DNA repair, cellular movement, drug transport, metabolic processing, and immune response, with a focus on their effects on response to PBC. The potential applications of these genetic polymorphisms as predictive indicators in clinical practice are explored, as are the challenges associated with their implementation.

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes\' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Increased albuminuria indicates underlying glomerular pathology and is associated with worse renal disease outcomes, especially in diabetic kidney disease. Many single nucleotide polymorphisms (SNPs), associated with albuminuria, could be potentially useful to construct polygenic risk scores (PRSs) for kidney disease. We investigated the diagnostic accuracy of SNPs, previously associated with albuminuria-related traits, on albuminuria and renal injury in the UK Biobank population, with a particular interest in diabetes. Multivariable logistic regression was used to evaluate the influence of 91 SNPs on urine albumin-to-creatinine ratio (UACR)-related traits and kidney damage (any pathology indicating renal injury), stratifying by diabetes. Weighted PRSs for microalbuminuria and UACR from previous studies were used to calculate the area under the receiver operating characteristic curve (AUROC). CUBN-rs1801239 and DDR1-rs116772905 were associated with all the UACR-derived phenotypes, in both the overall and non-diabetic cohorts, but not with kidney damage. Several SNPs demonstrated different effects in individuals with diabetes compared to those without. SNPs did not improve the AUROC over currently used clinical variables. Many SNPs are associated with UACR or renal injury, suggesting a role in kidney dysfunction, dependent on the presence of diabetes in some cases. However, individual SNPs or PRSs did not improve the diagnostic accuracy for albuminuria or renal injury compared to standard clinical variables.