Search

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

The article discusses the role of cancer genomics in the era of precision medicine. It highlights how advancements in sequencing technology and big data analysis have made genomics a key component of cancer research, providing insights into disease etiology, risk assessment, and treatment resistance mechanisms. The article also emphasizes the use of cancer genomics in guiding clinical decisions, such as determining hereditary cancer risk, predicting prognosis, and informing treatment selection. Additionally, it explores the classification of acute myeloid leukemia patients based on genomic features and chromosomal abnormalities, as well as the impact of epigenetic modifications on tumorigenesis. Overall, the article demonstrates how cancer genomics can contribute to a more personalized approach to cancer treatment and care. [Extracted from the article]

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is an umbrella term that encompasses several disabling neurodevelopmental conditions. These conditions are characterized by impaired manifestation in social and communication skills with repetitive and restrictive behaviors or interests. Thus far, there are no approved biomarkers for ASD screening and diagnosis; also, the current diagnosis depends heavily on a physician's assessment and family's awareness of ASD symptoms. Identifying blood proteomic biomarkers and performing deep blood proteome profiling could highlight common underlying dysfunctions between cases of ASD, given its heterogeneous nature, thus laying the foundation for large-scale blood-based biomarker discovery studies. This study measured the expression of 1196 serum proteins using proximity extension assay (PEA) technology. The screened serum samples included ASD cases (n = 91) and healthy controls (n = 30) between 6 and 15 years of age. Our findings revealed 251 differentially expressed proteins between ASD and healthy controls, of which 237 proteins were significantly upregulated and 14 proteins were significantly downregulated. Machine learning analysis identified 15 proteins that could be biomarkers for ASD with an area under the curve (AUC) = 0.876 using support vector machine (SVM). Gene Ontology (GO) analysis of the top differentially expressed proteins (TopDE) and weighted gene co-expression analysis (WGCNA) revealed dysregulation of SNARE vesicular transport and ErbB pathways in ASD cases. Furthermore, correlation analysis showed that proteins from those pathways correlate with ASD severity. Further validation and verification of the identified biomarkers and pathways are warranted. [ABSTRACT FROM AUTHOR]

Dietary selenium intake has been linked to reduced cancer risk, however the underlying mechanisms are yet unknown. We question the commonly used practice of applying selenium concentrations found in human blood to in vitro studies and evaluated the utility of bio markers, e.g., glutathione peroxidase 1 (GPx1) and thioredoxin reductase 1 (TrxR1), to deter mine appropriate selenium levels for in vitro work. Furthermore, we investigated the effects of Se-methylselenocysteine (SeMSC) on prostate cancer cell migration and invasion. After excluding cytotoxicity, we demonstrated that prostate cancer cell lines respond differently to selenium treatment as observed through biomarker assessment. We found that the maximum levels of GPx1 activity and TrxR1 expression were reachedatlower seleniumcon centrations in LNCaP compared to PC3 cells, and PC3 compared to DU145 cells. Therefore the use of selenium concentrations extrapolated from human studies for in vitro work may be applicable when further informed using a readout of selenium repletion including use of selenium responsive biomarkers. No effect on PC3 migration or invasion was observed after long term SeMSC treatment; however a slight increase was found when treatment was solely administered during the assay. The opposite could be observed when cells were cultured under low serum conditions, with a significant increase in migration upon long term but not upon acute SeMSC treatment. To conclude, these findings indicate that it is imperative to study the selenium sensitivity of an in vitro model preferably using biomarkers before investigating any effects on biological processes, or before comparing models. [ABSTRACT FROM AUTHOR]