Hidden patterns of gene expression provide prognostic insight for colorectal cancer

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

A Multi-Omics Overview of Colorectal Cancer to Address Mechanisms of Disease, Metastasis, Patient Disparities and Outcomes

Human colorectal cancer (CRC) is one of the most common malignancies in men and women across the globe, albeit CRC incidence and mortality shows a substantial racial and ethnic disparity, with the highest burden in African American patients. Even with effective screening tools such as colonoscopy and diagnostic detection assays, CRC remains a substantial health burden. In addition, primary tumors located in the proximal (right) or distal (left) sides of the colorectum have been shown to be unique tumor types that require unique treatment schema. Distal metastases in the liver and other organ systems are the major causes of mortality in CRC patients. Characterizing genomic, epigenomic, transcriptomic and proteomic (multi-omics) alterations has led to a better understanding of primary tumor biology, resulting in targeted therapeutic advancements. In this regard, molecular-based CRC subgroups have been developed that show correlations with patient outcomes. Molecular characterization of CRC metastases has highlighted similarities and differences between metastases and primary tumors; however, our understanding as to how to improve patient outcomes based on metastasis biology is lagging and remains a major obstacle to improving CRC patient outcomes. In this review, we will summarize the multi-omics features of primary CRC tumors and their metastases across racial and ethnic groups, the differences in proximal and distal tumor biology, molecular-based CRC subgroups, treatment strategies and challenges for improving patient outcomes.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.