Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes

The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications

Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87-100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.

Towards precision oncology discovery: four less known genes and their unknown interactions as highest-performed biomarkers for colorectal cancer

The goal of this study was to use a new interpretable machine-learning framework based on max-logistic competing risk factor models to identify a parsimonious set of differentially expressed genes (DEGs) that play a pivotal role in the development of colorectal cancer (CRC). Transcriptome data from nine public datasets were analyzed, and a new Chinese cohort was collected to validate the findings. The study discovered a set of four critical DEGs - CXCL8, PSMC2, APP, and SLC20A1 - that exhibit the highest accuracy in detecting CRC in diverse populations and ethnicities. Notably, PSMC2 and CXCL8 appear to play a central role in CRC, and CXCL8 alone could potentially serve as an early-stage marker for CRC. This work represents a pioneering effort in applying the max-logistic competing risk factor model to identify critical genes for human malignancies, and the interpretability and reproducibility of the results across diverse populations suggests that the four DEGs identified can provide a comprehensive description of the transcriptomic features of CRC. The practical implications of this research include the potential for personalized risk assessment and precision diagnosis and tailored treatment plans for patients.

Role of miRNAs to control the progression of Chronic Myeloid Leukemia by their expression levels

Chronic Myeloid Leukemia (CML) is a myeloproliferative disorder distinguished by a specific genetic anomaly known as a reciprocal translocation between chromosomes 9 and 22. This translocation causes fusion between the BCR and ABL regions. Consequently, BCR::ABL oncoprotein is formed, which plays a significant role in driving CML progression. Imatinib, a tyrosine kinase inhibitor (TKI), became the first line of drugs against CML. However, with continuous treatment, patients developed resistance against it. Indeed, to address this challenge, microRNA-based therapy emerges as a promising approach. miRNAs are 20-25 nucleotides long and hold great significance in various cellular processes, including cell differentiation, proliferation, migration, and apoptosis. In several malignancies, it has been reported that miRNAs might help to promote or prevent tumourigenesis and abnormal expression because they could act as both oncogenes/tumor suppressors. Recently, because of their vital regulatory function in maintaining cell homeostasis, miRNAs might be used to control CML progression and in developing new therapies for TKI-resistant patients. They might also act as potential prognostic, diagnostic, and therapeutic biomarkers based on their expression profiles. Various annotation tools and microarray-based expression profiles can be used to predict dysregulated miRNAs and their target genes. The main purpose of this review is to provide brief insights into the role of dysregulated miRNAs in CML pathogenesis and to emphasize their clinical relevance, such as their significant potential as therapeutics against CML. Utilizing these miRNAs as a therapeutic approach by inhibition or amplification of their activity could unlock new doors for the therapy of CML.

Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.

Genomic Transcriptome Benefits and Potential Harms of COVID-19 Vaccines Indicated from Optimized Genomic Biomarkers

COVID-19 vaccines can be the tugboats for preventing SARS-CoV-2 infections when they are practical and, more importantly, without adverse effects. However, the reality is that they may result in short-term or long-term impacts on COVID-19-related diseases and even trigger the formation of new variants of SARS-CoV-2. Using published data, we use a set of optimized-performance COVID-19 genomic biomarkers (MND1, CDC6, ZNF282) to study the benefits and adverse effects of the BNT162b2 vaccine. We found that the vaccine lowered the expression values of genes MND1 and CDC6 while heightening the expression values of ZNF282 in individuals who are SARS-CoV-2 naïve, which is expected and satisfies the biological equivalence between the COVID-19 disease and the genomic signature patterns established in the literature. However, we also found that COVID-19-convalescent octogenarians responded reversely. The vaccine heightened the expression values of MND1 and CDC6. In addition, it lowered the expression values of ZNF282. Such adverse effects raise outstanding concerns about whether or not COVID-19-convalescent individuals should take the current vaccine or when they can take it. These findings are new at the genomic level and can provide insights into developing next-generation vaccines, antiviral drugs, and pandemic management guidance.

Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19

Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

de Magalhães has shown recently that most human genes have several papers in PubMed mentioning cancer, leading the author to suggest that every gene is associated with cancer, a conclusion that contradicts the widely held view that cancer is driven by a limited number of cancer genes, whereas the majority of genes are just bystanders in carcinogenesis. We have analyzed PubMed to decide whether publication metrics supports the distinction of bystander genes and cancer genes. The dynamics of publications on known cancer genes followed a similar pattern: seminal discoveries triggered a burst of cancer-related publications that validated and expanded the discovery, resulting in a rise both in the number and proportion of cancer-related publications on that gene. The dynamics of publications on bystander genes was markedly different. Although there is a slow but continuous time-dependent rise in the proportion of papers mentioning cancer, this phenomenon just reflects the increasing publication bias that favors cancer research. Despite this bias, the proportion of cancer papers on bystander genes remains low. Here, we show that the distinctive publication dynamics of cancer genes and bystander genes may be used for the identification of cancer genes.

The Existence of at Least Three Genomic Signature Patterns and at Least Seven Subtypes of COVID-19 and the End of the Disease

Hoping to find genomic clues linked to COVID-19 and end the pandemic has driven scientists’ tremendous efforts to try all kinds of research. Signs of progress have been achieved but are still limited. This paper intends to prove the existence of at least three genomic signature patterns and at least seven subtypes of COVID-19 driven by five critical genes (the smallest subset of genes) using three blood-sampled datasets. These signatures and subtypes provide crucial genomic information in COVID-19 diagnosis (including ICU patients), research focuses, and treatment methods. Unlike existing approaches focused on gene fold-changes and pathways, gene-gene nonlinear and competing interactions are the driving forces in finding the signature patterns and subtypes. Furthermore, the method leads to high accuracy with hospitalized patients, showing biological and mathematical equivalences between COVID-19 status and the signature patterns and a methodological advantage over other methods that cannot lead to high accuracy. As a result, as new biomarkers, the new findings and genomic clues can be much more informative than other findings for interpreting biological mechanisms, developing the second (third) generation of vaccines, antiviral drugs, and treatment methods, and eventually bringing new hopes of an end to the pandemic.