Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

A novel halogenated adenosine analog 5'-BrDA displays potent toxicity against colon cancer cells in vivo and in vitro.

Adenosine, as a naturally occurring nucleoside, plays an important role in human health maintenance. In recent years, many studies have shown that adenosine has the effect of cancer inhibition, and some of its analogs have been successfully marketed as anticancer drugs. This report mainly describes the anti-colon cancer activities and mechanism of a novel halogenated adenosine analog named 5'-bromodeoxyadenosine (5'-BrDA). As a result, 5'-BrDA concentration-dependently inhibited colon cancer cells proliferation, induced autophagy without disruption of lysosomal stability, and promoted autophagy-independently cellular mitochondrial apoptosis by increasing the accumulation of reactive oxygen species. Furthermore, 5'-BrDA inhibited the tumor growth of colon cancer in CT26 inbred mice without affecting the body weight in vivo. Collectively, the above-mentioned mechanisms contributed to the anticancer activity of 5'-BrDA. It is rare to discover novel anticancer adenosine analogs during the past couple of decades. We believe that our work will enrich the understanding of adenosine analogs, also, pave the way for adenosine analogs product based anticancer drug development.