Publication date: Nov 03, 2024
Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p
| Concepts | Keywords |
|---|---|
| Algorithms | bioinformatics analysis |
| Immunotherapy | immune cell infiltration |
| Landscape | lung adenocarcinoma |
| Tumor | SARS‐CoV‐2 infection |
| Viruses | transcriptomics |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | lung adenocarcinoma |
| disease | MESH | hematological malignancies |
| disease | MESH | SARS-CoV-2 infection |
| pathway | REACTOME | SARS-CoV-2 Infection |
| disease | MESH | clinical significance |
| disease | MESH | tumor |
| disease | IDO | cell |
| disease | MESH | infection |