Exploring Key Biomarkers and Common Pathogenesis of Seven Digestive System Cancers and Their Correlation with COVID-19

Xiong, Zuming; Yang, Yongjun; Li, Wenxin; Lin, Yirong; Huang, Wei; Zhang, Sen

doi:10.3390/cimb45070349

Cited by 3 publications

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, a global gene expression study and docking analysis con rmed the overexpression of OLFM4 in COVID-19 infection (24). Although KIF14 has been shown to be important in the pathophysiology of COVID-19, especially in the co-pathogenesis with digestive cancers, its role in ARDS is unclear (25). This discrepancy not only highlights the complexity of gene expression in different disease conditions, but also provides a new direction for future studies to explore in depth the potential function and role of KIF14 in ARDS.…”

Section: Discussionmentioning

confidence: 99%

Combination of multiple omics and machine learning identifies diagnostic genes for ARDS and COVID-19

Tian,

Guo,

Guan

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Acute respiratory distress syndrome (ARDS) is a common acute clinical syndrome of the respiratory system with a high mortality rate and difficult prognosis.COVID-19 is a serious respiratory infectious disease caused by coronaviruses in a global pandemic. Some studies have suggested a possible association between COVID-19 and ARDS, but few studies have investigated the mechanism of interaction between them. METHODS Microarray data of ARDS (GSE32707 and GSE66890) and COVID-19 (GSE213313) were downloaded from the GEO database and searched for common differential genes for enrichment analysis.WGCNA was used to identify co-expression modules and genes associated with ARDS and COVID-19. RF and LASSO were performed for candidate gene identification. Machine learning XGBoost improved the diagnosis of hub genes in ARDS and COVID-19. The degree of immune cell infiltration in ARDS and COVID-19 samples was assessed using the CIBERSORT algorithm, and the relationship between hub genes and infiltrating immune cells was investigated. Changes in pathway activity per cell were visualized using Seurat standard flow down clustering (seurat) to visualize peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing (scRNA-seq) data from patients with sepsis-combined ARDS and patients with sepsis alone. RESULTS Limma difference analysis identified 314 up-regulated genes and 241 down-regulated genes in ARDS and COVID-19.WGCNA identified the purple-red co-expression module as the core module of ARDS and COVID-19. Five candidate genes, namely HIST1H2BK, TCF4, OLFM4, KIF14 and HK1, were screened using two machine learning algorithms, RF and LASSO. XGBoost constructed diagnostic models to evaluate the hub genes with high diagnostic efficacy in ARDS and COVID-19. Single-cell sequencing revealed the presence of alterations in five immune subpopulations, including monocytes, B cells, T cells, NK cells and platelets, with high expression levels and cellular occupancy of TCF4 and HK1, which are involved in oxidative reactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Combination of multiple omics and machine learning identifies diagnostic genes for ARDS and COVID-19

Tian,

Guo,

Guan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[ 51 ] A bioinformatics analysis revealed KIF4A as a key biomarker for COVID-19. [ 52 ] CDK1 is a protein-coding gene that regulates the cell cycle by controlling the transition from the G2 phase to the M phase, playing a pivotal role in cell division and proliferation. [ 53 ] Pharmacological or genetic inactivation of CDK1 not only blocks Jun-dependent immune checkpoint expression but also triggers histone-dependent immunogenic cell death in cancer cells in response to IFN-γ.…”

Section: Discussionmentioning

confidence: 99%

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches

Fang,

Sun,

Wen

et al. 2024

Medicine

View full text Add to dashboard Cite

Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear. Methods: This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein–protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC. Results: A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein–protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC. Conclusion: This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.

show abstract

Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations

Kanwal,

Zhang

2024

Front. Immunol.

View full text Add to dashboard Cite

The term "Long-COVID" (LC) is characterized by the aftereffects of COVID-19 infection. Various studies have suggested that Epstein–Barr virus (EBV) reactivation is among the significant reported causes of LC. However, there is a lack of in-depth research that could largely explore the pathogenic mechanism and pinpoint the key genes in the EBV and LC context. This study mainly aimed to predict the potential disease-associated common genes between EBV reactivation and LC condition using next-generation sequencing (NGS) data and reported naturally occurring biomolecules as inhibitors. We applied the bulk RNA-Seq from LC and EBV-infected peripheral blood mononuclear cells (PBMCs), identified the differentially expressed genes (DEGs) and the Protein–Protein interaction (PPI) network using the STRING database, identified hub genes using the cytoscape plugins CytoHubba and MCODE, and performed enrichment analysis using ClueGO. The interaction analysis of a hub gene was performed against naturally occurring bioflavonoid molecules using molecular docking and the molecular dynamics (MD) simulation method. Out of 357 common genes, 22 genes (CCL2, CCL20, CDCA2, CEP55, CHI3L1, CKAP2L, DEPDC1, DIAPH3, DLGAP5, E2F8, FGF1, NEK2, PBK, TOP2A, CCL3, CXCL8, DEPDC1, IL6, RETN, MMP2, LCN2, and OLR1) were classified as hub genes, and the remaining ones were classified as neighboring genes. Enrichment analysis showed the role of hub genes in various pathways such as immune-signaling pathways, including JAK-STAT signaling, interleukin signaling, protein kinase signaling, and toll-like receptor pathways associated with the symptoms reported in the LC condition. ZNF and MYBL TF-family were predicted as abundant TFs controlling hub genes' transcriptional machinery. Furthermore, OLR1 (PDB: 7XMP) showed stable interactions with the five shortlisted refined naturally occurring bioflavonoids, i.e., apigenin, amentoflavone, ilexgenin A, myricetin, and orientin compounds. The total binding energy pattern was observed, with amentoflavone being the top docked molecule (with a binding affinity of –8.3 kcal/mol) with the lowest total binding energy of −18.48 kcal/mol. In conclusion, our research has predicted the hub genes, their molecular pathways, and the potential inhibitors between EBV and LC potential pathogenic association. The in vivo or in vitro experimental methods could be utilized to functionally validate our findings, which would be helpful to cure LC or to prevent EBV reactivation.

show abstract

Exploring Key Biomarkers and Common Pathogenesis of Seven Digestive System Cancers and Their Correlation with COVID-19

Cited by 3 publications

References 62 publications

Combination of multiple omics and machine learning identifies diagnostic genes for ARDS and COVID-19

Combination of multiple omics and machine learning identifies diagnostic genes for ARDS and COVID-19

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches

Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations

Contact Info

Product

Resources

About