ArticleMolecular medicine reports2018
Bioinformatics analysis to screen for critical genes between survived and non‑survived patients with sepsis.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- The synergistic effect of resveratrol and curcumin on oncogenic lncRNA MAPT-IT1 and ferroptosis-related genes in breast cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Mechanisms of MTA3 in cancer and related diseases and its clinical applications.Frontiers in oncology · 2025Review
- Recurrent Implantation Failure: Bioinformatic Discovery of Biomarkers and Identification of Metabolic Subtypes.International journal of molecular sciences · 2023Article
- Review
- PRKCA Promotes Mitophagy through the miR-15a-5p/PDK4 Axis to Relieve Sepsis-Induced Acute Lung Injury.Infection and immunity · 2023Article
- Construction and Analysis of ceRNA Networks Reveal the Key Genes Associated with Bovine Herpesvirus Type 1 Infection.Infection and drug resistance · 2023Article
- Altered molecular pathways and prognostic markers in active systemic juvenile idiopathic arthritis: integrated bioinformatic analysis.Bosnian journal of basic medical sciences · 2022Article
- Identification of gene biomarkers with expression profiles in patients with allergic rhinitis.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2022Article
- Article
- Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients.Frontiers in oncology · 2021Article
- Bioinformatics-based identification of miRNA-, lncRNA-, and mRNA-associated ceRNA networks and potential biomarkers for preeclampsia.Medicine · 2020Article
- Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis.Molecular medicine reports · 2020Article
- Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Observational
- Inhibiting DNA Methylation Improves Survival in Severe Sepsis by Regulating NF-κB Pathway.Frontiers in immunology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a systemic inflammatory response syndrome, which is mostly induced by infection in the lungs, the abdomen and the urinary tract. The present study is aimed to investigate the mechanisms of sepsis. Expression profile of E‑MTAB‑4421 (including leukocytes isolated from 207 survived and 58 non‑survived patients with sepsis) and E‑MTAB‑4451 (including leukocytes isolated from 56 survived and 50 non‑survived patients with sepsis) were downloaded from the European Bioinformatics Institute database. Based on the E‑MTAB‑4421 expression profile, several differentially expressed genes (DEGs) were identified and performed with hierarchical clustering analysis by the limma and pheatmap packages in R. Using the BioGRID database and Cytoscape software, a protein‑protein interaction (PPI) network was constructed for the DEGs. Furthermore, module division and module annotation separately were conducted by the Mcode and BiNGO plugins in Cytoscape software. Additionally, the support vector machine (SVM) classifier was constructed by the SVM function of e1071 package in R, and then verified using the dataset of E‑MTAB‑4451. A total of 384 DEGs were screened in the survival group. The PPI network was divided into 4 modules (modules A, B, C and D) involving 11 DEGs including microtubule‑associated protein 1 light chain 3 alpha (MAP1LC3A), protein kinase C‑alpha (PRKCA), metastasis associated 1 family member 3 (MTA3), and scribbled planar cell polarity protein (SCRIB). SCRIB and PRKCA in module B, as well as MAP1LC3A and MTA3 in module D, might function in sepsis through PPIs. Functional enrichment demonstrated that MAP1LC3A in module D was enriched in autophagy vacuole assembly. Finally, the SVM classifier could correctly and effectively identify the samples in E‑MTAB‑4451. In conclusion, DEGs such as MAP1LC3A, PRKCA, MTA3 and SCRIB may be implicated in the progression of sepsis, and need further and more thorough confirmation.
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