Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

She, Han; Tan, Lei; Yang, Renjie; Zheng, Jie; Wang, Yi; Du, Yuanlin; Peng, Xiaoyong; Li, Qinghui; Lu, Hongxiang; Xiang, Xinming; Hu, Youli; Liu, Liangming; Li, Tao

doi:10.3389/fgene.2023.1158029

Cited by 7 publications

(6 citation statements)

References 37 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have revealed that the down-regulation of LEF1, BACH2, and BCL2 have good prognostic value in sepsis through bioinformatics analysis based on machine algorithms [42], as we found in the public data. LEF1 can be used as a biomarker in hematologic tumors, oral cancer, and colorectal cancer.…”

Section: Discussionsupporting

confidence: 70%

“…Myocardial dysfunction and liver injury are common organ injuries in sepsis, and they are closely related to necroptosis, so it is vital to nd the key genes of sepsis, especially those related to necroptosis. She et al have preliminarily explored that the potential necroptosis-biomarkers with good prognostic value in sepsis using several machine learning algorithms [42]. This study was conducted to further explore the underlying functions and regulatory networks targeting biomarkers as well as the functionality correlations of which with key necroptotic signaling pathway molecules, such as RIPK1, RIPK3 and MLKL.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

Liu,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers (ATRX, TSC1, CD40, BACH2, BCL2, and LEF1) with survival and diagnostic significance through Kaplan-Meier (KM) and ROC analyses. The ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. We examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. Hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while TF YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified Paclitaxel, Docetaxel, and Rasagiline as potential BCL2-targeting sepsis treatments. Real-Time Quantitative PCR confirmed ATRX, TSC1, and LEF1 down-regulation in sepsis samples, contrasting CD40's increased expression in case samples. Variations in BACH2 and BCL2 expression between disease and normal samples may result from sample differences. In conclusion, ATRX, TSC1, CD40, BACH2, BCL2, and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

show abstract

Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

Liu,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers reported that necroptosis pathway (another form of PCD) was significantly activated in sepsis ( Shashaty et al, 2019 ; Reilly et al, 2022 ). Han She et al ( She et al, 2023 ) identified BCL2 as one of the necroptosis-related hub genes in sepsis, highlighting its diagnostic and prognostic value. Moreover, necroptosis and pyroptosis (another form of PCD) pathways collaborated to aggravate tissue injury in the process of sepsis ( Chen et al, 2020 ), and BCL2 constrained the induction of these two pathways through interaction with a BCL2-homology-3 like domain ( Shi and Kehrl, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of apoptosis-immune-related gene signature and construction of diagnostic model for sepsis based on single-cell sequencing and bulk transcriptome analysis

Sun,

Hu,

et al. 2024

Front. Genet.

View full text Add to dashboard Cite

IntroductionSepsis leads to multi-organ dysfunction due to disorders of the host response to infections, which makes diagnosis and prognosis challenging. Apoptosis, a classic programmed cell death, contributes to the pathogenesis of various diseases. However, there is much uncertainty about its mechanism in sepsis.MethodsThree sepsis gene expression profiles (GSE65682, GSE13904, and GSE26378) were downloaded from the Gene Expression Omnibus database. Apoptosis-related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes Pathway database. We utilized LASSO regression and SVM-RFE algorithms to identify characteristic genes associated with sepsis. CIBERSORT and single cell sequencing analysis were employed to explore the potential relationship between hub genes and immune cell infiltration. The diagnostic capability of hub genes was validated across multiple external datasets. Subsequently, the animal sepsis model was established to assess the expression levels of hub genes in distinct target organs through RT-qPCR and Immunohistochemistry analysis.ResultsWe identified 11 apoptosis-related genes as characteristic diagnostic markers for sepsis: CASP8, VDAC2, CHMP1A, CHMP5, FASLG, IFNAR1, JAK1, JAK3, STAT4, IRF9, and BCL2. Subsequently, a prognostic model was constructed using LASSO regression with BCL2, FASLG, IRF9 and JAK3 identified as hub genes. Apoptosis-related genes were closely associated with the immune response during the sepsis process. Furthermore, in the validation datasets, aside from IRF9, other hub genes demonstrated similar expression patterns and diagnostic abilities as observed in GSE65682 dataset. In the mouse model, the expression differences of hub genes between sepsis and control group revealed the potential impacts on sepsis-induced organ injury.ConclusionThe current findings indicated the participant of apoptosis in sepsis, and apoptosis-related differentially expressed genes could be used for diagnosis biomarkers. BCL2, FASLG, IRF9 and JAK3 might be key regulatory genes affecting apoptosis in sepsis. Our findings provided a novel aspect for further exploration of the pathological mechanisms in sepsis.

show abstract

“…Previous studies have demonstrated the diagnostic value of STAT3 in sepsis-induced myocardial disease and sepsis-related respiratory distress syndrome ( Zhang et al, 2019 ; Song et al, 2023 ). Metabolism-related genes ( She et al, 2022 ), necrosis-related genes ( She et al, 2023 ), immune-related genes ( Zheng et al, 2022 ) common DEGs related to sepsis and metabolic syndrome ( Tao et al, 2023 ), apoptosis-related genes ( Wang et al, 2022 ), inflammation response signature genes ( Jiang et al, 2022 ), and signature genes for septic shock in children ( Fan et al, 2022 ) have all shown some efficacy in the diagnosis and prognostic assessment of sepsis. In addition, metabolites based on metabolomics analysis ( Li Y. et al, 2023 ), m6A regulatory factors ( Li F. et al, 2023 ), and non-coding RNAs such as miR-147b ( Trung et al, 2021 ), lncRNA THAP9-AS1 , and TSPOAP1-AS1 ( Wu et al, 2020 ) have all been reported as potential diagnostic or prognostic indicators for sepsis or septic shock.…”

Section: Discussionmentioning

confidence: 99%

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Jiang¹,

Ren²,

Yu³

et al. 2023

Front. Genet.

View full text Add to dashboard Cite

Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock.Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT.Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells.Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.

show abstract

Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

Cited by 7 publications

References 37 publications

Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

Exploring the prognostic necroptosis-related genes and underlying mechanism in sepsis using bioinformatics

Identification of apoptosis-immune-related gene signature and construction of diagnostic model for sepsis based on single-cell sequencing and bulk transcriptome analysis

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Contact Info

Product

Resources

About