Identification MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 as the potential immune-related key genes involved in ischaemic cardiomyopathy by random forest and nomogram

Abstract

The immune molecular mechanisms involved in ischaemic cardiomyopathy (ICM) have not been fully elucidated. The current study aimed to elucidate the immune cell infiltration pattern of the ICM and identify key immunerelated genes that participate in the pathologic process of the ICM. The differentially expressed genes (DEGs) were identified from two datasets (GSE42955 combined with GSE57338) and the top 8 key DEGs related to ICM were screened using random forest and used to construct the nomogram model. Moreover, the "CIBERSORT" software package was used to determine the proportion of infiltrating immune cells in the ICM. A total of 39 DEGs (18 upregulated and 21 downregulated) were identified in the current study. Four upregulated DEGs, including MNS1, FRZB, OGN, and LUM, and four downregulated DEGs, SERP1NA3, RNASE2, FCN3 and SLCO4A1, were identified by the random forest model. The nomogram constructed based on the above 8 key genes suggested a diagnostic value of up to 99% to distinguish the ICM from healthy participants. Meanwhile, most of the key DEGs presented prominent interactions with immune cell infiltrates. The RT-qPCR results suggested that the expression levels of MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 between the ICM and control groups were consistent with the bioinformatic analysis results. These results suggested that immune cell infiltration plays a critical role in the occurrence and progression of ICM. Several key immune-related genes, including the MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 genes, are expected to be reliable serum markers for the diagnosis of ICM and potential molecular targets for ICM immunotherapy.

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

“…GPCR ligand binding [60], extracellular matrix organization [61], cytokine signaling in immune system [62], interferon signaling [63], signaling by GPCR [64], neuronal system [65] and platelet activation, signaling and aggregation [66] plays an important role in the schizophrenia. Studies have revealed that SIX1 [67], VIP (vasoactive intestinal peptide) [68], GATA6 [69], FRZB (frizzled related protein) [70], CD40 [71], WT1 [72], PCDHGA3 [73], TFAP2B [74], HFE (homeostatic iron regulator) [75], NKX2-5 [76], IGFBP7 [77], HLA-F [78], CCL2 [79], COL1A2 [80], RUNX1 [81], TFF3 [82], IRX4 [83], NOS1 [84], DKK2 [85], IL18R1 [86], ADAM12 [87], NPPC (natriuretic peptide C) [88], COL1A1 [89], ABCG2 [90], SIX2 [91], CSRP1 [92], MR1 [93], NINJ2 [94], ACE (angiotensin I converting enzyme) [95], TBX1 [96], CTSC (cathepsin C) [97], DLX6 [98], KCNE1 [99], AZGP1 [100], CYP1B1 [101], PRRX1 [102], CD34 [103], A2M [104], CDKN2A [105], SERPINE1 [106], CD44 [107], FABP4 [108], ITGB3 [109], ALOX5AP [110], DAND5 [111], SFRP4 [112], RUNX2 [113], TACR3 [114], MYD88 [115], CYBA (cytochrome b-245 alpha chain) [116], STAT6 [117], FOXC1 [118], FN1 [119], TLR6 [120], CAV1 [121], RGS4 [122], TPM2 [123], TNFSF4 [124], LOX (lysyl oxidase) [125], SMOC2 [126], SPHK1 [127], FOLH1 [128], CYP2C8 [129], CD163 [130], DIRAS3 [131], OSMR (oncostatin M receptor) [132], POSTN (peri...…”

Identification of Shared Immune Cells and Immune-Related Co-Disease Genes in Chronic Heart Failure and Systemic Lupus Erythematosus Based on Transcriptome Sequencing

“… 48 RNASE2 has been identified as a potential key gene in ischaemic cardiomyopathy, which can progress to chronic HF, and may have relevance to HF. 49 Overexpression of RNASE2 in SLE patients have also been found to influence the processes of SLE by mediating the secretion of cytokines and chemokines from Monocytes. 50 In contrast, although less studied in HF and SLE, RNASE3 has been shown to be differentially expressed in a variety of inflammatory diseases such as asthma and allergic contact dermatitis.…”

“…48 RNASE2 has been identified as a potential key gene in ischaemic cardiomyopathy, which can progress to chronic HF, and may have relevance to HF. 49 Overexpression of RNASE2 in SLE patients have also been…”

Ferroptosis-related genes DUOX1 and HSD17B11 affect tumor microenvironment and predict overall survival of lung adenocarcinoma patients

“…Random Forest package in R can be used to construct classification models and screen FRGs to identify those that contribute most to the classification model. [17,18] Patients in the TCGA-LUAD cohort followed a random assignment principle to be disrupted into either the training or test cohort at a ratio of 7:3 (350:150), and cross-validation was used to retain the minimum number of FRGs to minimize the interpretable variance. We computed multivariate Cox regression analyses using the R package and then used a random forest classifier to compute risk scores (RS) for each FRG (RS = ∑βFRGs × ExpFRGs), where βFRGs represents the estimated contribution coefficient of FRGs in the regression analysis and ExpFRGs represents the expression level of FRGs.…”