Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock. Methods: The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults). Results: The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation. Conclusion: This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.

Purpose Early diagnosis of sepsis-induced acute respiratory distress syndrome (ARDS) is critical for effective treatment. We aimed to identify early stage biomarkers. Materials and Methods Differentially expressed genes were identified in whole blood samples from patients with sepsis or ARDS based on the Gene Expression Omnibus (GEO) datasets GSE32707, GSE54514 and GSE10361. Functional enrichment analysis explored the biological characteristics of differentially expressed genes. Genes with high functional connectivity based on a protein-protein interaction network were marked as hub genes, which were validated using the GEO dataset GSE76293, and a gene set variation analysis index (GSVA) was assigned. Diagnostic and predictive ability of the hub genes were assessed by receiver operating characteristic (ROC) curve analysis. DNA methylation levels of hub genes were quantified using the GEO dataset GSE67530. Results Forty-one differentially expressed genes were shared between sepsis-specific and ARDS-specific datasets. MAP2K2 and IRF7 functional activity was highly connected in sepsis-induced ARDS. Hub genes included RETN, MVP, DEFA4, CTSG, AZU1, FMNL1, RBBP7, POLD4, RIN3, IRF7. ROC curve analysis of the hub gene GSVA index showed good diagnostic ability in sepsis or ARDS. Among genes related to sepsis-induced ARDS, 17 were differentially methylated. Principal component analysis and heatmaps indicated that gene methylation patterns differed significantly between ARDS patients and controls. Conclusion We identified a genetic profile specific to early-stage sepsis-induced ARDS. The abnormal expression of these genes may be caused by hypomethylation, which may serve as a biomarker for early diagnosis of ARDS.

Kinesin family member 2C (KIF2C) is known as an oncogenic gene to regulate tumor progression and metastasis. However, its pan-cancer analysis has not been reported. In this study, we comprehensively analyzed the characteristics of KIF2C in various cancers. We found that KIF2C was highly expressed and corresponded to a poor prognosis in various cancers. We also found a significant correlation between KIF2C and clinicopathological characteristics, particularly in cervical cancer, which is the most common gynecological malignancy and is the second leading cause of cancer-related deaths among women worldwide. KIF2C mutation is strongly associated with the survival rate of cervical cancer, and KIF2C expression was significantly upregulated in cervical cancer tissues and cervical cancer cells. Moreover, KIF2C promoted cervical cancer cells proliferation, invasion, and migration in vitro and as well increased tumor growth in vivo. KIF2C knockdown promotes the activation of the p53 signaling pathway by regulating the expression of related proteins. The rescue assay with KIF2C and p53 double knockdown partially reversed the inhibitory influence of KIF2C silencing on cervical cancer processes. In summary, our study provided a relatively comprehensive description of KIF2C as an oncogenic gene and suggested KIF2C as a therapeutic target for cervical cancer.

Purpose Endometrial cancer (EC) is the sixth most common cancer in women and its incidence and mortality have been rising over the last decades. The latest research indicates that FABP4 plays a significant role in multiple types of cancer. But few studies were focused on EC. The aim of this article is to investigate whether FABP4 can suppress tumor growth and metastasis of EC via PI3K/Akt pathway to provide a novel therapeutic target for the treatment of EC. Materials and Methods FABP4 mRNA levels of EC were analysed through The Cancer Genome Atlas database (TCGA), and expression of FABP4 in EC cancer tissues was determined by immunohistochemistry (IHC) assays. Stable overexpressing cell lines were established using lentivirus infection to analyze the biological function of FABP4 in vitro. CCK8 assay and colony formation assay were performed to assess cell proliferation ability. Wound healing assay and transwell were performed to analyse migration and invasion of cells. The subcutaneous xenograft mouse model was used to evaluate tumor growth in vivo. Additionally, all protein levels were detected by Western blotting assay. Results We found that the expression of the FABP4 mRNA was decreased in tumor samples compared to normal tissue according to TCGA database analysis. Subsequent experimental mRNA and protein expression analysis confirmed that FABP4 expression was lower in EC tissue than normal endometrial tissue. In addition, we found overexpression of FABP4 inhibited the proliferation, migration and invasion in vitro and suppressed tumor growth in vivo. Further functional and mechanistic analysis of FABP4 demonstrated that its function is mediated by restraining the phosphorylation of PI3K/Akt signaling pathway. Conclusion Our studies shed light for the first time about the functional role of FABP4 in EC and provide a novel biomarker for EC as well as a therapeutic target for the therapy of EC.

Purpose Acute respiratory distress syndrome (ARDS) is a rapidly progressive diffuse lung injury that is characterized by high mortality and acute onset. The pathological mechanisms of ARDS are still unclear. But alveolar macrophages have been shown to play an important role in inflammatory responses during ARDS. We aimed to find the biomarkers for ARDS for early diagnosis, to give ARDS patients timely treatment. Methods Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) and screened for differentially expressed genes (DEGs). The common upregulated genes in all the datasets were defined as circulating ARDS alveolar macrophage-related genes (cARDSAMGs). We performed a functional enrichment analysis to explore potential biological functions of cARDSAMGs, and we built protein–protein interaction networks. Gene set variation analysis (GSVA) was used to calculate the core gene set variation analysis (CGSVA) score for individual samples. Receiver operating characteristic (ROC) curve analysis was applied on the CGSVA score to evaluate its ability for diagnosis of ARDS. Results A total of 60 genes were upregulated in all ARDS datasets and were therefore denominated as cARDSAMGs. The cARDSAMGs were significantly involved in multiple inflammation-, immunity- and phagocytosis-related biological processes and pathways. In the protein–protein interaction network associated with host responses to ADRS, eight genes were identified as a core gene set: PTCRA, JAG1, C1QB, ADAM17, C1QA, MMP9, VSIG4 and TNFAIP3. ROC curve analysis showed that the CGSVA score may be considered as a biomarker for ARDS: it was significantly higher in patients with ARDS than those in healthy in both alveolar lavage fluid and whole blood. Conclusion The ARDS alveolar macrophage-related CGSVA score may be useful as a biomarker for ARDS.