Gene expression profiles analysis identifies key genes for acute lung injury in patients with sepsis

Prognostic targets recognition of rectal adenocarcinoma based on transcriptomics

Colorectal cancer is currently the third most common cancer around the world. In this study, we chose a bioinformatics analysis method based on network analysis to dig out the pathological mechanism and key prognostic targets of rectal adenocarcinoma (READ).In this study, we downloaded the clinical information data and transcriptome data from the Cancer Genome Atlas database. Differentially expressed genes analysis was used to identify the differential expressed genes in READ. Community discovery algorithm analysis and Correlation analysis between gene modules and clinical data were performed to mine the key modules related to tumor proliferation, metastasis, and invasion. Genetic significance (GS) analysis and PageRank algorithm analysis were applied for find key genes in the key module. Finally, the importance of these genes was confirmed by survival analysis.Transcriptome datasets of 165 cancer tissue samples and 9 paracancerous tissue samples were selected. Gene coexpression networks were constructed, multilevel algorithm was used to divide the gene coexpression network into 11 modules. From GO enrichment analysis, module 11 significantly associated with clinical characteristic N, T, and event, mainly involved in 2 types of biological processes which were highly related to tumor metastasis, invasion, and tumor microenvironment regulation: cell development and differentiation; the development of vascular and nervous systems. Based on the results of survival analysis, 7 key genes were found negatively correlated to the survival rate of READ, such as MMP14, SDC2, LAMC1, ELN, ACTA2, ZNF532, and CYBRD1.Our study found that these key genes were predicted playing an important role in tumor invasion and metastasis, and being associated with the prognosis of READ. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

Identification of Key Metabolites for Acute Lung Injury in Patients with Sepsis

BACKGROUND

The study aimed to detect critical metabolites in acute lung injury (ALI).

METHODS

A comparative analysis of microarray profile of patients with sepsis-induced ALI compared with sepsis patients with was conducted using bioinformatic tools through constructing multi-omics network. Multi-omics composite networks (gene network, metabolite network, phenotype network, gene-metabolite association network, phenotype-gene association network, and phenotype-metabolite association network) were constructed, following by integration of these composite networks to establish a heterogeneous network. Next, seed genes, and ALI phenotype were mapped into the heterogeneous network to further obtain a weighted composite network. Random walk with restart (RWR) was used for the weighted composite network to extract and prioritize the metabolites. On the basis of the distance proximity among metabolites, the top 50 metabolites with the highest proximity were identified, and the top 100 co-expressed genes interacted with the top 50 metabolites were also screened out.

RESULTS

Totally, there were 9363 nodes and 10,226,148 edges in the integrated composite network. There were 4 metabolites with the scores > 0.009, including CHITIN, Tretinoin, sodium ion, and Celebrex. Adenosine 5'-diphosphate, triphosadenine, and tretinoin had higher degrees in the composite network and the co-expressed network.

CONCLUSION

Adenosine 5'-diphosphate, triphosadenine, and tretinoin may be potential biomarkers for diagnosis and treatment of ALI.

Potential mechanism and drug candidates for sepsis-induced acute lung injury

The present study aimed to explore the mechanisms underlying sepsis-induced acute lung injury (ALI) and identify more effective therapeutic strategies to treat it. The gene expression data set GSE10474 was downloaded and assessed to identify differentially expressed genes (DEGs). Principal component analysis, functional enrichment analysis and differential co-expression analysis of DEGs were performed. Furthermore, potential target drugs for key DEGs were assessed. A total of 209 DEGs, including 107 upregulated and 102 downregulated genes were screened. A number of DEGs, including zinc finger and BTB domain containing 17 (ZBTB17), heat shock protein 90 kDa β, member 1 (HSP90B1) and major histocompatibility complex, class II, DR α were identified. Furthermore, gene ontology terms including antigen processing and presentation, glycerophospholipid metabolism, transcriptional misregulation in cancer, thyroid hormone synthesis and pathways associated with diseases, such as asthma were identified. In addition, a differential co-expression network containing ubiquitin-conjugating enzyme E2 D4, putative and tubulin, γ complex associated protein 3 was constructed. Furthermore, a number of gene-drug interactions, including between HSP90B1 and adenosine-5′-diphosphate and radicicol, were identified. Therefore, DEGs, including ZBTB17 and HSP90B1, may be important in the pathogenesis of sepsis-induced ALI. Furthermore, drugs including adenosine-5′-diphosphate may be novel drug candidates to treat patients with ALI.

Lipid metabolites as potential diagnostic and prognostic biomarkers for acute community acquired pneumonia

Early diagnosis of acute community-acquired pneumonia (CAP) is important in patient triage and treatment decisions. To identify biomarkers that distinguish patients with CAP from non-CAP controls, we conducted an untargeted global metabolome analysis for plasma samples from 142 patients with CAP (CAP cases) and 97 without CAP (non-CAP controls). Thirteen lipid metabolites could discriminate between CAP cases and non-CAP controls with area-under-the-receiver-operating-characteristic curve of >0.8 (P ≤ 10⁻⁹). The levels of glycosphingolipids, sphingomyelins, lysophosphatidylcholines and L-palmitoylcarnitine were higher, while the levels of lysophosphatidylethanolamines were lower in the CAP cases than those in non-CAP controls. All 13 metabolites could distinguish CAP cases from the non-infection, extrapulmonary infection and non-CAP respiratory tract infection subgroups. The levels of trihexosylceramide (d18:1/16:0) were higher, while the levels of lysophosphatidylethanolamines were lower, in the fatal than those of non-fatal CAP cases. Our findings suggest that lipid metabolites are potential diagnostic and prognostic biomarkers for CAP.

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Thirteen lipid metabolites could discriminate CAP cases from non-CAP controls.

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The levels of 2 lipid metabolites differ between fatal and non-fatal CAP cases.

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Lipid metabolites are potential diagnostic and prognostic biomarkers for CAP.

Bioinformatics analysis of transcription profiling of sepsis

Sepsis is a fatal whole-body inflammatory response that complicates a serious infection. To elucidate the molecular mechanism of sepsis, transcription profile data of GSE12624 which included a total of 70 samples (34 sepsis samples and 36 non-sepsis samples) was downloaded. The t test based on Bayes method in limma package was used to identify differentially expressed genes (DEGs) between sepsis and non-sepsis samples (criterion: P value <0.05). Gene Ontology (GO) enrichment analysis was conducted to investigate the biological processes involved DEGs. Protein-protein interaction (PPI) network and sub-network analysis were conducted to investigate the interactions between DEGs. A total of 894 DEGs, including 479 downregulated DEGs and 415 upregulated DEGs, were identified in sepsis samples comparing with non-sepsis samples. GO enrichment analysis showed that DEGs mainly involved in cellular metabolic process, primary metabolic process, and response to organic cyclic compound. In the PPI network, four genes of CDC2, GTF2F2, PCNA, and SMAD4 with degrees more than 10 were identified. Subsequently, four sub-networks, in which genes of PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45 had relative high degrees were identified from the PPI network. After the discussion referring to previous studies, we suggested that CDC2, GTF2F2, PCNA, SMAD4 PSMA3, PTBP1, and GADD45 might be used as new therapeutic targets for sepsis. However, experiments should be further performed to prove the practical utility of these candidates.