The pathway enrichment analysis of up and down-regulated genes between infected and healthy controls.

The circles size and color mean the gene ratio and the adjusted p-value, respectively. The top pathways are shown.</p

Gene expression comparison between infected and healthy controls.

Volcano plot displaying combined effect size (x-axis) and negative log10 of the false discovery rate value (y-axis). The significant up and down-regulated genes are plotted as red dots.</p

Venn diagram for overlap visualization between meta-analysis results and feature genes identified from the feature selection models.

Venn diagram for overlap visualization between meta-analysis results and feature genes identified from the feature selection models.</p

The xlsx file, including the A to M Tables.

BackgroundLeishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease.MethodsThis study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis.ResultsAs a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways.ConclusionsThese findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.</div

Schematic overview of the strategy for understanding aspects of response of immune system cells to <i>Leishmania</i> infection.

Schematic overview of the strategy for understanding aspects of response of immune system cells to Leishmania infection.</p

The top significant Gene Ontology (GO) categories of the differentially expressed genes (DEGs) in three ontologies: BP, biological process; MF, molecular function; and CC, cellular component.

The top significant Gene Ontology (GO) categories of the differentially expressed genes (DEGs) in three ontologies: BP, biological process; MF, molecular function; and CC, cellular component.</p

Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs).

Hierarchical cluster tree representing seven modules of co-expressed genes. The gene dendrogram was constructed by clustering dissimilarity using consensus Topological Overlap. The color row indicates the corresponding module colors. Each colored row represents a module color-coded to highlight a group of genes with strong interconnections.</p

The number of differentially expressed genes (DEGs) in different transcription factor families.

The number of up- or down-regulated are shown for each transcription factor family.</p

Twenty-five top mirRNAs that target differentially expressed genes (DEGs).

Twenty-five top mirRNAs that target differentially expressed genes (DEGs).</p

S7 Table -

PurposePancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.MethodsSeveral transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.ResultsAfter applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.ConclusionWe identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.</div