Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer

Proteomic Studies of Primary Acute Myeloid Leukemia Cells Derived from Patients Before and during Disease-Stabilizing Treatment Based on All-Trans Retinoic Acid and Valproic Acid

All-trans retinoic acid (ATRA) and valproic acid (VP) have been tried in the treatment of non-promyelocytic variants of acute myeloid leukemia (AML). Non-randomized studies suggest that the two drugs can stabilize AML and improve normal peripheral blood cell counts. In this context, we used a proteomic/phosphoproteomic strategy to investigate the in vivo effects of ATRA/VP on human AML cells. Before starting the combined treatment, AML responders showed increased levels of several proteins, especially those involved in neutrophil degranulation/differentiation, M phase regulation and the interconversion of nucleotide di- and triphosphates (i.e., DNA synthesis and binding). Several among the differentially regulated phosphorylation sites reflected differences in the regulation of RNA metabolism and apoptotic events at the same time point. These effects were mainly caused by increased cyclin dependent kinase 1 and 2 (CDK1/2), LIM domain kinase 1 and 2 (LIMK1/2), mitogen-activated protein kinase 7 (MAPK7) and protein kinase C delta (PRKCD) activity in responder cells. An extensive effect of in vivo treatment with ATRA/VP was the altered level and phosphorylation of proteins involved in the regulation of transcription/translation/RNA metabolism, especially in non-responders, but the regulation of cell metabolism, immune system and cytoskeletal functions were also affected. Our analysis of serial samples during the first week of treatment suggest that proteomic and phosphoproteomic profiling can be used for the early identification of responders to ATRA/VP-based treatment.

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4-0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44-0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06-2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28-0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell-cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

Establishment of a Genomic-Clinicopathologic Nomogram for Predicting Early Recurrence of Hepatocellular Carcinoma After R0 Resection

BACKGROUND

A high rate of postoperative recurrence, especially early recurrence (ER) occurring within 1 year, seriously impedes patients with hepatocellular carcinoma (HCC) from achieving long-term survival. This study aimed to establish a genomic-clinicopathologic nomogram for precisely predicting ER in HCC patients after R0 resection.

METHODS

Two reliable datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected as the training and validation cohorts, respectively. The prognostic genes related to ER were screened out by univariate Cox regression analysis and differential expression analysis. The gene-based prognostic index was constructed using LASSO and Cox regression analyses, and its independent prognostic value was assessed by Kaplan-Meier and multivariate Cox analyses. Gene set enrichment analysis (GSEA) was performed to explore the biological pathways related to the prognostic index. Finally, the nomogram integrating all the independent prognostic factors was established and comprehensively evaluated by calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis.

RESULTS

Nine dysregulated and prognostic genes related to ER (ZNF131, TATDN2, TXN, DDX55, KPNA2, ZNF30, TIMELESS, SFRP1, and COLEC11) were identified (all P < 0.05). The prognostic index model based on the 9 genes was successfully constructed using the TCGA cohort and showed a certain capability to discriminate the ER group from the non-ER group (P < 0.05) and good independent prognostic value in terms of predicting poor early recurrence-free survival (P < 0.05). Eight biological pathways significantly related to ER were identified by GSEA, such as "cell cycle", "homologous recombination" and "p53 signaling pathway." The genomic-clinicopathologic nomogram integrating the 9-gene-based prognostic index and TNM stage displayed significantly higher predictive accuracy and clinical application value than that of TNM stage model both in the training and validation cohorts (all P < 0.05).

CONCLUSIONS

The novel genomic-clinicopathologic nomogram may be a convenient and powerful tool for accurately predicting ER in HCC patients after R0 resection.

Identification of key genes involved in the biosynthesis of triterpenic acids in the mint family

Triterpenic acids (TAs), a large group of natural compounds with diverse biological activity, are produced by several plant taxa. Betulinic, oleanolic, and ursolic acids are the most medicinally important TAs and are mainly found in plants of the mint family. Metabolic engineering is strongly dependent on identifying the key genes in biosynthetic pathways toward the products of interest. In this study, gene expression tracking was performed by transcriptome mining, co-expression network analysis, and tissue-specific metabolite-expression analysis in order to identify possible key genes involved in TAs biosynthetic pathways. To this end, taxa-specific degenerate primers of six important genes were designed using an effective method based on the MEME algorithm in a phylogenetically related group of sequences and successfully applied in three members of the Lamiaceae (Rosmarinus officinalis, Salvia officinalis, and Thymus persicus). Based on the results of in-depth data analysis, genes encoding squalene epoxidase and oxido squalene cyclases are proposed as targets for boosting triterpene production. The results emphasize the importance of identifying key genes in triterpene biosynthesis, which may facilitate genetic manipulation or overexpression of target genes.

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.