Network analysis based on TCGA reveals hub genes in colon cancer

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein-protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Network analysis identifies DAPK3 as a potential biomarker for lymphatic invasion and colon adenocarcinoma prognosis

Colon adenocarcinoma is a prevalent malignancy with significant mortality. Hence, the identification of molecular biomarkers with prognostic significance is important for improved treatment and patient outcomes. Clinical traits and RNA-Seq of 551 patient samples in the UCSC Toil Recompute Compendium of The Cancer Genome Atlas TARGET and Genotype Tissue Expression project datasets (primary_site = colon) were used for weighted gene co-expression network analysis to reveal the association between gene networks and cancer cell invasion. One module, containing 151 genes, was significantly correlated with lymphatic invasion, a histopathological feature of higher risk colon cancer. DAPK3 (death-associated protein kinase 3) was identified as the pseudohub of the module. Gene ontology identified gene enrichment related to cytoskeletal organization and apoptotic signaling processes, suggesting modular involvement in tumor cell survival, migration, and epithelial-mesenchymal transformation. Although DAPK3 expression was reduced in patients with colon cancer, high expression of DAPK3 was significantly correlated with greater lymphatic invasion and poor overall survival.

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WCGNA reveals a gene module linked to lymphatic invasion in colon adenocarcinoma

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DAPK3 is a pseudohub gene with differential expression in colon cancer

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Gene ontology identified relationships to cytoskeletal organization and apoptosis

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DAPK3 was correlated with lymphatic invasion and poor overall survival

GCNT4 is Associated with Prognosis and Suppress Cell Proliferation in Gastric Cancer

Background

GCNT4 is a member of the glucosaminyl (N-acetyl) transferases family that has been implicated in multiple human malignancies. However, the role of GCNT4 in gastric cancer (GC) is unknown. In this present study, we aimed to explore the role and clinicopathological correlation of GCNT4 in GC.

Materials and Methods

We first evaluated the dysregulation of GCNT4 in The Cancer Genome Atlas (TCGA) and then we performed RT-qPCR and immunohistochemistry to validate the results in a cohort of in-house patients. The clinicopathological correlation and function of GCNT4 in GC were also analysed.

Results

GCNT4 was found to be significantly downregulated in GC. In addition, GCNT4 expression correlated with tumour depth, nervous invasion and pathological tumor-node-metastasis (pTNM) stage. Moreover, lower GCNT4 levels conferred poor overall survival (OS) and disease-free survival (DFS) to GC patients. Multivariate Cox regression analysis revealed that GCNT4 protein expression is an independent prognostic factor for OS in patients with GC. Further functional experimental results revealed that overexpression of GCNT4 appears to halt GC cell proliferation and the cell cycle.

Conclusion

Altogether, these findings indicated that GCNT4 regulates the GC cell cycle and have important implications for the selection of therapeutic targets to prevent tumour proliferation.

MALAT1/miR-15b-5p/MAPK1 mediates endothelial progenitor cells autophagy and affects coronary atherosclerotic heart disease via mTOR signaling pathway

Objective: Present study focused on the influence of lncRNA MALAT1 on coronary atherosclerotic heart disease (CAD) by regulating miR-15b-5p/MAPK1 and mTOR signaling pathway.

Method: Differentially expressed genes and activated pathway were investigated through bioinformatics analysis. QRT-PCR was conducted to verify expression of MALAT1, miR-15b-5p and MAPK1 in CAD blood samples and endothelial progenitor cells (EPCs). In addition, the interactions among MALAT1, miR-15b-5p and MAPK1 were revealed by Luciferase reporter assay. Cell autophagy of EPCs was examined by Cyto-ID Autophagy Detection Kit and transmission electron microscope. MTT assay and flow cytometry were carried out to assess cell viability and apoptosis in different interference conditions. Western blot was performed to testify the expression of pERK1/2 (MAPK1), phosphorylated mTOR, ATG1 and LC3-II. Vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) were detected by qRT-PCR. Finally, the effect of lncRNA MALAT1 on cell autophagy and atherogenesis was tested in vivo.

Results: MALAT1 was overexpressed in CAD blood samples and EPCs. Knockdown of MALAT1 and MAPK1 promoted cell viability, autophagy and further suppressed the development of CAD. AntagoMALAT1 protects mice against atherosclerosis.

Conclusion: LncRNA MALAT1 inhibited EPCs autophagy and increased cell viability while repressed apoptosis of CAD via activating mTOR signaling pathway.

Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer

Colon cancer is the third most common cancer, with a high incidence and mortality. Construction of a specific and sensitive prediction model for prognosis is urgently needed. In this study, profiles of patients with colon cancer with clinical and gene expression data were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). CXC chemokines in patients with colon cancer were investigated by differential expression gene analysis, overall survival analysis, receiver operating characteristic analysis, gene set enrichment analysis (GSEA), and weighted gene coexpression network analysis. CXCL1, CXCL2, CXCL3, and CXCL11 were upregulated in patients with colon cancer and significantly correlated with prognosis. The area under curve (AUC) of the multigene forecast model of CXCL1, CXCL11, CXCL2, and CXCL3 was 0.705 in the GSE41258 dataset and 0.624 in TCGA. The prediction model was constructed using the risk score of the multigene model and three clinicopathological risk factors and exhibited 92.6% and 91.8% accuracy in predicting 3-year and 5-year overall survival of patients with colon cancer, respectively. In addition, by GSEA, expression of CXCL1, CXCL11, CXCL2, and CXCL3 was correlated with several signaling pathways, including NOD-like receptor, oxidative phosphorylation, mTORC1, interferon-gamma response, and IL6/JAK/STAT3 pathways. Patients with colon cancer will benefit from this prediction model for prognosis, and this will pave the way to improve the survival rate and optimize treatment for colon cancer.

Identification of a prognostic 28-gene expression signature for gastric cancer with lymphatic metastasis

Gastric cancer (GC) patients have high mortality due to late-stage diagnosis, which is closely associated with lymph node metastasis. Exploring the molecular mechanisms of lymphatic metastasis may inform the research into early diagnostics of GC. In the present study, we obtained RNA-Seq data from The Cancer Genome Altas and used Limma package to identify differentially expressed genes (DEGs) between lymphatic metastases and non-lymphatic metastases in GC tissues. Then, we used an elastic net-regularized COX proportional hazard model for gene selection from the DEGs and constructed a regression model composed of 28-gene signatures. Furthermore, we assessed the prognostic performance of the 28-gene signature by analyzing the receive operating characteristic curves. In addition, we selected the gene PELI2 amongst 28 genes and assessed the roles of this gene in GC cells. The good prognostic performance of the 28-gene signature was confirmed in the testing set, which was also validated by GSE66229 dataset. In addition, the biological experiments showed that PELI2 could promote the growth and metastasis of GC cells by regulating vascular endothelial growth factor C. Our study indicates that the identified 28-gene signature could be considered as a sensitive predictive tool for lymphatic metastasis in GC.

Gold-containing compound BDG-I inhibits the growth of A549 lung cancer cells through the deregulation of miRNA expression

Gold complex bis(diethyldithiocarbamato-gold(I)) bis(diphenylphosphino) methane (BDG-I) is cytotoxic toward different cancer cell lines. We compared the cytotoxic effect of BDG-I with that of cisplatin in the A549 lung cancer cell line. Additionally, we investigated the molecular mechanism underlying the toxic effect of BDG-I toward the A549 cell line and the identification of cancer-related miRNAs likely to be involved in killing the lung cancer cells. Further, X-ray crystallographic data of the compound were acquired. Using microarray, global miRNA expression profiling in BDG-I-treated A549 cells revealed 64 upregulated and 86 downregulated miRNAs, which targeted 4689 and 2498 genes, respectively. Biological network connectivity of the miRNAs was significantly higher for the upregulated miRNAs than for the downregulated miRNAs. Two of the 10 most upregulated miRNAs (hsa-mir-20a-5p and hsa-mir-15b-5p) were associated with lung cancer. AmiGo2 server and Panther pathway analyses indicated significant enrichment in transcription regulation of miRNA target genes that promote intrinsic kinase-mediated signaling, TGF-β, and GnRH signaling pathways, as well as oxidative stress responses. BDG-I crystal structure X-ray diffraction studies revealed gold–gold intramolecular interaction [Au…Au = 3.1198 (3) Å] for a single independent molecule, reported to be responsible for its activity against cancer. Our present study sheds light on the development of novel gold complex with favorable anti-cancer therapeutic functionality.

Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis

Introduction

Colorectal cancer (CRC) is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM) stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA) and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression.

Materials and methods

We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) to detect the pathological stage-related miRNA and gene modules and construct a miRNA–gene network. The Cancer Genome Atlas (TCGA) colon adenocarcinoma (CAC) RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and characterize the results of WGCNA.

Results

Two gene modules (Gmagenta and Ggreen) and one miRNA module were associated with the pathological stage. Six hub genes (COL1A2, THBS2, BGN, COL1A1, TAGLN and DACT3) were related to prognosis and validated to be associated with the pathological stage. Five hub miRNAs were identified to be related to prognosis (hsa-miR-125b-5p, hsa-miR-145-5p, hsa-let-7c-5p, hsa-miR-218-5p and hsa-miR-125b-2-3p). A total of 18 hub genes and seven hub miRNAs were predominantly expressed in tumor stroma. Proteoglycans in cancer, focal adhesion, extracellular matrix (ECM)–receptor interaction and so on were common pathways of the three modules. Hsa-let-7c-5p was located at the core of miRNA–gene network.

Conclusion

These findings help to advance the understanding of tumor stroma in the progression of CAC and provide prognostic biomarkers as well as therapeutic targets.