Interactome analysis of gene expression profiles identifies CDC6 as a potential therapeutic target modified by miR-215-5p in hepatocellular carcinoma

MALAT1-miRNAs network regulate thymidylate synthase and affect 5FU-based chemotherapy

Background

The active metabolite of 5-Fluorouracil (5FU), used in the treatment of several types of cancer, acts by inhibiting the thymidylate synthase encoded by the TYMS gene, which catalyzes the rate-limiting step in DNA replication. The major failure of 5FU-based cancer therapy is the development of drug resistance. High levels of TYMS-encoded protein in cancerous tissues are predictive of poor response to 5FU treatment. Expression of TYMS is regulated by various mechanisms, including involving non-coding RNAs, both miRNAs and long non-coding RNAs (lncRNAs).

Aim

To delineate the miRNAs and lncRNAs network regulating the level of TYMS-encoded protein.

Main body

Several miRNAs targeting TYMS mRNA have been identified in colon cancers, the levels of which can be regulated to varying degrees by lncRNAs. Due to their regulation by the MALAT1 lncRNA, these miRNAs can be divided into three groups: (1) miR-197-3p, miR-203a-3p, miR-375-3p which are downregulated by MALAT1 as confirmed experimentally and the levels of these miRNAs are actually reduced in colon and gastric cancers; (2) miR-140-3p, miR-330-3p that could potentially interact with MALAT1, but not yet supported by experimental results; (3) miR-192-5p, miR-215-5p whose seed sequences do not recognize complementary response elements within MALAT1. Considering the putative MALAT1-miRNAs interaction network, attention is drawn to the potential positive feedback loop causing increased expression of MALAT1 in colon cancer and hepatocellular carcinoma, where YAP1 acts as a transcriptional co-factor which, by binding to the TCF4 transcription factor/ β-catenin complex, may increase the activation of the MALAT1 gene whereas the MALAT1 lncRNA can inhibit miR-375-3p which in turn targets YAP1 mRNA.

Conclusion

The network of non-coding RNAs may reduce the sensitivity of cancer cells to 5FU treatment by upregulating the level of thymidylate synthase.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis.

Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively.

Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value.

Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

Melatonin: A regulator of the interplay between FoxO1, miR96, and miR215 signaling to diminish the growth, survival, and metastasis of murine adenocarcinoma

Melatonin (Mel.), also known as the magic hormone, is a nocturnally secreted hormone orchestrates the clearance of free radicals that have been built up and cumulated during day. This study aims to detect the impact of pineal gland removal on the incidence of tumor development and to assess the signaling pathways via which exogenous melatonin counteract cancer growth. This goal has been achieved by novel approach for pineal destruction using dental micromotor which validated by melatonin downregulation in blood plasma. Mice were injected sub-cutenously with Ehrlich cells to develop solid tumor as a murine model of breast cancer. The increase at tumor markers carcino embryonic antigen, TNFα, and nuclear factor kappa-light-chain-enhancer of activated B cells was over countered by exogenous melatonin supplementation (20 mg/kg) daily for 1 month. The anticancer effects of melatonin were significantly mediated by scavenging H₂ O₂ and NO and diminishing of lipid peroxidation marker malondialdehyde. The real-time polymerase chain Rx analyses indicated a significant effect of Melatonin in upregulating the expression of miR215, fork head box protein O1 (foxO1), and downregulation of miR96. Flowcytometric analyses indicated a significant effect of melatonin on induction of cell cycle arrest at G1 phase which was further confirmed by Ki67 downregulation. Immunohistochemical analyses indicated the role of melatonin in upregulating P53-dependent apoptosis and downregulating CD44 signaling for survivin, matrix metallo-protein kinase 2, and vascular endothelial growth factor to inhibit cell survival and metastasis. In conclusion, this study sheds the light on M./P53/miR215/CD44 with an emphasis on M./miR96//foxO1 signaling cascades, as a novel pathway of melatonin signaling in adenocarcinoma to diminish cancer cell growth, survival and metastasis.