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Background: Hepatocellular carcinoma (HCC) is globally recognized as one of the most frequently occurring primary malignant liver tumors, making the identification of HCC biomarkers critically important. The protein MITD1 (Microtubule Interacting and Trafficking Domain containing 1) has been shown to interact with ESCRT-III and participates in cytokinesis, the last step in cell division. This is the first investigation into the expression of MITD1 and its prognostic value, potential biological functions and effects on the immune system in HCC patients., Methods: The gene expression and clinicopathology analysis, enrichment analysis and immune infiltration analysis are based on data obtained from The Cancer Genome Atlas (TCGA), with additional bioinformatics analyses performed. The statistical analysis was conducted in R and immune responses of MITD1 expression in HCC were analyzed using TIMER and CIBERSORT. In addition, GEPIA, K-M survival analysis and data from the HPA were used to validate the outcomes., Results: Our results highlighted that MITD1 plays a key role as an independent prognostic factor in patients with HCC. MITD1 expression was associated with age, grade, stage and tumor status. GSEA revealed that MITD1 is closely correlated with cell cycle control via the NOTCH signaling pathway. CIBERSORT analysis revealed that the amount of NK cells decreased when MITD1 expression was high., Conclusions: The identification of MITD1 as a new biomarker for HCC could help elucidate how changes in cytokinesis and the immune environment promote liver cancer development. With further analysis, MITD1 may be able to serve as a predictor for human HCC prognosis., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Lung adenocarcinoma (LUAD) is a crucial pathological type of lung cancer. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LUAD. TTC21A is a gene has not reported in cancer, and the mechanism behind it is still unclear. Our study assesses TTC21A role in LUAD, via TCGA data., Methods: GEPIA was utilized to analyze the expression of TTC21A in LUAD. We evaluated the influence of TTC21A on survival of LUAD patients by survival module. Then, data sets of LUAD were downloaded from TCGA. The correlations between clinical information and TTC21A expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression. In addition, we explored the correlation between TTC21A and cancer immune infiltrates using CIBERSORT and "Correlation" module of GEPIA., Results: The univariate analysis using logistic regression, wherein TTC21A expression served as a categorical dependent variable (with a median expression value of 2.5), indicated that increased TTC21A expression is significantly correlated with pathological stage, tumor status and lymph nodes. Moreover, multivariate analysis revealed that the up-regulated TTC21A expression, negative results of pathological stage and distant metastasis are independent prognostic factors for good prognosis. Specifically, a positive correlation between increased TTC21A expression and immune infiltrating level of B cells, Neutrophils, Mast cells and T cells was established using CIBERSORT analysis. Furthermore, we confirmed it in "correlation" module of GEPIA., Conclusion: Together with all these findings, increased TTC21A expression correlates with favorable prognosis and increased proportion of immune cells, such as B cells, Neutrophils, Mast cells and T cells in LUAD. These conclusions indicate that TTC21A could serve as a potential biomarker to assess prognosis and immune infiltration level in LUAD., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients' prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = -0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG., (© 2019 Wiley Periodicals, Inc.)

Background and Objective: The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC., Methods: We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GC and normal samples were analyzed with limma package. Gene ontology (GO) and KEGG enrichment analysis were performed using DAVID. Then we established the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING). The prognostic analysis of hub genes were performed through Gene Expression Profiling Interactive Analysis (GEPIA). Additionally, we used real-time quantitative PCR to validate the expression of hub genes in 5 pairs of tumor tissues and corresponding adjacent tissues. Finally, the candidate small molecules as potential drugs to treat GC were predicted in CMap database., Results: Through integrating five microarray datasets, a total of 172 overlap DEGs were detected including 79 up-regulated and 93 down-regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in digestion, collagen fibril organization and cell adhesion. Signaling pathway analysis indicated that these DEGs played an vital in ECM-receptor interaction, focal adhesion and metabolism of xenobiotics by cytochrome P450. Protein-protein interaction network among the overlap DEGs was established with 124 nodes and 365 interactions. Three DEGs with high degree of connectivity (NID2, COL4A1 and COL4A2) were selected as hub genes. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with worse survival of patients. Finally, the 20 most significant small molecules were obtained based on CMap database and spiradoline was the most promising small molecule to reverse the GC gene expression., Conclusions: Our results indicated that NID2, COL4A1 and COL4A2 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of GC initiation and progression., (Copyright © 2019 Elsevier GmbH. All rights reserved.)

Background: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC., Methods: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC., Results: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression., Conclusions: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment., (© 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.)