ANOS1 accelerates the progression of esophageal cancer identified by multi-omic approaches.

To assess the role of ANOS1 in esophageal cancer (ESCA) progression, multi-omic analysis and experimental validation were employed. It was revealed that ANOS1 expression is significantly enhanced in ESCA patients and cell lines. The expression level of ANOS1 in ESCA patients can distinguish the malignancy from normal tissue with an area under curve (AUC) >0.75. Moreover, increased expression of ANOS1 is associated with advanced T stage and worse disease-free survival of ESCA patients. Therefore, a clinically applicable nomogram with ANOS1 was established with strong predictive power. Furthermore, high expression of ANOS1 in ESCA is correlated with (i) the enrichment of epithelial-mesenchymal transition by gene set enrichment analysis, (ii) the involvement in hypoxia, angiogenesis, WNT signaling pathway, and TGFß signaling pathway by gene set variation analysis, (iii) the presence of the small insertion and deletion mutational signature ID9, associated with chromothripsis, in the single-nucleotide polymorphism analysis, (iv) the amplification of 11q13.3 in the copy number variants analysis, (v) the enrichment of cancer-associated fibroblasts and mesenchymal stromal cells in the tumor microenvironment. All the results from multi-omic analysis indicate that ANOS1 plays a pivotal role in accelerating the progression of ESCA. Results from in vivo and in vitro experiments show that the knockdown of ANOS1 hampers the proliferation of ESCA cells, further validating the oncogenic role of ANOS1 in ESCA. Additionally, potential chemotherapeutics with sensitivity were identified in the high-ANOS1 group. In conclusion, ANOS1 accelerates the progression of ESCA.

Smoking patients with laryngeal cancer screened with a novel immunogenomics-based prognostic signature.

The immune system greatly affects the prognosis of various malignancies. Studies on differentially expressed immune-related genes (IRGs) in the immune microenvironment of laryngeal squamous cell carcinoma (LSCC) have rarely been reported. In this paper, the prognostic potentials of IRGs were explored in LSCC patients with smoking use. The RNA-seq data containing IRGs and corresponding clinical information of smoking LSCC patients was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed IRGs were identified and functional enrichment analysis was used to reveal the pathway of IRGs. Then, IRGs with prognostic potentials in smoking LSCC patients were screened out by univariate Cox regression analysis. Finally, multivariate Cox regression analysis was conducted to assess the prognostic signature of 5 IRGs after adjustment of clinical factors and patients were classified into two subgroups based on different IRGs expression. The prognostic capacity of the model was verified by another independent cohort from Gene Expression Omnibus (GEO) database. Nomogram including the prognostic signature was established and shown some clinical net benefit. These findings may contribute to the development of potential therapeutic targets and biomarkers for the new-immunotherapy of LSCC patients with smoking use.

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.

BACKGROUND: The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. METHODS: The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. RESULTS: By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. CONCLUSION: The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

Identification of Potential Biomarkers and Survival Analysis for Head and Neck Squamous Cell Carcinoma Using Bioinformatics Strategy: A Study Based on TCGA and GEO Datasets.

The mechanism and gene markers of head and neck squamous cell carcinoma (HNSCC), a common malignant tumor, have not yet been identified. The aim of this study was to identify the key genes and pathways associated with HNSCC and to further analyze its molecular mechanism and prognostic significance. In this study, the expression profile chip data GSE6631 from Gene Expression Omnibus (GEO) included paired HNSCC tumor and normal samples from 22 patients; the RNAseq tertiary dataset of HNSCC and corresponding clinical information from The Cancer Genome Atlas (TCGA) included biological information of 12 normal head and neck tissues and 111 HNSCC sample tissues. Differentially expressed genes (DEGs) were screened by R software, and the pathway enrichment analysis of DEGs was performed by DAVID, String, and Sytoscape software programs. Combining the GEO and the TCGA databases, we used bioinformatics technology to screen out 50 DEGs in HNSCC and enrich the biological functions and key pathways of HNSCC. Then we performed Gene Ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, protein-protein interaction (PPI) analysis, and survival analysis on these DEGs. Using CMap, we identified candidate small molecules that might reverse HNSCC gene expression. Finally, four most important small molecules that could provide more reliable biomarkers for early diagnosis and individualized control of HNSCC were identified.