Integrated bioinformatic analysis unveils significant genes and pathways in the pathogenesis of supratentorial primitive neuroectodermal tumor

Total pelvic exenteration and a new model of diversion for giant primitive neuroectodermal tumor of prostate: A case report and review of the literature

The present study reports a rare primitive neuroectodermal tumor (PNET) of prostate. A 27-year-old male was admitted to Harbin Medical University Cancer Hospital (Harbin, China) for dysuria and dyschezia. Magnetic resonance imaging (MRI) revealed a large mass that may involve the bladder and rectum next to the prostate. Histopathological analysis of biopsy of prostate indicated mesenchymal origin tumor, and immunohistochemistric staining confirmed diagnosis of PNET of prostate. En bloc total pelvic exenteration and double barrel sigmoidostomy were performed. Double stomas in the skin incision were used for fecal and urinary diversion, respectively. Short-term outcome is satisfactory, while long-term efficacy remains to be poor. Clinical features of PNET of prostate should be paid much more attention and radical surgery and adjuvant chemotherapy should be recommended.

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.

Gene set enrichment analysis and meta-analysis to identify six key genes regulating and controlling the prognosis of esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a common malignancy with high mortality. Because of the lack of clarity in the relevant genes and mechanisms involved, and the current difficulty for oncotherapy in providing therapeutic solutions, there is an urgent need to study this matter. While gene probe studies have been used to select the most virulent genes and pathways, paucity of case controls during gene screening and lack of conclusive results to expound the etiology and pathogenesis of the disease, have reduced study reliability.

Methods

We chose six datasets from independent studies in the Gene Expression Omnibus (GEO) database and used gene set enrichment analysis and meta-analysis to select key genes and pathways.

Results

We found four down-regulated and four up-regulated pathways through gene set enrichment analysis, and 406 differential genes through meta-analysis. Based on The Cancer Genome Atlas (TCGA), 995 differentially expressed genes were screened out. Comparing the 406 gene set with the 995 gene set, we found 19 common genes, of which 6 had a common pathway and were screened out as key genes regulating and controlling the prognosis of ESCC.

Conclusions

Among the 19 genes, we found three genes that affect the chemotherapy of ESCC: BUB1B, BUB1, and TTK. Another three genes NDC1, NUP107, and NUP155 on the RNA transport pathway were also found. Altogether, these six genes are not only crucial in the development of ESCC, but also determine the prognosis of patients. The key genes and pathways identified in the present study will be used for the next stage in our study, which will involve gene elimination and other experimentation methods.