Construction and validation of a TP53-associated immune prognostic model for gastric cancer.

Increasing evidence indicates that TP53 mutation impacts the patients' prognosis by regulating the gastric cancer (GC) immunophenotype. An immune prognostic signature (IPS) was constructed based on TP53 status. The effects of the IPS on the immune microenvironment of GC were analyzed. We also constructed a nomogram integrating the IPS and other clinical factors. An IPS was constructed in the TCGA cohort and validated in the meta-GEO cohort. TP53 mutation resulted in the downregulation of the immune response in GC. Concretely, high-risk patients were characterized by increased monocyte, macrophage M0 and T cell follicular helper infiltration; increased stromal score, ESTIMATE score and immune score; higher TIM3 and BTLA expression; and decreased dendritic cell and T cell CD4 memory-activated infiltration and tumor purity. The nomogram also showed good predictive performance. These results suggest that the IPS is an effective prognostic indicator for GC patients, which might provide a theoretical foundation for immunotherapy.

A Novel miRNA-mRNA Axis Involves in Regulating Transcriptional Disorders in Pancreatic Adenocarcinoma.

BACKGROUND: Currently, there is still a lack of understanding about the mechanism and therapeutic targets of pancreatic adenocarcinoma (PAAD). The potential of miRNA-mRNA networks for the identification of regulatory mechanisms involved in PAAD development remains unexplored. METHODS: We compared differentially expressed miRNAs (DEMIs) and differentially expressed genes (DEGs) in PAAD and normal tissues from the Gene Expression Omnibus (GEO) database. Transcription factors (TFs) were obtained from FunRich. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs and DEMIs were implemented using Database for Annotation, Visualization and Integrated Discovery (DAVID). Then, key miRNAs and targeted mRNAs were identified by assessment of their expression and prognosis in UALCAN and Kaplan-Meier plotters. In the last step, the candidate miRNA-mRNA selected was confirmed by real-time quantitative polymerase chain reaction (qRT-PCR). RESULTS: We distinguished 62 significant DEMIs, 1314 upregulated DEGs, and 1110 downregulated DEGs. The top 10 TFs were identified. In total, there were 160 hub genes obtained by intersecting the set of 2224 predicted targets with the set of significant DEGs. And we selected 8 key miRNAs. Furthermore, low expression of miR-455-3p in PAAD tissue was closely connected with poor prognosis, and only 5 target mRNAs were predicted to be increased in PAAD tissue with poor prognosis. Therefore, a novel miRNA-hub gene regulatory network in PAAD was constructed. Finally, in vitro experiments indicated that miR-455-3p expression was decreased in PAAD sample. HOXC4, DLG4, DYNLL1 and FBXO45 were validated by qRT-PCR as highly probable targets of miR-455-3p. CONCLUSION: A novel miRNA-mRNA axis has been discovered that may be involved in the regulation of transcriptional disorders and affected the survival of PAAD patients, which would provide a novel strategy for the treatment of PAAD.

Identification of potential crucial genes associated with the pathogenesis and prognosis of liver hepatocellular carcinoma.

AIMS: Liver hepatocellular carcinoma (LIHC) is the main manifestation of primary liver cancer, with low survival rate and poor prognosis. Medical decision-making process of LIHC is so complex that new biomarkers for diagnosis and prognosis have yet to be explored, this study aimed to identify the genes involved in the pathophysiology of LIHC and biomarkers that can be used to predict the prognosis of LIHC. METHODS: Six Gene Expression Omnibus (GEO) datasets selected from GEO were screened and integrated to find out the differential expression genes (DEGs) obtained from LIHC and normal hepatic tissues. The Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis of DEGs was implemented by DAVID. The Protein-protein interaction network was performed via STRING. In addition, Cox regression model was used to construct a gene prognostic signature. RESULTS: We ascertained 10 hub genes, nine of them (CDK1, CDC20, CCNB1, Thymidylate synthetase, Nuclear division cycle80, NUF2, MAD2L1, CCNA2 and BIRC5) as biomarkers of progression in LIHC patients. We also build a six gene prognosis signature (SOCS2, GAS2L3, NLRP5, TAF3, UTP11 and GAGE2A), which can be implemented to predict over survival effectively. CONCLUSIONS: We revealed promising genes that may participate in the pathophysiology of LIHC, and found available biomarkers for LIHC prognosis prediction, which were significant for researchers to further understand the molecular basis of LIHC and direct the synthesis medicine of LIHC.

Construction and analysis of a competing endogenous RNA network to reveal potential prognostic biomarkers for Oral Floor Squamous Cell Carcinoma.

BACKGROUND: Patients diagnosed with Oral Floor Squamous Cell Carcinoma (OFSCC) face considerable challenges in physiology and psychology. This study explored prognostic signatures to predict prognosis in OFSCC through a detailed transcriptomic analysis. METHOD: We built an interactive competing endogenous RNA (ceRNA) network that included lncRNAs, miRNAs and mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to predict the gene functions and regulatory pathways of mRNAs. Least absolute shrinkage and selection operator algorithm (LASSO) analysis and Cox regression analysis were used to screen prognosis factors. The Kaplan-Meier method was used to analyze the survival rate of prognosis factors. Risk score was used to assess the reliability of the prediction model. RESULTS: A specific ceRNA network consisting of 56 mRNAs, 16 miRNAs and 31 lncRNAs was established. Three key genes (HOXC13, TGFBR3, KLHL40) and 4 clinical factors (age, gender, TNM, and clinical stage) were identified and effectively predicted the for survival time. The expression of a gene signature was validated in two external validation cohorts. The signature (areas under the curve of 3 and 5 years were 0.977 and 0.982, respectively) showed high prognostic accuracy in the complete TCGA cohort. CONCLUSIONS: Our study successfully developed an extensive ceRNA network for OFSCC and further identified a 3-mRNA and 4-clinical-factor signature, which may serve as a biomarker.

Identification of hub genes correlated with the pathogenesis and prognosis of gastric cancer via bioinformatics methods.

BACKGROUND: Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world and 5-year overall survival (OS) rate is less than 10%. So, it is urgent to identified novel diagnostic and prognostic biomarkers. METHODS: Twelve GEO (gene expression omnibus) datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between GC and normal tissues were screened and integrated using limma and RobustRankAggreg (RRA) packages in R software. Protein-protein interaction (PPI) network, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses for DEGs were conducted via STRING and DAVID, respectively. Moreover, Cox regression model was used to construct a gene prognosis signature. RESULTS: Ten genes (COL1A1, CXCL8, COL3A1, SPP1, COL1A2, TIMP1, CXCL1, BGN, MMP3 and SERPINE1) were identified and might be highly related to GC. Further analysis showed high expression of CXCL8, COL3A1, CXCL1, MMP3 and SERPINE1, were significantly associated with late stage of GC. Lastly, we build a seven-gene prognosis signature (CYP19A1, SERPINE1, CGB5, CALCR, ASGR2, CYTL1 and ABCB5), which can give a good prediction of OS. CONCLUSIONS: Our article screened out key genes highly associating with GC's developments and prognosis, and it is useful for researcher to further understand GC's molecular basis and direct the synthesis medicine of GC.

Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a type of malignant tumor with the highest mortality rate among all neoplasms worldwide, and its exact pathogenesis is still poorly understood. Timely diagnosis and treatment are of great importance in order to decrease the mortality rate of PAAD. Therefore, identifying new biomarkers for diagnosis and prognosis is essential to enable early detection of PAAD and to improve the overall survival (OS) rate. In order to screen and integrate differentially expressed genes (DEGs) between PAAD and normal tissues, a total of seven datasets were downloaded from the Gene Expression Omnibus database and the 'limma' and 'robustrankggreg' packages in R software were used. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery website, and the protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A gene prognostic signature was constructed using the Cox regression model. A total of 10 genes (CDK1, CCNB1, CDC20, ASPM, UBE2C, TPX2, TOP2A, NUSAP1, KIF20A and DLGAP5) that may be associated with pancreatic adenocarcinoma were identified. According to the differentially expressed genes in The Cancer Genome Atlas, the present study set up four prognostic signatures (matrix metalloproteinase 12, sodium voltage-gated channel α subunit 11, tetraspanin 1 and SH3 domain and tetratricopeptide repeats-containing 2), which effectively predicted OS. The hub genes that were highly associated with the occurrence, development and prognosis of PAAD were identified, which may be helpful to further understand the molecular basis of pancreatic cancer and guide the synthesis of drugs for PPAD.

Identification of hub genes correlated with the pathogenesis and prognosis in Pancreatic adenocarcinoma on bioinformatics methods.

BACKGROUND: Pancreatic adenocarcinoma (PC), is a type of digestive tract cancer with the highest mortality all over the word, and its exact pathogenesis is not clear. Therefore, it is of great significance to search for genes related to PC and elucidate its molecular mechanism. METHODS: We integrated and analyzed 8 microarray datasets from the Gene Expression Comprehensive Database (GEO) and PC patient information from the Cancer Genome Atlas (TCGA) database to identified differentially expressed genes (DEGs) based on standardized annotation information. The overlapped DEGs both in the GEO and TCGA datasets were identified as key genes. Kaplan-Meier comprehensive expression scoring method was conducted to determine whether the key genes are related to the survival rate of PC. The expression of those key genes was analyzed by GEPIA and UALCAN. Lastly, Cox regression model was used to construct a gene prognosis signature. RESULTS: The TSPAN1 gene was identified that might be highly related to the pathogenesis of PC. Further analysis showed high expression of TSPAN1 was closely related to the stage 2, moderately differentiated (intermediate grade), and poorly differentiated (high grade) of PC. Finally, we build a four-gene prognosis signature (AIM2, B3GNT3, MATK and BEND4), which can be applied to predict overall survival (OS) effectively. CONCLUSIONS: We revealed promising genes that may participate in the pathophysiology of PC, and found available biomarkers for PC prognosis prediction, which were significant for researchers to further understand the molecular basis of PC and direct the synthesis medicine of PC.