No abstract

No abstract

Background Metabolic abnormalities have recently been widely studied in various cancer types. This study aims to explore the expression profiles of metabolism-related genes (MRGs) in endometrial cancer (EC). Methods We analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated with EC patient prognosis. Functional pathway enrichment analysis of the DE-MRGs was performed. LASSO and Cox regression analyses were performed to select MRGs closely related to EC patient outcomes. A prognostic signature was developed, and the efficacy was validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients’ survival probability. Results Forty-seven DE-MRGs were significantly correlated with EC patient prognosis. Functional enrichment analysis showed that these MRGs were highly enriched in amino acid, glycolysis, and glycerophospholipid metabolism. Nine MRGs were found to be closely related to EC patient outcomes: CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2 and ACACB. Based on these nine DE-MRGs, we developed a prognostic signature, and its efficacy in part of and the entire TCGA EC cohort was validated. The nine-MRG signature was independent of other clinical features, and could effectively distinguish high- and low-risk EC patients and predict patient OS. The nomogram showed excellent consistency between the predictions and actual survival observations. Conclusions The MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice.

No abstract

Background Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. However, the molecular mechanisms and the prognostic prediction for EC patients remain unclear. Methods In the current study, we performed an in-depth analysis of over 500 patients which were obtained from the Cancer Genome Atlas (TCGA) database. The bioinformatics analysis included gene set enrichment analysis (GSEA) and Cox and lasso regression analyses to ensure overall survival (OS)-related genes, moreover, to construct a prognostic model and a nomogram for EC patients. Results GSEA identified 4 gene sets significantly associated with EC, which are DNA repair, unfolded protein response, reactive oxygen species pathway and UV response up. Twenty-five OS-related DNA repair genes were screened out, after that, a 9-mRNA signature was constructed to measure the risk scores of patients with different outcomes. In addition, a nomogram contained the 9-mRNA model and clinical parameters was also presented to assess the prognosis. Patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib. Conclusions The identified highly enriched gene sets may offer a novel insight into the tumorigenesis and treatment of EC. Additionally, the constructed 9-mRNA model and the nomogram have prominent clinical implications for prognosis evaluation and specific therapy guidance for EC patients.

Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P < 0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression while SNX10 presented high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency (AUC = 0.738). GSEA analysis demonstrated that the two genes were correlated with the chemokine signaling pathway (P < 0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p = 0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients (P = 0.007 and 0.003). Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

Aims: A growing number of studies have unveiled that long non-coding RNA (lncRNA) is conductive to cervical cancer (CC) development. However, the effect of LIPE-AS1 is remained to be studied in CC.Main Methods: Reverse transcription-polymerase chain reaction (RT-PCR) was employed to measure LIPE-AS1 expression in CC tissues and the adjacent normal tissues. Additionally, we conducted gain- and loss-of functional experiments of LIPE-AS1 and adopted CCK8 assay, BrdU assay, and in vivo tumor formation experiment to test the proliferation of CC cells (HCC94 and HeLa). Besides, the apoptosis, invasion, and epithelial-mesenchymal transformation (EMT) of CC cells were estimated using flow cytometry, transwell assay, and western blot, respectively. Further, LIPE-AS1 downstream targets were analyzed through bioinformatics, and the binding relationships between LIPE-AS1 and miR-195-5p were verified via dual-luciferase activity experiment and RNA Protein Immunoprecipitation (RIP) assay. Moreover, rescue experiments were conducted to confirm the effects of LIPE-AS1 and miR-195-5p in regulating CC development and the expressions of MAPK signaling pathway related proteins were detected by RT-PCR, western blot, and immunofluorescence.Key Findings: LIPE-AS1 was over-expressed in CC tissues (compared to normal adjacent tissues) and was notably related to tumor volume, distant metastasis. Overexpressing LIPE-AS1 accelerated CC cell proliferation, migration and EMT, inhibited apoptosis; while LIPE-AS1 knockdown had the opposite effects. The mechanism studies confirmed that LIPE-AS1 sponges miR-195-5p as a competitive endogenous RNA (ceRNA), which targets the 3′-untranslated region (3′-UTR) of MAP3K8. LIPE-AS1 promoted the expression of MAP3K8 and enhanced ERK1/2 phosphorylation, which were reversed by miR-195-5p.Significance: LIPE-AS1 regulates CC progression through the miR-195-5p/MAPK signaling pathway, providing new hope for CC diagnosis and treatment.

No abstract