Complete oculomotor nerve palsy - first manifestation of gastric adenocarcinoma: clinical experience and literature review

The diagnosis and management of the alteration of the normal function of the oculomotor nerve (third cranial nerve) varies depending on the characteristics of the paralysis, the age of the patient, and the associated symptoms and signs. Oculomotor nerve palsy may be caused by lesions located anywhere from the oculomotor nucleus to the termination of the third nerve in the extraocular muscles. Although there have been significant advances in neuroimaging to facilitate early diagnosis, the management of a patient presenting with isolated oculomotor palsy is still challenging. This review tackles the case of a 52-year-old patient, with a history of pulmonary tuberculosis (at the age of five), referred to the Department of Ophthalmology, St. Spiridon Emergency Clinical Hospital, Iaşi, Romania. The patient had diplopia accompanied by right eyelid ptosis, symptoms that began suddenly 10 days before hospitalization. The clinical examination showed right eye grade II palpebral ptosis, exotropia with limitation of eyeball movements in adduction, supra-∕infraduction. Biomicroscopic examination of the anterior pole revealed the presence of anisocoria and light-near dissociation on the affected side. Numerous investigations were performed to identify the cause, starting with tumoral markers, which were within normal limits. Magnetic resonance angiography (MRA) was performed, and posterior communicating artery aneurysm was ruled out. The endocrinology examination and hormonal laboratory tests were also within normal parameters. Due to suspicions of generalized tuberculosis raised by the infectious disease doctor or presence of secondary lesions, thoraco-abdomino-pelvic computed tomography (CT) scan with contrast agent was done and its findings required gastroenterological exploration. After various explorations, the certainty diagnosis was set by histopathological examination, which revealed gastric adenocarcinoma.

Five genes involved in circular RNA-associated competitive endogenous RNA network correlates with metastasis in papillary thyroid carcinoma

This study aimed to identify potential circular RNA (circRNA), microRNA (miRNA) and mRNA biomarkers as well as their underlying regulatory mechanisms in papillary thyroid carcinoma (PTC). Three microarray datasets from the Gene Expression Omnibus database as well as expression data and clinical phenotype from The Cancer Genome Atlas (TCGA) were downloaded, followed by differential expression, functional enrichment, protein-protein interaction (PPI), and module analyses. The support vector machine (SVM)-recursive feature elimination (RFE) algorithm was used to screen the key circRNAs. Finally, the mRNA-miRNA-circRNA regulatory network and competitive endogenous RNA (ceRNA) network were constructed. The prognostic value and clinical correlations of key mRNAs were investigated using TCGA dataset, and their expression was validated using the UALCAN database. A total of 1039 mRNAs, 18 miRNAs and 137 circRNAs were differentially expressed in patients with PTC. A total of 37 key circRNAs were obtained using the SVM-RFE algorithm, whereas 46 key mRNAs were obtained from significant modules in the PPI network. A total of 11 circRNA-miRNA pairs and 40 miRNA-mRNA pairs were predicted. Based on these interaction pairs, 46 circRNA-miRNA-mRNA regulatory pairs were integrated, of which 8 regulatory pairs in line with the ceRNA hypothesis were obtained, including two circRNAs (circ_0004053 and circ_0028198), three miRNAs (miR-199a-5p, miR-199b-5p, and miR-7-5p), and five mRNAs, namely APOA2, CCL20, LPAR5, MFGE8, and TIMP1. Survival analysis showed that LPAR5 expression was associated with patient survival. APOA2 expression showed significant differences between metastatic and non-metastatic tumors, whereas CCL20, LPAR5, MFGE8 and TIMP1 showed significant differences between metastatic and non-metastatic lymph nodes. Overall, we identified several potential targets and regulatory mechanisms involved in PTC. APOA2, CCL20, LPAR5, MFGE8, and TIMP1 may be correlated with PTC metastasis.

Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data

Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.

Prognostic score model-based signature genes for predicting the prognosis of metastatic skin cutaneous melanoma

PURPOSE

Cutaneous melanoma (SKCM) is the most invasive malignancy of skin cancer. Metastasis to distant lymph nodes or other system is an indicator of poor prognosis in melanoma patients. The aim of this study was to identify reliable prognostic biomarkers for SKCMs.

METHODS

Four RNA-sequencing datasets associated with SKCMs were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database as well as corresponding clinical information. Differentially expressed genes (DEGs) were screened between primary and metastatic samples by using MetaDE tool. Weighted gene co-expression network analysis (WGCNA) was conducted to screen functional modules. A prognostic score (PS)-based predictive model and nomogram model were constructed to identify signature genes and independent clinicopathologic factors.

RESULTS

Based on MetaDE analysis and WGCNA, a total of 456 overlapped genes were identified as hub genes related to SKCMs progression. Functional enrichment analysis revealed these genes were mainly involved in the hippo signaling pathway, signaling pathways regulating pluripotency of stem cells, pathways in cancer. In addition, eight optimal DEGs (RFPL1S, CTSV, EGLN3, etc.) were identified as signature genes by using PS model. Cox regression analysis revealed that pathologic stage T, N and recurrence were independent prognostic factors. Three clinical factors and PS status were incorporated to construct a nomogram predictive model for estimating the three years and five-year survival probability of individuals.

CONCLUSIONS

The prognosis prediction model of this study may provide a promising method for decision making in clinic and prognosis predicting of SKCM patients.

Bioinformatics analysis identified hub genes in prostate cancer tumorigenesis and metastasis

OBJECTIVE

Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. To discover key molecular changes in PCa development and metastasis, we analyzed microarray data of localized PCa, metastatic PCa and normal prostate tissue samples from clinical specimens.

METHODS

Gene expression profiling datasets of PCa were analyzed online. The DAVID was used to perform GO functional and KEGG pathway enrichment analyses. CytoHubba in Cytoscape software was applied to identify hub genes. Survival data were downloaded from GEPIA. Gene expression data were obtained from ONCOMINE and UALCAN.

RESULTS

We obtained 4 sets of differentially expressed genes (DEGs), DEGs 1: a comparison of the gene expression between 4 normal prostate and 5 localized PCa samples in GSE27616; DEGs 2: a comparison of the gene expression between 6 normal prostate and 7 localized PCa samples in GSE3325; DEGs 3: a comparison of the gene expression between 5 localized PCa and 4 metastatic PCa samples in GSE27616; DEGs 4: a comparison of the gene expression between 7 localized PCa and 6 metastatic PCa samples in GSE3325. A comparison of these 4 sets of genes revealed 51 overlapped genes. GO function analysis revealed enrichment of the 51 DEGs in functions related to the proteinaceous extracellular matrix and centrosome, protein homodimerization activity and chromatin binding were the main functions of these genes, which participated in regulating cell division, mitotic nuclear division, proteinaceous extracellular matrix, cell adhesion and apoptotic process. KEGG pathway analysis indicated that these identified DEGs were mainly enriched in progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle. We defined the 16 genes with the highest degree of connectivity as the hub genes in the 51 overlapped DEGs. Cox regression revealed TOP2A, CCNB2, BUB1, CDK1 and EZH2 were related to Disease-free survival (DFS). The expression levels of the 5 genes were 2.232-, 1.786-, 2.303-, 1.699-, and 1.986-fold higher in PCa than the levels in normal tissues, respectively (P < 0.05). We obtained 20 hub genes from DEGs by the comparison of normal prostate tissue vs. localized cancer tissue. Among them, KIF20A, CDKN3, PBK and CDCA2, were expressed higher in PCa than in normal tissues, and were associated with the DFS of PCa patients. Meanwhile, we obtained 20 hub genes from DEGs by the comparison of localized cancer tissue vs. metastatic cancer tissue. Cox regression revealed PLK1, CCNA2 and CDC20, were associated with both the DFS and overall survival of PCa patients.

CONCLUSIONS

The results suggested that the functions of KIF20A, CDKN3, PBK and CDCA2 may contribute to PCa development and the functions of PLK1, CCNA2 and CDC20 may contribute to PCa metastasis. Meanwhile, the functions of TOP2A, CCNB2, BUB1, CDK1 and EZH2 may contribute to both PCa development and metastasis.