SLC34A2 Up-regulation And SLC4A4 Down-regulation Correlates With Invasion, Metastasis, And The MAPK Signaling Pathway In Papillary Thyroid Carcinomas

Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest

Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF).

Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by “limma” of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory.

Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.

Characterization of SLC34A2 as a Potential Prognostic Marker of Oncological Diseases

The main goal of this study is to consider SLC34A2 as a potential prognostic marker of oncological diseases using the mutational, expression, and survival data of cancer studies which are publicly available online. We collected data from four databases (cBioPortal, The Cancer Genome Atlas; cBioPortal, Genie; International Cancer Genome Consortium; ArrayExpress). In total, 111,283 samples were categorized according to 27 tumor locations. Ninety-nine functionally significant missense mutations and twelve functionally significant indel mutations in SLC34A2 were found. The most frequent mutations were SLC34A2-ROS1, p.T154A, p.P506S/R/L, p.G257A/E/R, p.S318W, p.A396T, p.P410L/S/H, p.S461C, p.A473T/V, and p.Y503H/C/F. The upregulation of SLC34A2 was found in samples of myeloid, bowel, ovarian, and uterine tumors; downregulation was found in tumor samples of breast, liver, lung, and skin cancer tumors. It was found that the life expectancy of breast and thymus cancer patients with an SLC34A2 mutation is lower, and it was revealed that SLC34A2 overexpression reduced the life span of patients with brain, ovarian, and pancreatic tumors. Thereby, for these types of oncological diseases, the mutational profile of SLC34A2 can be a potential prognostic marker for breast and thymus cancers, and the upregulation of SLC34A2 can be a potential prognostic marker for brain, ovarian, and pancreatic cancers.

Solute carrier family 16 member 5 downregulation and its methylation might serve as a prognostic indicator of prostate cancer

Prostate cancer (PCa), characterized by high invasion, metastasis, and recurrence, is the most prevalent malignant tumor in men worldwide. A clear understanding of the underlying molecular mechanisms and their role during PCa tumorigenesis can help develop prognostic and targeted therapies. We analyzed datasets from public databases, including the Cancer Genome Atlas (TCGA) and Oncomine and Gene Expression Profiling Interactive Analysis for differential expression of solute carrier family 16 member 5 (SLC16A5). We further investigated its relationship with clinical stage, pathological grade, and prognosis of PCa. The promoter methylation level of SLC16A5 in PCa was also investigated by UALCAN. We also utilized datasets from UCSC Xena to explore the prognostic role of SLC16A5 methylation levels and CpG site. Correlations between SLC16A5 and immune infiltration were discovered through TIMER. We observed significantly lower levels of SLC16A5 mRNA in PCa relative to normal tissues across six datasets from Oncomine database (p < .001) and 498 cases from TCGA database (p < .0001). SLC16A5 is strongly negatively regulated by its DNA methylation, with a Spearman of -0.81 and Pearson of -0.80 (p < .001). The aberrant SLC16A5 expression resulted in a significant relationship with clinical stage, pathological grade, and lower SLC16A5 mRNA expression, and its hypermethylation was related to a poorer PCa prognosis. SLC16A5 acted as an important factor for PCa diagnosis, with an AUC of 0.9038 (95% CI: 0.8597-0.9479; p < .0001). Besides, the aberrant SLC16A5 expression revealed close correlations with multiple immune cells. Overall, these results indicate that decreased SLC16A5 expression might be a potential biomarker for determining prognosis and immune infiltration in PCa. The positive SLC16A5 modulation might be a promising therapeutic target for PCa.