Identification of solute-carrier family 27A molecules (SCL27As) as a potential biomarker of ovarian cancer based on bioinformatics and experiments

Establishment of a prognostic model for ovarian cancer based on mitochondrial metabolism-related genes

Background

Mitochondrial metabolism and mitochondrial structure were found to be altered in high-grade serous ovarian cancer (HGSOC). The intent of this exploration was to systematically depict the relevance between mitochondrial metabolism-related genes (MMRGs) and the prognosis of HGSOC patients by bioinformatics analysis and establish a prognostic model for HGSOC.

Methods

First of all, screened differentially expressed genes (DEGs) between TCGA-HGSOC and GTEx-normal by limma, with RNA-seq related HGSOC sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, expressed MMRGs (DE-MMRGs) were acquired by overlapping DEGs with MMRGs, and an enrichment analysis of DE-MMRGs was performed. Kaplan-Meier (K-M) survival analysis and Cox regression analysis were conducted to validate the genes' prognostic value, Gene Set Enrichment Analysis (GSEA) to elucidate the molecular mechanisms of the risk score, and CIBERSORT algorithm to explore the immuno landscape of HGSOC patients. Finally, a drug sensitivity analysis was made via the Drug Sensitivity in Cancer (GDSC) database.

Results

436 HGSOC-related DE-MMRGs (222 up-regulated and 214 down-regulated) were observed to participate in multiple metabolic pathways. The study structured a MMRGs-related prognostic signature on the basis of IDO1, TNFAIP8L3, GPAT4, SLC27A1, ACSM3, ECI2, PPT2, and PMVK. Risk score was the independent prognostic element for HGSOC. Highly dangerous population was characterized by significant association with mitochondria-related biological processes, lower immune cell abundance, lower expression of immune checkpoint and antigenic molecules. Besides, 54 drugs associated with eight prognostic genes were obtained. Furthermore, copy number variation was bound up with the 8 prognostic genes in expression levels.

Conclusion

We have preliminarily determined the prognostic value of MMRGs in HGSOC as well as relationship between MMRGs and the tumor immune microenvironment.

Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer

Tumor microenvironment (TME) plays important roles in prognosis and immune evasion. However, the relationship between TME-related genes and clinical prognosis, immune cell infiltration, and immunotherapy response in breast cancer (BRCA) remains unclear. This study described the TME pattern to construct a TME-related prognosis signature, including risk factors PXDNL, LINC02038 and protective factors SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108, as an independent prognostic factor for BRCA. We found that the prognosis signature was negatively correlated with the survival time of BRCA patients, infiltration of immune cells and the expression of immune checkpoints, while positively correlated with tumor mutation burden and adverse treatment effects of immunotherapy. Upregulation of PXDNL and LINC02038 and downregulation of SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108 in high-risk score group synergistically contribute to immunosuppressive microenvironment which characterized by immunosuppressive neutrophils, impaired cytotoxic T lymphocytes migration and natural killer cell cytotoxicity. In summary, we identified a TME-related prognostic signature in BRCA, which was connected with immune cell infiltration, immune checkpoints, immunotherapy response and could be developed for immunotherapy targets.

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients' quality of life.