Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis.

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation.

As lung cancer remains the leading cause of cancer deaths globally, characterizing the tumor molecular profiles is crucial to tailoring treatments for individuals at advanced stages. Cancer cells exhibit strong dependence on iron for their proliferation, and several iron-regulatory proteins have been proposed as either oncogenes or tumor suppressive genes. This study aims to evaluate the prospective therapeutic and prognostic values of the sideroflexin (SFXN) gene family, whose functions involve mitochondrial iron metabolism, in lung adenocarcinoma (LUAD). Differential expression analysis using TIMER and UALCAN tools was first employed to compare SFXNs expression levels between normal and LUAD tissues. Next, SFXNs' prognostic values, biological significance, and potential as immunotherapy candidates were examined from GEPIA, cBioPortal, MetaCore, Cytoscape, and TIMER databases. It was found that all members of SFXN family, except SFXN3, were differentially expressed in LUAD compared to normal samples and within different stages of LUAD. Survival analysis then revealed SFXN1 to be related to worse overall survival outcome in patients with LUAD. Furthermore, several correlations between expression of SFXN1 and immune infiltration cells were discovered. To conclude, our study provides evidence of SFXN family gene's relevance to the prognosis and immunotherapeutic targets of LUAD.

Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis.

G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer.