Background: Nucleolar and spindle-associated protein 1 (NUSAP1) was previously reported to be associated with poor prognosis in multiple cancers. In the present study, we comprehensively investigated the clinicopathological features and potential prognostic value of NUSAP1 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

Background: Major depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD. Methods: In this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes. Results: We identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD. Conclusions: Our findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.

Glioblastoma multiform is a lethal primary brain tumor derived from astrocytic, with a poor prognosis in adults. Reticulocalbin-1 (RCN1) is a calcium-binding protein, dysregulation of which contributes to tumorigenesis and progression in various cancers. The present study aimed to identify the impact of RCN1 on the outcomes of patients with Glioblastoma multiforme (GBM). The study applied two public databases to require RNA sequencing data of Glioblastoma multiform samples with clinical data for the construction of a training set and a validation set, respectively. We used bioinformatic analyses to determine that RCN1 could be an independent factor for the overall survival of Glioblastoma multiform patients. In the training set, the study constructed a predictive prognostic model based on the combination of RCN1 with various clinical parameters for overall survival at 0.5-, 1.0-, and 1.5-years, as well as developed a nomogram, which was further validated by validation set. Pathways analyses indicated that RCN1 was involved in KEAS and MYC pathways and apoptosis. In vitro experiments indicated that RCN1 promoted cell invasion of Glioblastoma multiform cells. These results illustrated the prognostic role of RCN1 for overall survival in Glioblastoma multiform patients, indicated the promotion of RCN1 in cell invasion, and suggested the probability of RCN1 as a potential targeted molecule for treatment in Glioblastoma multiform.

Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, theCIBERSORTxalgorithm and ssGSEA were implemented for functional analysis. Patients with high mRNAsi/c_mRNAsi GBM showed better prognosis among the four GBM cohorts. After identifying the confounding variables, c_mRNAsi still maintained its prognostic value. Model evaluation showed that the c_mRNAsi-based model performed well. Patients with high c_mRNAsi exhibited significant immune suppression. Moreover, c_mRNAsi correlated negatively with infiltrating levels of immune-related cells. In addition, ssGSEA revealed that immune-related pathways were generally activated in patients with high c_mRNAsi. We comprehensively evaluated GBM stemness indices based on large cohorts and established a c_mRNAsi-based classifier for prognosis prediction.

Following the publication of the 2021 WHO Classification of Central Nervous System Tumors (CNS5), Following the publication of the 2021 WHO Classification of Central Nervous System Tumors (CNS5), prognostic markers of glioblastoma (GBM) need to be further explored. Radiomics is a non-invasive and reproducible method for the prognostic assessment of multiple solid tumors. This study aimed to explore the prognostic value and biological significance of MRI T1-weighted enhancement (CE-T1) based radiomics in GBM (CNS5). A six-features radiomics prognostic model was created to calculate the radiomics score (RS). High RS (HR = 3.718, 95%CI: 2.222 − 6.220, P < 0.001) was an independent risk factor for overall survival (OS). The correlation between RS and OS was externally verified based on the First Affiliated Hospital of Fujian Medical University cohorts (n = 93; HR = 2.015, 95% CI: 1.079 − 3.762, P = 0.028) and the Second Affiliated Hospital of Zhejiang University School of Medicine cohorts (n = 126; HR = 1.779, 95% CI: 1.023 − 3.091, P = 0.041). Through biological significance exploration, RS was found to be significantly correlated with DNA repair (P = 0.009) and glycolysis (P = 0.001) pathway enrichment scores. RS was associated with γδT cell infiltration and the expression of LAG3. The MRI CE-T1 based radiomics models can predict GBM (CNS5) prognosis noninvasively. RS is relevant to DNA repair, and may guide the screening of radiosensitive populations.