Identification and clinical validation of NUSAP1 as a novel prognostic biomarker in ovarian cancer

KIF2A correlates with lymphovascular invasion and higher tumor stage, and can be used to predict worse prognosis in patients with endometrial carcinoma

Kinesin family protein 2A (KIF2A) is a microtubule depolymerase that participates in the progression of various cancers; however, its clinical utility in endometrial carcinoma (EC) remains unclear. The aim of the present study was to assess KIF2A expression and its relationship with prognosis in patients with EC. Data from 230 patients with EC who underwent tumor resection were reviewed in the current, retrospective study. KIF2A expression was measured in 230 formalin-fixed paraffin-embedded (FFPE) specimens of tumor tissue and 50 FFPE specimens of non-tumor tissue using immunohistochemistry (IHC). KIF2A expression was elevated in EC tumor tissue vs. non-tumor tissue (P<0.001). Furthermore, tumor KIF2A expression was linked with lymphovascular invasion (P=0.004) and higher International Federation of Gynecology and Obstetrics (FIGO) stage (P=0.001). High tumor KIF2A expression (IHC score>3) was correlated with shorter disease-free survival (DFS; P=0.014) and overall survival (OS; P=0.012). Moreover, the time-dependent receiver operating characteristic curves revealed that tumor KIF2A expression had an acceptable use for estimating the relapse and death risks at each timepoint within 6 years, with each area under the curve remaining stable at ≥0.7. Notably, tumor KIF2A expression (high vs. low) independently forecast shorter DFS (hazard ratio, 2.506; P=0.013), but not OS (P>0.05). Furthermore, information from The Human Protein Atlas database indicated that high tumor KIF2A expression was associated with worse OS in patients with EC (P=0.027). Tumor KIF2A is not only associated with lymphovascular invasion and higher FIGO stage, but also reflects unfavorable survival in patients with EC.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

NUSAP1 regulates mouse oocyte meiotic maturation

The correct assembly of the spindle apparatus directly regulates the precise separation of chromosomes in mouse oocytes, which is crucial for obtaining high-quality oocytes capable of successful fertilization. The localization, assembly, migration, and disassembly of the spindle are regulated by a series of spindle-associated proteins, which exhibit unique expression level variations and specific localization in oocytes. Proteomic analysis revealed that among many representative spindle-associated proteins, the expression level of nucleolar and spindle-associated protein 1 (NUSAP1) significantly increased after meiotic resumption, with a magnitude of change higher than that of other proteins. However, the role of NUSAP1 during oocyte meiosis maturation has not been reported. Here, we report that NUSAP1 is distributed within the cell nucleus during the germinal vesicle (GV) oocytes with non-surrounded nucleolus stage and is not enriched in the nucleus during the GV-surrounded nucleolus stage. Interestingly, NUSAP1 forms distinct granular aggregates near the spindle poles during the prophase of the first meiotic division (Pro-MI), metaphase I, and anaphase I/telophase I stages. Nusap1 depletion leads to chromosome misalignment, increased aneuploidy, and abnormal spindle assembly, particularly a decrease in spindle pole width. Correspondingly, RNA-seq analysis revealed significant suppression of the "establishment of spindle orientation" signaling pathway. Additionally, the attenuation of F-actin in NUSAP1-deficient oocytes may affect the asymmetric division process. Gene ontology analysis of NUSAP1 interactomes, identified through mass spectrometry here, revealed significant enrichment for RNA binding. As an RNA-binding protein, NUSAP1 is likely involved in the regulation of messenger RNA homeostasis by influencing the dynamics of processing (P)-body components. Overall, our results demonstrate the critical importance of precise regulation of NUSAP1 expression levels and protein localization for maintaining mouse oocyte meiosis.

Comprehensive pan-cancer analysis reveals NUSAP1 is a novel predictive biomarker for prognosis and immunotherapy response

Nucleolar and spindle-associated protein 1 (NUSAP1) is a microtubule-associated protein that plays a crucial role in mitosis. Despite initial reports suggesting a potential involvement of NUSAP1 in tumor progression and malignant cell regulation, there has been no systematic analysis of its role in the tumor immune microenvironment, nor its predictive value for prognosis and immunotherapy response across different cancer types. In this study, we analyze NUSAP1 mRNA and protein expression levels in various human normal and tumor tissues, using data from TCGA, GTEx, CPTAC, HPA databases, and clinical samples. Our findings reveal that NUSAP1 is highly expressed in multiple tumor tissues across most cancer types and is primarily expressed in malignant and immune cells, according to single-cell sequencing data from the TISCH database. Prognostic analysis based on curated survival data from the TCGA database indicates that NUSAP1 expression levels can predict clinical outcomes for 26 cancer types. Furthermore, Gene Set Enrichment Analysis (GSEA) suggests that NUSAP1 promotes cell proliferation, tumor cell invasion, and regulation of anti-tumor response. Analysis of immune score, immune cell infiltration, and anti-cancer immunity cycle using ESTIMATE, TIMER, and TIP databases show that high NUSAP1 levels are associated with low CD4+T and NKT cell infiltration but high Th2 and MDSC infiltration, inversely correlated with antigen-presenting molecules and positively correlated with a variety of immune negative regulatory molecules. Notably, patients with melanoma, lung, and kidney cancer with high NUSAP1 expression levels have shorter survival times and lower immunotherapy response rates. Using Cmap analysis, we identify Entinostat and AACOCF3 as potential inhibitors of NUSAP1-mediated pro-oncogenic effects. In vitro and in vivo experiments further confirm that NUSAP1 knockdown significantly reduces the proliferation ability of A549 and MCF-7 cells. Overall, our study highlights the potential of NUSAP1 expression as a novel biomarker for predicting prognosis and immuno-therapeutic efficacy across different human cancers and suggests its potential for developing novel antitumor drugs or improving immunotherapy.

Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer

Progress in ovarian cancer treatment lags behind other tumor types. With diagnosis usually at an advanced stage, there is a high demand for reliable prognostic biomarkers capable of the selection of effective chemo- and targeted therapies. Our goal was to establish a large-scale transcriptomic database and use it to uncover and rank survival-associated genes. Ovarian cancer cohorts with transcriptome-level gene expression data and clinical follow-up were identified from public repositories. All samples were normalized and entered into an integrated database. Cox univariate survival analysis was performed for all genes and was followed by multivariate analysis for selected genes involving clinical and pathological variables. False discovery rate was computed for multiple hypothesis testing and a 1% cutoff was used to determine statistical significance. The complete integrated database comprises 1816 samples from 17 datasets. Altogether, 2468 genes were correlated to progression-free survival (PFS), and 704 genes were correlated with overall survival (OS). The most significant genes were WBP1L, ASAP3, CNNM2, and NCAPH2 for progression-free survival and CSE1L, NUAK1, ALPK2, and SHKBP1 for overall survival. Genes significant for PFS were also preferentially significant for predicting OS as well. All data including HR and p values as well as the used cutoff values for all genes for both PFS and OS are provided to enable the ranking of future biomarker candidates across all genes. Our results help to prioritize genes and to neglect those which are most likely to fail in studies aiming to establish new clinically useful biomarkers and therapeutic targets in serous ovarian cancer.

Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis

Objectives

This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer.

Introduction

Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates.

Methods

We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer.

Results

We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables.

Conclusion

This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.