Clinical significance of nonerythrocytic spectrin Beta 1 (SPTBN1) in human kidney renal clear cell carcinoma and uveal melanoma: a study based on Pan-Cancer Analysis

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma

BACKGROUND

Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies.

OBJECTIVE

We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells.

METHODS

The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells.

RESULTS

A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1.

CONCLUSION

The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

SPTBN1 Mediates the Cytoplasmic Constraint of PTTG1, Impairing Its Oncogenic Activity in Human Seminoma

Seminoma is the most common testicular cancer. Pituitary tumor-transforming gene 1 (PTTG1) is a securin showing oncogenic activity in several tumors. We previously demonstrated that nuclear PTTG1 promotes seminoma tumor invasion through its transcriptional activity on matrix metalloproteinase 2 (MMP-2) and E-cadherin (CDH1). We wondered if specific interactors could affect its subcellular distribution. To this aim, we investigated the PTTG1 interactome in seminoma cell lines showing different PTTG1 nuclear levels correlated with invasive properties. A proteomic approach upon PTTG1 immunoprecipitation uncovered new specific securin interactors. Western blot, confocal microscopy, cytoplasmic/nuclear fractionation, sphere-forming assay, and Atlas database interrogation were performed to validate the proteomic results and to investigate the interplay between PTTG1 and newly uncovered partners. We observed that spectrin beta-chain (SPTBN1) and PTTG1 were cofactors, with SPTBN1 anchoring the securin in the cytoplasm. SPTBN1 downregulation determined PTTG1 nuclear translocation, promoting its invasive capability. Moreover, a PTTG1 deletion mutant lacking SPTBN1 binding was strongly localized in the nucleus. The Atlas database revealed that seminomas that contained higher nuclear PTTG1 levels showed significantly lower SPTBN1 levels in comparison to non-seminomas. In human seminoma specimens, we found a strong PTTG1/SPTBN1 colocalization that decreases in areas with nuclear PTTG1 distribution. Overall, these results suggest that SPTBN1, along with PTTG1, is a potential prognostic factor useful in the clinical management of seminoma.

Development and validation of a nomogram based on common biochemical indicators for survival prediction of children with high-risk neuroblastoma: A valuable tool for resource-limited hospitals

BACKGROUND

Despite multiple attempts have been made to develop risk stratification within high-risk neuroblastoma (NB) patients (age of diagnosis ≥ 18 month-old with metastatic NB), the definition of "ultra high-risk NB" is still lack of consensus, and indicators for identifying this subgroup are still unclear. This study aimed to develop a nomogram based on easy-to-obtain blood-derived biofactors for identifying ultra high-risk NB patients with highest risk of death within 3 or 5 years.

METHODS

One hundred sixty-seven NB patients who treated at Sun Yat-sen University Cancer Center between 2015 and 2023 were recruited and clustered randomly into training and validation cohorts (116 and 51 cases, respectively). Univariate and multivariate Cox analysis were performed in training set to screen independent prognostic indicators for constructing nomogram model of predicting 1-, 3- and 5-year overall survival (OS). The discrimination power of the nomogram in training and validation sets were assessed by concordance index (C-index) and calibration plot. Based on the risk score obtained from nomogram model, the prognostic accuracy of 1-, 3- and 5-year OS rates in training and validation cohorts were further evaluated using the area under receiver operating characteristic (ROC) curves (AUC).

RESULTS

Through univariate and multivariate Cox analysis, independent prognostic indicators, including serum lactate dehydrogenase (LDH) and albumin (ALB), were identified in training set, and used to establish a nomogram model. The model showed good discrimination power with C-index in training cohort being 0.706 (95%CI: 0.633-0.788). According to the cut-point calculated based on the established nomogram, patients with a nomogram score > 34 points could be stratified to ultra high-risk NB subgroup, and this subgroup had poorer OS than those in non-ultra one (p < 0.001). AUC values of ROC curves for 3- and 5-year OS rates in the training set were 0.758 and 0.756, respectively. Moreover, based on the cut-point score (34 points) developed in training set, The model also showed good discrimination power with C-index of 0.773 (95%CI: 0.664-0.897) and powerful prognostic accuracy of AUC for 3- and 5-year OS rates being 0.825 and 0.826, respectively, in validation cohort.

CONCLUSIONS

We developed a simple-to-use nomogram based on common laboratory indicators to identify the subgroup of ultra high-risk NB before treatment, providing these children even from developing countries or regions access to intensified multimodal treatments earlier and thus improving their long-term outcome.