Genomic Analysis Made It Possible to Identify Gene-Driver Alterations Covering the Time Window between Diagnosis of Neuroblastoma 4S and the Progression to Stage 4

Unignored intracellular journey and biomedical applications of extracellular vesicles

The intracellular journey of extracellular vesicles (EVs) cannot be ignored in various biological pathological processes. In this review, the biogenesis, biological functions, uptake pathways, intracellular trafficking routes, and biomedical applications of EVs were highlighted. Endosomal escape is a unique mode of EVs release. When vesicles escape from endosomes, they avoid the fate of fusing with lysosomes and being degraded, thus having the opportunity to directly enter the cytoplasm or other organelles. This escape mechanism is crucial for EVs to deliver specific signals or substances. The intracellular trafficking of EVs after endosomal escape is a complex and significant biological process that involves the coordinated work of various cellular structures and molecules. Through the in-depth study of this process, the function and regulatory mechanism of EVs are fully understood, providing new dimensions for future biomedical diagnosis and treatment.

Neuroblastoma Patients' Outcome and Chromosomal Instability

Chromosomal instability (CIN) induces a high rate of losses or gains of whole chromosomes or parts of chromosomes. It is a hallmark of most human cancers and one of the causes of aneuploidy and intra-tumor heterogeneity. The present study aimed to evaluate the potential prognostic role of CIN in NB patients at diagnosis. We performed array comparative genomic hybridization analyses on 451 primary NB patients at the onset of the disease. To assess global chromosomal instability with high precision, we focused on the total number of DNA breakpoints of gains or losses of chromosome arms. For each tumor, an array-CGH-based breakpoint instability index (BPI) was assigned which defined the total number of chromosomal breakpoints per genome. This approach allowed us to quantify CIN related to whole genome disruption in all NB cases analyzed. We found differences in chromosomal breakages among the NB clinical risk groups. High BPI values are negatively associated with survival of NB patients. This association remains significant when correcting for stage, age, and MYCN status in the Cox model. Stratified analysis confirms the prognostic effect of BPI index in low-risk NB patients with non-amplified MYCN and with segmental chromosome aberrations.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Machine learning immune-related gene based on KLRB1 model for predicting the prognosis and immune cell infiltration of breast cancer

Objective

Breast cancer is a prevalent malignancy that predominantly affects women. The development and progression of this disease are strongly influenced by the tumor microenvironment and immune infiltration. Therefore, investigating immune-related genes associated with breast cancer prognosis is a crucial approach to enhance the diagnosis and treatment of breast cancer.

Methods

We analyzed data from the TCGA database to determine the proportion of invasive immune cells, immune components, and matrix components in breast cancer patients. Using this data, we constructed a risk prediction model to predict breast cancer prognosis and evaluated the correlation between KLRB1 expression and clinicopathological features and immune invasion. Additionally, we investigated the role of KLRB1 in breast cancer using various experimental techniques including real-time quantitative PCR, MTT assays, Transwell assays, Wound healing assays, EdU assays, and flow cytometry.

Results

The functional enrichment analysis of immune and stromal components in breast cancer revealed that T cell activation, differentiation, and regulation, as well as lymphocyte differentiation and regulation, play critical roles in determining the status of the tumor microenvironment. These DEGs are therefore considered key factors affecting TME status. Additionally, immune-related gene risk models were constructed and found to be effective predictors of breast cancer prognosis. Further analysis through KM survival analysis and univariate and multivariate Cox regression analysis demonstrated that KLRB1 is an independent prognostic factor for breast cancer. KLRB1 is closely associated with immunoinfiltrating cells. Finally, in vitro experiments confirmed that overexpression of KLRB1 inhibits breast cancer cell proliferation, migration, invasion, and DNA replication ability. KLRB1 was also found to inhibit the proliferation of breast cancer cells by blocking cell division in the G1/M phase.

Conclusion

KLRB1 may be a potential prognostic marker and therapeutic target associated with the microenzymic environment of breast cancer tumors, providing a new direction for breast cancer treatment.

Metastasis pattern and prognosis in children with neuroblastoma

BACKGROUND

We aimed to investigate the different metastases and prognoses of neuroblastoma (NB) and determine the risk factors of metastasis.

METHOD

Data of 1224 patients with NB were obtained from the Surveillance, Epidemiology and End Results database (2010-2018). Pearson's chi-square test, Kaplan-Meier analysis, multivariable logistic regression and Cox regression analysis were used to determine the factors associated with prognosis.

RESULTS

The overall incidence of NB was an age-adjusted rate of 8.2 patients per 1,000,000 children. In total, 1224 patients were included in our study, with 599 patients (48.9%) exhibiting distant metastases. Compared to patients with non-metastatic NB, a greater proportion of patients with metastatic NB were under 1 year, male, had an adrenal primary site, unilateral tumour, a tumour size > 10 cm, neuroblastoma-not otherwise specified (NB-NOS), second malignant neoplasms and were more likely to choose radiotherapy and chemotherapy. Multivariate Cox regression showed that metastasis was an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). The survival rate of non-metastatic patients with NB was better than those with metastasis (OS: hazard ratio (HR): 0.248, P < 0.001; CSS: HR: 0.267, P < 0.001). The bone and liver were the two most common isolated metastatic sites in NB. However, no statistical difference was observed in OS and CSS between the only bone metastasis group, only liver metastasis group and bone metastasis combined with liver metastasis group (all P > 0.05). Additionally, age at diagnosis > 1 year (odds ratio (OR): 3.295, P < 0 .001), grades III-IV (OR: 26.228, P < 0 .001) and 5-10 cm tumours (OR: 1.781, P < 0 .001) increased the risk of bone metastasis of NB. Moreover, no surgical treatment (OR: 2.441, P < 0 .001) increased the risk of liver metastasis of NB.

CONCLUSION

Metastatic NB has unique clinicopathological features, with the bone and liver as the most common single metastatic sites of NB. Therefore, more aggressive treatment is recommended for high-risk children with NB displaying distant metastases.

A Simple, Test-Based Method to Control the Overestimation Bias in the Analysis of Potential Prognostic Tumour Markers

The early evaluation of prognostic tumour markers is commonly performed by comparing the survival of two groups of patients identified on the basis of a cut-off value. The corresponding hazard ratio (HR) is usually estimated, representing a measure of the relative risk between patients with marker values above and below the cut-off. A posteriori methods identifying an optimal cut-off are appropriate when the functional form of the relation between the marker distribution and patient survival is unknown, but they are prone to an overestimation bias. In the presence of a small sample size, which is typical of rare diseases, the external validation sets are hardly available and internal cross-validation could be unfeasible. We describe a new method to obtain an unbiased estimate of the HR at an optimal cut-off, exploiting the simple relation between the HR and the associated p-value estimated by a random permutation analysis. We validate the method on both simulated data and set of gene expression profiles from two large, publicly available data sets. Furthermore, a reanalysis of a previously published study, which included 134 Stage 4S neuroblastoma patients, allowed for the identification of E2F1 as a new gene with potential oncogenic activity. This finding was confirmed by an immunofluorescence analysis on an independent cohort.

Exosomes in Neuroblastoma Biology, Diagnosis, and Treatment

Neuroblastoma is an extracranial solid tumour of the developing sympathetic nervous system accounting for circa 15% of deaths due to cancer in paediatric patients. The clinical course of this cancer may be variable, ranging from aggressive progression to regression, while the amplification of MYCN in this cancer is linked to poor patient prognosis. Extracellular vesicles are a double membrane encapsulating various cellular components including proteins and nucleic acids and comprise exosomes, apoptotic bodies, and microvesicles. The former can act as mediators between cancer, stromal and immune cells and thereby influence the tumour microenvironment by the delivery of their molecular cargo. In this study, the contribution of extracellular vesicles including exosomes to the biology, prognosis, diagnosis and treatment of neuroblastoma was catalogued, summarised and discussed. The understanding of these processes may facilitate the in-depth dissection of the complexity of neuroblastoma biology, mechanisms of regression or progression, and potential diagnostic and treatment options for this paediatric cancer which will ultimately improve the quality of life of neuroblastoma patients.