Deciphering the tumor-suppressive role of PSMB9 in melanoma through multi-omics and single-cell transcriptome analyses

The intersections between neuroscience and medulloblastoma

Medulloblastoma (MB) represents the most common malignant central nervous system tumor in childhood. The nervous system plays a critical role in the progression of MB, with interactions between the nervous system and cancer significantly influencing oncogenesis, tumor growth, invasion, stemness, and metabolism. These interactions also regulate angiogenesis, metastatic dissemination, the tumor immune microenvironment, and drug resistance. Investigating the nervous system-MB axis holds promise for identifying diagnostic markers, prognostic biomarkers, and therapeutic targets. It also provides insights into the molecular mechanisms underlying MB and informs the development of novel therapeutic strategies. This review summarizes the latest advancements in understanding the interplay between the nervous system and MB, including the role of glial cells in MB and the potential of drug repurposing targeting nervous system components for MB treatment. These findings underscore promising diagnostic and therapeutic opportunities for MB management. Additionally, we outline future research directions in neurosciences that may pave the way for innovative therapeutic approaches and deepen our understanding of this complex disease.

Comprehensive proteomic analysis and multidimensional model construction of peritoneal metastasis in gastric cancer

Peritoneal metastasis following gastric cancer surgery is often associated with a poor prognosis. This study aimed to investigate the mechanisms underlying peritoneal metastasis and to develop a predictive model for the risk of postoperative peritoneal metastases in gastric cancer. We performed a comprehensive analysis of the protein mass spectra and tumor microenvironment in paraffin-embedded primary tumor sections from gastric cancer patients, both with and without postoperative peritoneal metastases. Using proteomic profiling, we identified 9595 proteins and stratified patients into three distinct proteomic subgroups (Pro1, Pro2, Pro3) based on differential protein expression. Simultaneously, immune cell profiling allowed us to classify patients into four immune subgroups (IG-I, IG-II, IG-III, IG-IV). The relationships between these proteomic, immune, and metastasis classifications were further explored to uncover potential associations and mechanisms driving metastasis. Building on these insights, we developed an integrative model combining proteomics, immunological, and radiomics data for predicting postoperative peritoneal metastases. This model demonstrated high predictive efficacy, offering a robust tool for identifying high-risk patients. Our findings provide a deeper understanding of the biological processes underlying peritoneal metastasis in gastric cancer, highlighting the interplay between proteomic and immune factors. By establishing novel patient subgroups and an effective prediction model, this study lays the groundwork for early diagnosis and tailored therapeutic strategies to improve outcomes for gastric cancer patients.

Dual role of interferon-gamma in the response of melanoma patients to immunotherapy with immune checkpoint inhibitors

Interferon-gamma (IFN-γ) is a cytokine produced mainly by immune cells and can affect cancer cells by modulating the activity of multiple signaling pathways, including the canonical Janus-activated kinase/signal transducer and activator of transcription (JAK/STAT) cascade. In melanoma, IFN-γ can exert both anticancer effects associated with cell-cycle arrest and cell death induction and protumorigenic activity related to immune evasion leading to melanoma progression. Notably, IFN-γ plays a crucial role in the response of melanoma patients to immunotherapy with immune checkpoint inhibitors (ICIs), which are currently used in the clinic. As these agents target programmed death-1 (PD-1) and its ligand (PD-L1), cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and lymphocyte-activation gene 3 (LAG-3), they are designed to restore the antimelanoma immune response. In this respect, IFN-γ produced by cells in the tumor microenvironment in response to ICIs has a beneficial influence on both immune and melanoma cells by increasing antigen presentation, recruiting additional T-cells to the tumor site, and inducing direct antiproliferative effects and apoptosis in melanoma cells. Therefore, IFN-γ itself and IFN-γ-related gene signatures during the response to ICIs can constitute biomarkers or predictors of the clinical outcome of melanoma patients treated with ICIs. However, owing to its multifaceted roles, IFN-γ can also contribute to developing mechanisms associated with the acquisition of resistance to ICIs. These mechanisms can be associated with either decreased IFN-γ levels in the tumor microenvironment or diminished responsiveness to IFN-γ due to changes in the melanoma phenotypes associated with affected activity of other signaling pathways or genetic alterations e.g., in JAK, which restricts the ability of melanoma cells to respond to IFN-γ. In this respect, the influence of IFN-γ on melanoma-specific regulators of the dynamic plasticity of the cell phenotype, including microphthalmia-associated transcription factor (MITF) and nerve growth factor receptor (NGFR)/CD271 can affect the clinical efficacy of ICIs. This review comprehensively discusses the role of IFN-γ in the response of melanoma patients to ICIs with respect to its positive influence and role in IFN-γ-related mechanisms of resistance to ICIs as well as the potential use of predictive markers on the basis of IFN-γ levels and signatures of IFN-γ-dependent genes.

Integrative analysis of DNA methylation and gene expression in skin cutaneous melanoma by bioinformatic approaches

Skin cutaneous melanoma represents a significant threat among skin cancers. Investigating key methylated genes with prognostic implications remains an area ripe for exploration in this field. This study aims to identify survival-associated methylated genes and their specific methylation sites in skin cutaneous melanoma through integrated bioinformatic analysis. Utilizing data from the Cancer Genome Atlas database, gene methylation and expression files were analyzed. The results indicate that patients with skin cutaneous melanoma exhibiting high expression of hypomethylated HHEX experience better outcomes compared to those with low expression of hypermethylated HHEX. Furthermore, fifteen methylation sites within HHEX were found to significantly correlate with its expression levels. Expression of HHEX demonstrated a downward trend across pathological stages I-IV. The identified driven gene, HHEX, likely plays a crucial role in the survival of skin cutaneous melanoma patients. These findings provide new epigenetic insights and potential targets for early prognosis prediction in skin cutaneous melanoma.

Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer

OBJECTIVE

The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.

METHODS

A GNNs model was constructed to predict the immunotherapy response and identify key pathways. Based on the genes of key pathways, bioinformatic methods were used to generate a simple linear scoring model, namely responseScore. The intrinsic mechanism of responseScore was explored from the perspectives of multi-omics analysis. The relationship between each gene involved in responseScore and prognosis was also explored. Transfection experiments with human bladder cancer cells were used to investigate the biological effects of PSMB9 gene.

RESULTS

The final GNNs model had an AUC of 0.785 on the training set and an AUC of 0.839 on the validation set. R-HSA-69620 and others were identified as key pathways. ResponseScore had a good performance in predicted the immunotherapy response and prognosis. Analysis results from genetic variation, pathways and tumor microenvironment, showed that responseScore was significantly associated with immune cell infiltration and anti-tumor immunity. The results of single-cell analysis showed that responseScore was closely related to the functional state of natural killer cells. Compared with the PCDH-NC group, cell migration and proliferation were significantly inhibited while cell apoptosis increased in the PCDH-PSMB9 group.

CONCLUSION

The GNNs predictive model and responseScore constructed in this study can reflect the immunotherapy response and prognosis of bladder cancer patients. ResponseScore can also reflect features such as tumor microenvironment, antitumor immunity, and natural killer cell function status in bladder cancer. PSMB9 was identified as a significant gene for prognosis. High expression of PSMB9 can inhibit bladder cancer cell migration and proliferation while increasing cell apoptosis.

Biphasic co-detection of melanoma aneuploid tumor cells and tumor endothelial cells in guidance of specifying the field cancerized surgical excision margin and administering immunotherapy

An optimum safety excision margin (EM) delineated by precise demarcation of field cancerization along with reliable biomarkers that enable predicting and timely evaluating patients' response to immunotherapy significantly impact effective management of melanoma. In this study, optimized biphasic "immunofluorescence staining integrated with fluorescence insitu hybridization" (iFISH) was conducted along the diagnosis-metastasis-treatment-cellular MRD axis to longitudinally co-detect a full spectrum of intact CD31- aneuploid tumor cells (TCs), CD31+ aneuploid tumor endothelial cells (TECs), viable and necrotic circulating TCs (CTCs) and circulating TECs (CTECs) expressing PD-L1, Ki67, p16 and Vimentin in unsliced specimens of the resected primary tumor, EM, dissected sentinel lymph nodes (SLNs) and peripheral blood in an early-stage melanoma patient. Numerous PD-L1+ aneuploid TCs and TECs were detected at the conventional safety EM (2 cm), quantitatively indicating the existence of a field cancerized EM for the first time. Contrary to highly heterogeneous PD-L1 expression and degrees of Chr8 aneuploidy in TCs and TECs in the primary lesions as well as CTCs and CTECs in peripheral blood, almost all TCs and TECs in SLNs and EM were homogeneously PD-L1+ haploid cells. Dynamic monitoring and cellular MRD assessment revealed that, in contrast to PD-L1+ CTCs being responsive to the immune checkpoint inhibitor (ICI-anti-PD-1), multiploid (≥pentasomy 8) PD-L1+ and Ki67+ CTECs were respectively resistant to ICI-sensitized T cells. In therapeutically stressed lymphatic and hematogenous metastatic cascades, stratified phenotypic and karyotypic profiling of iFISH tissue and liquid biopsied TCs, TECs, CTCs and CTECs in future large-cohort studies will enable appropriate re-specification of the optimal safety EM and distribution mapping of in-depth characterized, subcategorized target cells to help illustrate their metastatic relevance, ultimately improving risk stratification and clinical intervention of tumor progression, metastases, therapy resistance and cancer relapse.

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.

METHODS

Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.

RESULTS

By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.

CONCLUSIONS

This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Deciphering a GPCR-lncrna-miRNA nexus: Identification of an aberrant therapeutic target in ovarian cancer

Ovarian cancer ranks as a leading cause of mortality among gynecological malignancies, primarily due to the lack of early diagnostic tools, effective targeted therapy, and clear understanding of disease etiology. Previous studies have identified the pivotal role of Lysophosphatidic acid (LPA)-signaling in ovarian cancer pathobiology. Our earlier transcriptomic analysis identified Urothelial Carcinoma Associated-1 (UCA1) as an LPA-stimulated long non-coding RNA (lncRNA). In this study, we elucidate the tripartite interaction between LPA-signaling, UCA1, and let-7 miRNAs in ovarian cancer progression. Results show that the elevated expression of UCA1 enhances cell proliferation, invasive migration, and therapy resistance in high-grade serous ovarian carcinoma cells, whereas silencing UCA1 reverses these oncogenic phenotypes. UCA1 expression inversely correlates with survival outcomes and therapy response in ovarian cancer clinical samples, underscoring its prognostic significance. Mechanistically, UCA1 sequesters let-7 miRNAs, effectively neutralizing their tumor-suppressive functions involving key oncogenes such as Ras and c-Myc. More significantly, intratumoral delivery of UCA1-specific siRNAs inhibits the growth of cisplatin-refractory ovarian cancer xenografts, demonstrating the therapeutic potential of targeting LPAR-UCA1-let-7 axis in ovarian cancer. Thus, our results identify LPAR-UCA1-let-7 axis as a novel avenue for targeted treatment strategies.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.