Identification of Secreted Phosphoprotein 1 (SPP1) as a Prognostic Factor in Lower-Grade Gliomas

Investigative needle core biopsies support multimodal deep-data generation in glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer with few effective therapies. Stereotactic needle biopsies are routinely used for diagnosis; however, the feasibility and utility of investigative biopsies to monitor treatment response remains ill-defined. Here, we demonstrate the depth of data generation possible from routine stereotactic needle core biopsies and perform highly resolved multi-omics analyses, including single-cell RNA sequencing, spatial transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics on standard biopsy tissue obtained intra-operatively. We also examine biopsies taken from different locations and provide a framework for measuring spatial and genomic heterogeneity. Finally, we investigate the utility of stereotactic biopsies as a method for generating patient-derived xenograft (PDX) models. Multimodal dataset integration highlights spatially mapped immune cell-associated metabolic pathways and validates inferred cell-cell ligand-receptor interactions. In conclusion, investigative biopsies provide data-rich insight into disease processes and may be useful in evaluating treatment responses.

Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas

Introduction

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma.

Methods

Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots.

Results

We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma.

Discussion

Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

Immune cell infiltration and drug response in glioblastoma multiforme: insights from oxidative stress-related genes

BACKGROUND

GBM, also known as glioblastoma multiforme, is the most prevalent and lethal type of brain cancer. The cell proliferation, invasion, angiogenesis, and treatment of gliomas are significantly influenced by oxidative stress. Nevertheless, the connection between ORGs and GBM remains poorly comprehended. The objective of this research is to investigate the predictive significance of ORGs in GBM and their potential as targets for therapy.

METHODS

We identified differentially expressed genes in glioma and ORGs from public databases. A risk model was established using LASSO regression and Cox analysis, and its performance was evaluated with ROC curves. We then performed consistent cluster analysis on the model, examining its correlation with immunity and drug response. Additionally, PCR, WB and IHC were employed to validate key genes within the prognostic model.

RESULTS

9 ORGs (H6PD, BMP2, SPP1, HADHA, SLC25A20, TXNIP, ACTA1, CCND1, EEF1A1) were selected via differential expression analysis, LASSO and Cox analysis, and incorporated into the risk model with high predictive accuracy. Enrichment analyses using GSVA and GSEA focused predominantly on malignancy-associated pathways. Subtype C of GBM had the best prognosis with the lowest risk score. Furthermore, the model exhibited a strong correlation with the infiltration of immune cells and had the capability to pinpoint potential targeted therapeutic medications for GBM. Ultimately, we selected HADHA for in vitro validation. The findings indicated that GBM exhibits a significant upregulation of HADHA. Knockdown of HADHA inhibited glioma cell proliferation and diminished their migration and invasion capacities and influenced the tumor growth in vivo.

CONCLUSION

The risk model, built upon 9 ORGs and the identification of GBM subtypes, suggests that ORGs have a broad application prospect in the clinical immunotherapy and targeted drug treatment of GBM. HADHA significantly influences the development of gliomas, both in vivo and in vitro.

Integrative multi-omics characterization reveals sex differences in glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults, with limited treatment modalities and poor prognosis. Recent studies have highlighted the importance of considering sex differences in cancer incidence, prognosis, molecular disparities, and treatment outcomes across various tumor types, including colorectal adenocarcinoma, lung adenocarcinoma, and GBM.

METHODS

We performed comprehensive analyses of large-scale multi-omics data (genomic, transcriptomic, and proteomic data) from TCGA, GLASS, and CPTAC to investigate the genetic and molecular determinants that contribute to the unique clinical properties of male and female GBM patients.

RESULTS

Our results revealed several key differences, including enrichments of MGMT promoter methylation, which correlated with increased overall and post-recurrence survival and improved response to chemotherapy in female patients. Moreover, female GBM exhibited a higher degree of genomic instability, including aneuploidy and tumor mutational burden. Integrative proteomic and phosphor-proteomic characterization uncovered sex-specific protein abundance and phosphorylation activities, including EGFR activation in males and SPP1 hyperphosphorylation in female patients. Lastly, the identified sex-specific biomarkers demonstrated prognostic significance, suggesting their potential as therapeutic targets.

CONCLUSIONS

Collectively, our study provides unprecedented insights into the fundamental modulators of tumor progression and clinical outcomes between male and female GBM patients and facilitates sex-specific treatment interventions. Highlights Female GBM patients were characterized by increased MGMT promoter methylation and favorable clinical outcomes compared to male patients. Female GBMs exhibited higher levels of genomic instability, including aneuploidy and TMB. Each sex-specific GBM is characterized by unique pathway dysregulations and molecular subtypes. EGFR activation is prevalent in male patients, while female patients are marked by SPP1 hyperphosphorylation.

Thrombin Cleavage of Osteopontin and the Host Anti-Tumor Immune Response

Osteopontin (OPN) is a multi-functional protein that is involved in various cellular processes such as cell adhesion, migration, and signaling. There is a single conserved thrombin cleavage site in OPN that, when cleaved, yields two fragments with different properties from full-length OPN. In cancer, OPN has tumor-promoting activity and plays a role in tumor growth and metastasis. High levels of OPN expression in cancer cells and tumor tissue are found in various types of cancer, including breast, lung, prostate, ovarian, colorectal, and pancreatic cancer, and are associated with poor prognosis and decreased survival rates. OPN promotes tumor progression and invasion by stimulating cell proliferation and angiogenesis and also facilitates the metastasis of cancer cells to other parts of the body by promoting cell adhesion and migration. Furthermore, OPN contributes to immune evasion by inhibiting the activity of immune cells. Thrombin cleavage of OPN initiates OPN's tumor-promoting activity, and thrombin cleavage fragments of OPN down-regulate the host immune anti-tumor response.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

OBJECTIVES

To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

METHODS

The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS.

RESULTS

The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01).

CONCLUSIONS

Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment.

KEY POINTS

• MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Secreted phosphoprotein 1 promotes angiogenesis of glioblastoma through upregulating PSMA expression via transcription factor HIF-1&alpha;

Glioblastoma multiforme (GBM) is a highly vascularized malignant brain tumor. Our previous study showed that prostate-specific membrane antigen (PSMA) promotes angiogenesis of GBM. However, the specific mechanism underlying GBM-induced PSMA upregulation remains unclear. In this study, we demonstrate that the GBM-secreted cytokine phosphoprotein 1 (SPP1) can regulate the expression of PSMA in human umbilical vein endothelial cells (HUVECs). Our mechanistic study further reveals that SPP1 regulates the expression of PSMA through the transcription factor HIF1α. Moreover, SPP1 promotes HUVEC migration and tube formation. In addition, HIF1α knockdown reduces the expression of PSMA in HUVECs and blocks the ability of SPP1 to promote HUVEC migration and tube formation. We further confirm that SPP1 is abundantly expressed in GBM, is associated with poor prognosis, and has high clinical diagnostic value with considerable sensitivity and specificity. Collectively, our findings identify that the GBM-secreted cytokine SPP1 upregulates PSMA expression in endothelial cells via the transcription factor HIF1α, providing insight into the angiogenic process and promising candidates for targeted GBM therapy.

Single Cell RNA-Seq Identifies Immune-Related Prognostic Model and Key Signature-SPP1 in Pancreatic Ductal Adenocarcinoma

There are no reliable biomarkers for early diagnosis or prognosis evaluation in pancreatic ductal adenocarcinoma (PDAC). Multiple scRNA-seq datasets for PDAC were retrieved from online databases and combined with scRNA-seq results from our previous study. The malignant ductal cells were identified through calculating copy number variation (CNV) scores. The robust markers of malignant ductal cells in PDAC were found. Five immune-related signatures, including SPP1, LINC00683, SNHG10, LINC00237, and CASC19, were used to develop a risk score formula to predict the overall survival of PDAC patients. We also constructed an easy-to-use nomogram, combining risk score, N stage, and margin status. The expression level of SPP1 was related to the prognosis and immune regulators. We found that SPP1 was mainly expressed in ductal cells and macrophages in PDAC. In conclusion, we constructed a promising prognostic model based on immune-related signatures for PDAC using scRNA-seq and TCGA_PAAD datasets.

Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Background: Glioma is the most prevalent malignant intracranial tumor. Many studies have shown that angiogenesis plays a crucial role in glioma tumorigenesis, metastasis, and prognosis. In this study, we conducted a comprehensive analysis of angiogenesis-related genes (ARGs) in glioma.

Methods: RNA-sequencing data of glioma patients were obtained from TCGA and CGGA databases. Via consensus clustering analysis, ARGs in the sequencing data were distinctly classified into two subgroups. We performed univariate Cox regression analysis to determine prognostic differentially expressed ARGs and least absolute shrinkage and selection operator Cox regression to construct a 14-ARG risk signature. The CIBERSORT algorithm was used to explore immune cell infiltration, and the ESTIMATE algorithm was applied to calculate immune and stromal scores.

Results: We found that the 14-ARG signature reflected the infiltration characteristics of different immune cells in the tumor immune microenvironment. Additionally, total tumor mutational burden increased significantly in the high-risk group. We combined the 14-ARG signature with patient clinicopathological data to construct a nomogram for predicting 1-, 3-, and 5-year overall survival with good accuracy. The predictive value of the prognostic model was verified in the CGGA cohort. SPP1 was a potential biomarker of glioma risk and was involved in the proliferation, invasion, and angiogenesis of glioma cells.

Conclusion: In conclusion, we established and validated a novel ARG risk signature that independently predicted the clinical outcomes of glioma patients and was associated with the tumor immune microenvironment.

Potential Progression Mechanism and Key Genes in Early Stage of mTBI

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease caused by repetitive mild traumatic brain injury (rmTBI), and the lack of sensitive diagnostic and prognostic biomarkers for rmTBI leads to long-term sequelae after injury. The purpose of this study is to identify key genes of rmTBI and find the potential progression mechanism in early stage of mTBI. We downloaded the gene expression profiles of GSE2871 from Gene Expression Omnibus (GEO) datasets. Differentially expressed genes (DEGs) were screened from the cerebral cortex of rats 24 hours after smTBI, and these DEGs were then subjected to GO enrichment analysis, KEGG pathway analysis, PPI analysis, and hub analysis. Key genes were identified as the most significantly expressed genes and had a higher degree of connectivity from hub genes. By using homemade metal pendulum impact equipment and a multiple regression discriminant equation to assess the severity of rats after injury, smTBI and rmTBI rat models were established in batches, and q-PCR analyses were performed to verify the key genes. The main KEGG pathways were cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. SPP1 and C3 were the most significant DEGs, and their connectivity degree was the highest 24 hours after smTBI (logFC > 4; connectivity degree ＞15). The q-PCR analyses were performed 24 hours and 14 days after mTBI. The results showed that SPP1 and C3 were significantly upregulated in smTBI and in rmTBI at 24 hours after injury compared with their levels in sham-injured rats, and the phenomenon persisted 14 days after injury. Notably, 14 days after injury, both of these genes were significantly upregulated in the rmTBI group compared with the smTBI. These pathways and genes identified could help understanding the development in mTBI.

Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Background: SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer. Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer. Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC. Conclusion: SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

Stromal Protein-Mediated Immune Regulation in Digestive Cancers

Simple Summary

Solid cancers are surrounded by a network of non-cancerous cells comprising different cell types, including fibroblasts, and acellular protein structures. This entire network is called the tumor microenvironment (TME) and it provides a physical barrier to the tumor shielding it from infiltrating immune cells, such as lymphocytes, or therapeutic agents. In addition, the TME has been shown to dampen efficient immune responses of infiltrated immune cells, which are key in eliminating cancer cells from the organism. In this review, we will discuss how TME proteins in particular are involved in this dampening effect, known as immunosuppression. We will focus on three different types of digestive cancers: pancreatic cancer, colorectal cancer, and gastric cancer. Moreover, we will discuss current therapeutic approaches using TME proteins as targets to reverse their immunosuppressive effects.

Abstract

The stromal tumor microenvironment (TME) consists of immune cells, vascular and neural structures, cancer-associated fibroblasts (CAFs), as well as extracellular matrix (ECM), and favors immune escape mechanisms promoting the initiation and progression of digestive cancers. Numerous ECM proteins released by stromal and tumor cells are crucial in providing physical rigidity to the TME, though they are also key regulators of the immune response against cancer cells by interacting directly with immune cells or engaging with immune regulatory molecules. Here, we discuss current knowledge of stromal proteins in digestive cancers including pancreatic cancer, colorectal cancer, and gastric cancer, focusing on their functions in inhibiting tumor immunity and enabling drug resistance. Moreover, we will discuss the implication of stromal proteins as therapeutic targets to unleash efficient immunotherapy-based treatments.

MiR-920 and LSP1 co-regulate the growth and migration of glioblastoma cells by modulation of JAK2/STAT5 pathway

This study probes the function and mechanism of lymphocyte-specific protein 1 (LSP1) in glioblastoma pathogenesis. According to the data acquired from TCGA, Oncomine and GEO databases, the expression and prognostic value of LSP1 and miR-920 in glioblastoma patients were analyzed. The expression levels of LSP1 in U251 and A172 cell lines were analyzed by qRT-PCR and western blotting. CCK8, colony formation and transwell assays were utilized to test glioblastoma cell malignant abilities. Furthermore, the associations between LSP1 and miR-920 were indentified by bioinformatics analysis and rescue assays. Moreover, the protein expression levels of p-JAK2, JAK2, p-STAT5 and STAT5, as the hallmark of JAK/STAT5 signaling, were detected by western blotting. The observations showed that LSP1 was highly augmented in glioblastoma samples. Additionally, up-regulation of LSP1 was associated with a unfavorable prognosis in glioblastoma patients. Biological experiments revealed that depletion of LSP1 significantly suppressed the proliferation, invasion and migration of U251 and A172 cells. MiR-920, as an upstream regulator of LSP1, negatively modulated LSP1 expression and promoted U251 cells malignant behaviors after miR-920 inhibitor treatment. However, together knockdown LSP1 and miR-920 inhibited these effects. Moreover, the expression levels of p-JAK2 and p-STAT5 were increased or decreased in U251 cells after transfection of miR-920 inhibitor or si-LPS1. Taken together, miR-920 might blocked the malignant development of glioblastoma cells, which is possibly realized by targeting LSP1 and modulation of JAK/STAT5 pathway. These findings implied that miR-920/LSP1 was a potential therapeutic target for glioblastoma treatment.

Expression and prognostic potential of GPX1 in human cancers based on data mining

BACKGROUND

Glutathione peroxidase-1 (GPX1) is a member of the GPX family, which considered an enzyme that interacts with oxidative stress. GPX1 differential expression is closely correlated with carcinogenesis and disease progression. In this study, we used bioinformatics analysis to investigate GPX1 expression level and explore the prognostic information in different human cancers.

METHODS

Expression was analyzed via the Oncomine database and Gene Expression Profiling Interactive Analysis tool, and potential prognostic analysis was evaluated using the UALCAN, GEPIA, and DriverDBv3 databases. Then, the UALCAN database was used to find the promoter methylation of GPX1 in defied cancer types. While GPX1 related functional networks were found within the GeneMANIA interactive tool and Cytoscape software. Moreover, Metascape online website was used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways.

RESULTS

We found that GPX1 was commonly overexpressed in most human cancers. High expression of GPX1 could lead to poor outcomes in Brain Lower Grade Glioma, while GPX1 over expression was correlated with better prognosis in Kidney renal papillary cell carcinoma (KIPP). High GPX1 expression was marginally associated with poor prognosis in acute myeloid leukemia (AML). Gene regulation network suggested that GPX1 mainly involved in pathways including the glutathione metabolism, ferroptosis, TP53 regulates metabolic genes, reactive oxygen species (ROS) metabolic process, and several other signaling pathways.

CONCLUSIONS

Our findings revealed that GPX1 showed significant expression differences among cancers and served as a prognostic biomarker for defined cancer types. The data mining effectively revealed useful information about GPX1 expression, prognostic values, and potential functional networks in cancers, thus providing researchers with an available way to further explore the mechanism underlying carcinogenesis of genes of interest in different cancers.

Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research

Cancer research requires models closely resembling the tumor in the patient. Human tissue cultures can overcome interspecies limitations of animal models or the loss of tissue architecture in in vitro models. However, analysis of tissue slices is often limited to histology. Here, we demonstrate that slices are also suitable for whole transcriptome sequencing and present a method for automated histochemistry of whole slices. Tumor and peritumoral tissue from a patient with glioblastoma was processed to slice cultures, which were treated with standard therapy including temozolomide and X-irradiation. Then, RNA sequencing and automated histochemistry were performed. RNA sequencing was successfully accomplished with a sequencing depth of 243 to 368 x 10⁶ reads per sample. Comparing tumor and peritumoral tissue, we identified 1888 genes significantly downregulated and 2382 genes upregulated in tumor. Treatment significantly downregulated 2017 genes, whereas 1399 genes were upregulated. Pathway analysis revealed changes in the expression profile of treated glioblastoma tissue pointing towards downregulated proliferation. This was confirmed by automated analysis of whole tissue slices stained for Ki67. In conclusion, we demonstrate that RNA sequencing of tissue slices is possible and that histochemical analysis of whole tissue slices can be automated which increases the usability of this preclinical model.