Important oncogenic and immunogenic roles of SPP1 and CSF1 in hepatocellular carcinoma

Exploring common genomic biomarkers to disclose common drugs for the treatment of colorectal cancer and hepatocellular carcinoma with type-2 diabetes through transcriptomics analysis

Type 2 diabetes (T2D) is a crucial risk factor for both colorectal cancer (CRC) and hepatocellular carcinoma (HCC). However, so far, there was no study that has investigated common drugs against HCC and CRC during their co-occurrence with T2D patients. Consequently, patients often require multiple disease-specific multiple drugs, which can lead toxicities and adverse effects to the patients due to drug-drug interactions. This study aimed to identify common genomic biomarkers (cGBs) and associated pathogenetic mechanisms underlying CRC, HCC, and T2D to uncover potential common therapeutic compounds against these three diseases. Firstly, we identified 86 common differentially expressed genes (cDEGs) capable of separating each of CRC, HCC and T2D patients from control groups based on transcriptomic profiling. Of these cDEGs, 37 genes were upregulated and 49 were downregulated. Genetic association studies based on average of Log2 fold-change (aLog2FC) of cDEGs suggested a genetic association among CRC, HCC and T2D. Subsequently, six top-ranked cDEGs (MYC, MMP9, THBS1, IL6, CXCL1, and SPP1) were identified as common genomic biomarkers (cGBs) through protein-protein interaction (PPI) network analysis. Further analysis of these cGBs with GO-terms and KEGG pathways revealed shared pathogenetic mechanisms of three diseases, including specific biological processes, molecular functions, cellular components and signaling pathways. The gene co-regulatory network analysis identified two transcription factors (FOXC1 and GATA2) and three miRNAs (hsa-mir-195-5p, hsa-mir-124a-3p, and hsa-mir-34a-5p) as crucial transcriptional and post-transcriptional regulators of the cGBs. Finally, cGBs-guided seven candidate drugs (Digitoxin, Camptosar, AMG-900, Imatinib, Irinotecan, Midostaurin, and Linsitinib) as the common treatment against T2D, CRC and HCC were identified through molecular docking, cross-validation, and ADME/T (Absorption-Distribution-Metabolism-Excretion-Toxicity) analysis. Most of these findings received support by the literature review of diseases specific individual studies. Thus, this study offers valuable insights for researchers and clinicians to improve the diagnosis and treatment of CRC and/or HCC patients during the co-occurrence of T2D.

SPP1 as a Prognostic and Immunotherapeutic Biomarker in Gliomas and Other Cancer Types: A Pan-Cancer Study

Background

Gliomas, including glioblastoma (GBM), present significant treatment challenges due to their poor prognosis and complex tumor microenvironment. This study investigates the role of Secreted Phosphoprotein 1 (SPP1) as a prognostic and immunotherapeutic biomarker in gliomas and other cancers through pan-cancer analysis.

Methods

A comprehensive pan-cancer analysis was conducted using datasets from UCSC TCGA Pan-Cancer, TCGA-GBM, UALCAN, and single-cell sequencing data from GEO and TISCH. The correlation of SPP1 expression with overall survival (OS), progression-free survival (PFS), immune cell infiltration, and immune checkpoint markers was analyzed. Functional validation was performed via SPP1 knockdown in glioma cell lines to evaluate effects on proliferation, invasion, and immune interactions.

Results

SPP1 was found to be overexpressed in 27 tumor types, with high expression correlating with poor OS, PFS, and increased immune cell infiltration, particularly with CD8+ T cells and macrophages. Single-cell analysis indicated SPP1 enrichment in macrophages interacting with malignant GBM cells. Knockdown of SPP1 significantly inhibited glioma cell proliferation, invasion, and promoted apoptosis.

Conclusion

The findings suggest that SPP1 is a promising target for immunotherapy, potentially improving outcomes for patients with gliomas and other cancers. Further research is warranted to explore SPP1-targeted therapies and their efficacy in clinical settings.

Identification of the entosis-related prognostic signature and tumour microenvironment in hepatocellular carcinoma on the basis of bioinformatics analysis and experimental validation

Liver cancer ranks among the deadliest cancers worldwide. Entosis, a recently uncovered method of cell death, has not yet been fully explored for its relevance to HCC. A bioinformatics analysis was performed to determine the expression and mutational landscapes of Entosis-related genes (ERGs). A subset of differentially expressed Entosis-related genes (DEERGs) was generated. A risk model for entosis was subsequently constructed employing LASSO and Cox regression methodologies. The correlations among ERGs, genes associated with risk, the developed risk model, and the immune context of the tumour were explored. Furthermore, the study investigated the varying drug sensitivities between high-risk and slight-risk patient groups. The expression patterns of four pivotal risk genes were delineated via qRT‒PCR and WB. A prognostic model comprising four DEERGs (KIF18A, SPP1, LCAT and TRIB3) was developed. The ability of this model to predict the survival outcomes of patients with HCC was confirmed through receiver operating characteristic curve analysis. Patients were grouped according to their risk assessments, revealing that the low-risk population demonstrated a more favourable survival outcome than did the high-risk population. The high-risk population presented reduced tumour stroma, immune and ESTIMATE scores, along with an increased proportion of cancer stem cells and tumour mutation burden. Additionally, a connection between the risk model and the responsiveness of various chemotherapy drugs as well as the efficacy of immunotherapies in patients was noted. These findings provide significant guidance for the development of targeted clinical treatment strategies. qRT‒PCR and WB analysis revealed that the gene expression of KIF18A and SPP1 were elevated in HCCLM3 cells compared with that in THLE2 cells; whereas, the expression level of LCAT and TIRB3 was decreased. The four genes KIF18A, SPP1, LCAT and TRIB3 could effectively predict the survival prognosis of patients with liver cancer. KIF18A and SPP1 were elevated in HCC tissues compared with that in THLE2 cells.

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection

Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecules from different biological modalities and the limited sample size. Here, we introduce GD-Net, a Graph Deep learning algorithm to enhance the accuracy of survival prediction with an average accuracy of 72% by early fusing of multimodal information, which includes an interpretable and lightweight XGBoost module to efficiently extract informative features. First, we applied GD-Net to eight cancer datasets and achieved superior performance compared to benchmarking methods, with an average 7.9% higher C-index value. The ablation experiments strongly supported that multi-modal integration could significantly improve accuracy over the single-modality model. In the deep case study of liver cancer, 319 differential genes, 15 differential miRNAs and 155 methylated differential genes based on the predicted risk subgroups are identified as the informative features, and then we have statistically and biologically validated the efficacy of these key molecules in internal and external test datasets. The comprehensive independent validations demonstrated that GD-Net is accurate and competitive in predicting different cancer outcomes in real-time, and it is an effective tool for identifying new multimodal prognosis biomarkers.

A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets

Traumatic brain injury (TBI) heterogeneity remains a critical barrier to translating therapies. Identifying final common pathways/molecular signatures that integrate this heterogeneity informs biomarker and therapeutic-target development. We present the first large-scale murine single-cell atlas of the transcriptomic response to TBI (334,376 cells) across clinically relevant models, sex, brain region, and time as a foundational step in molecularly deconstructing TBI heterogeneity. Results were unique to cell populations, injury models, sex, brain regions, and time, highlighting the importance of cell-level resolution. We identify cell-specific targets and previously unrecognized roles for microglial and ependymal subtypes. Ependymal-4 was a hub of neuroinflammatory signaling. A distinct microglial lineage shared features with disease-associated microglia at 24 h, with persistent gene-expression changes in microglia-4 even 6 months after contusional TBI, contrasting all other cell types that mostly returned to naive levels. Regional and sexual dimorphism were noted. CEREBRI, our searchable atlas (https://shiny.crc.pitt.edu/cerebri/), identifies previously unrecognized cell subtypes/molecular targets and is a leverageable platform for future efforts in TBI and other diseases with overlapping pathophysiology.

Axatilimab in Recurrent or Refractory Chronic Graft-versus-Host Disease

BACKGROUND

Colony-stimulating factor 1 receptor (CSF1R)-dependent monocytes and macrophages are key mediators of chronic graft-versus-host disease (GVHD), a major long-term complication of allogeneic hematopoietic stem-cell transplantation. The CSF1R-blocking antibody axatilimab has shown promising clinical activity in chronic GVHD.

METHODS

In this phase 2, multinational, pivotal, randomized study, we evaluated axatilimab at three different doses in patients with recurrent or refractory chronic GVHD. Patients were randomly assigned to receive axatilimab, administered intravenously, at a dose of 0.3 mg per kilogram of body weight every 2 weeks (0.3-mg dose group), at a dose of 1 mg per kilogram every 2 weeks (1-mg dose group), or at a dose of 3 mg per kilogram every 4 weeks (3-mg dose group). The primary end point was overall response (complete or partial response) in the first six cycles; the key secondary end point was a patient-reported decrease in chronic GVHD symptom burden, as assessed by a reduction of more than 5 points on the modified Lee Symptom Scale (range, 0 to 100, with higher scores indicating worse symptoms). The primary end point would be met if the lower bound of the 95% confidence interval exceeded 30%.

RESULTS

A total of 241 patients were enrolled (80 patients in the 0.3-mg dose group, 81 in the 1-mg dose group, and 80 in the 3-mg dose group). The primary end point was met in all the groups; an overall response was observed in 74% (95% confidence interval [CI], 63 to 83) of the patients in the 0.3-mg dose group, 67% (95% CI, 55 to 77) of the patients in the 1-mg dose group, and 50% (95% CI, 39 to 61) of the patients in the 3-mg dose group. A reduction of more than 5 points on the modified Lee Symptom Scale was reported in 60%, 69%, and 41% of the patients in the three dose groups, respectively. The most common adverse events were dose-dependent transient laboratory abnormalities related to CSF1R blockade. Adverse events leading to discontinuation of axatilimab occurred in 6% of the patients in the 0.3-mg dose group, 22% in the 1-mg dose group, and 18% in the 3-mg dose group.

CONCLUSIONS

Targeting CSF1R-dependent monocytes and macrophages with axatilimab resulted in a high incidence of response among patients with recurrent or refractory chronic GVHD. (Funded by Syndax Pharmaceuticals and Incyte; AGAVE-201 ClinicalTrials.gov number, NCT04710576.).

Benchmark N-glycoproteomics study of common differential tissue and serum N-glycoproteins of patients with hepatocellular carcinoma

For hepatocellular carcinoma (HCC), N-glycosylation has been proved to be widely involved in various aspects of the disease, including development, metastasis, subtyping, diagnosis and prognosis. The common practice is to discover biomarkers in situ of cancer occurrence (i.e., cancer vs. adjacent tissues) yet to clinically monitor in sera because of non-invasiveness. This study benchmarks N-glycoproteomics characterization of common differential tissue and serum N-glycoproteins of patients with HCC. Differential N-glycosylation in matched tissue and serum samples from the same patients were quantitatively characterized at the intact N-glycopeptide molecular level, and 29 common N-glycoproteins were found. Subcellular localization analysis was carried out to confirm the tissue originality. Secreted N-glycoprotein APOH was up-regulated, and transmembrane and intracellular N-glycoproteins including OSMR, GAT2, CSF-1 and MAGI3 were down-regulated.

Screening and molecular docking verification of feature genes related to phospholipid metabolism in hepatocarcinoma caused by hepatitis B

BACKGROUND

The progression of tumours is related to abnormal phospholipid metabolism. This study is anticipated to present a fresh perspective for disease therapy targets of hepatocarcinoma caused by hepatitis B virus in the future by screening feature genes related to phospholipid metabolism.

METHODS

This study analysed GSE121248 to pinpoint differentially expressed genes (DEGs). By examining the overlap between the metabolism-related genes and DEGs, the research focused on the genes involved in phospholipid metabolism. To find feature genes, functional enrichment studies were carried out and a network diagram was proposed. These findings were validated via data base of The Cancer Genome Atlas (TCGA). Further analyses included immune infiltration studies and metabolomics. Finally, the relationships between differentially abundant metabolites and feature genes were confirmed by molecular docking, providing a thorough comprehension of the molecular mechanisms.

RESULTS

The seven genes with the highest degree of connection (PTGS2, IGF1, SPP1, BCHE, NR1I2, NAMPT, and FABP1) were identified as feature genes. In the TCGA database, the seven feature genes also had certain diagnostic efficiency. Immune infiltration analysis revealed that feature genes regulate the infiltration of various immune cells. Metabolomics successfully identified the different metabolites of the phospholipid metabolism pathway between patients and normal individuals. The docking study indicated that different metabolites may play essential roles in causing disease by targeting feature genes.

CONCLUSIONS

In this study, for the first time, it reveals the possible involvement of genes linked to phospholipid metabolism-related genes using bioinformatics analysis. Identifying genes and probable therapeutic targets could provide clues for the further treatment of disease.

Macrophage colony-stimulating factor and its role in the tumor microenvironment: novel therapeutic avenues and mechanistic insights

The tumor microenvironment is a complex ecosystem where various cellular and molecular interactions shape the course of cancer progression. Macrophage colony-stimulating factor (M-CSF) plays a pivotal role in this context. This study delves into the biological properties and functions of M-CSF in regulating tumor-associated macrophages (TAMs) and its role in modulating host immune responses. Through the specific binding to its receptor colony-stimulating factor 1 receptor (CSF-1R), M-CSF orchestrates a cascade of downstream signaling pathways to modulate macrophage activation, polarization, and proliferation. Furthermore, M-CSF extends its influence to other immune cell populations, including dendritic cells. Notably, the heightened expression of M-CSF within the tumor microenvironment is often associated with dismal patient prognoses. Therefore, a comprehensive investigation into the roles of M-CSF in tumor growth advances our comprehension of tumor development mechanisms and unveils promising novel strategies and approaches for cancer treatment.

Revealing cell-cell communication pathways with their spatially coupled gene programs

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

Deciphering hepatocellular carcinoma pathogenesis and therapeutics: a study on anoikis, ceRNA regulatory network and traditional Chinese medicine

Introduction: Hepatocellular carcinoma (HCC) is responsible for approximately 90% of liver malignancies and is the third most common cause of cancer-related mortality worldwide. However, the role of anoikis, a programmed cell death mechanism crucial for maintaining tissue equilibrium, is not yet fully understood in the context of HCC. Methods: Our study aimed to investigate the expression of 10 anoikis-related genes (ARGs) in HCC, including BIRC5, SFN, UBE2C, SPP1, E2F1, etc., and their significance in the disease. Results: Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we discovered that these ARGs are involved in important processes such as tissue homeostasis, ion transport, cell cycle regulation, and viral infection pathways. Furthermore, we found a significant correlation between the prognostic value of five ARGs and immune cell infiltrates. Analysis of clinical datasets revealed a strong association between BIRC5 expression and HCC pathological progression, including pathological stage, T stage, overall survival (OS), and race. By constructing a competing endogenous RNA (ceRNA) network and using molecular docking, we identified ten bioactive compounds from traditional Chinese medicine (TCM) that could potentially modulate BIRC5. Subsequent in vitro experiments confirmed the influence of platycodin D, one of the identified compounds, on key elements within the ceRNA network. Discussion: In conclusion, our study presents a novel framework for an anoikis-centered prognostic model and an immune-involved ceRNA network in HCC, revealing potential regulatory targets. These insights contribute to our understanding of HCC pathology and may lead to improved therapeutic interventions.