Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

Conformation-sensitive targeting of CD18 depletes M2-like tumor-associated macrophages resulting in inhibition of solid tumor progression

BACKGROUND

Tumor-associated macrophages (TAMs) primarily exist in the M2-like phenotype in the tumor microenvironment (TME). M2-TAMs contribute to tumor progression by establishing an immunosuppressive environment. However, TAM targeting is hindered, mainly owing to a lack of specific biomarkers for M2-TAMs. Previously, we demonstrated that a novel peptide drug conjugate (TB511) consisting of a TAM-binding peptide and the apoptosis-promoting peptide targets M2-TAMs. This was achieved through M2-TAM targeting, although the target mechanism of action remained elusive. Herein, we elucidate the anticancer efficacy of TB511 by identifying new target proteins that preferentially bind to M2-TAMs and clarifying the apoptosis-inducing mechanism in these cells.

METHODS

We investigated the target proteins and binding site of TB511 using LC-MS/MS analyses, surface plasmon resonance and peptide-protein interaction 3D modeling. Activated CD18 expression in M2 TAMs was assessed using Quantibrite PE beads in PBMCs. The anticancer efficacy of TB511 was tested using colorectal cancer (CRC) and non-small cell lung cancer (NSCLC) mouse model. The immunotherapeutic effect of TB511 was investigated through spatial transcriptomics in human pancreatic ductal adenocarcinoma (PDAC) model.

RESULTS

Activated CD18 was highly expressed in human tumor tissues and was significantly higher in M2 TAMs than in other immune cells. TB511 showed high binding affinity to CD18 among the cell membrane proteins of M2 macrophages and appeared to bind to the cysteine-rich domain in the activated form. Moreover, TB511 specifically induced apoptosis in M2 TAMs, but its targeting ability to M2 macrophages was inhibited in CD18 blockade or knockout model. In mouse or humanized mouse models of solid tumors such as CRC, NSCLC, and PDAC, TB511 suppressed tumor growth by targeting M2-TAMs via CD18 and enhancing the presence of CD8+ T cells in the TME.

CONCLUSIONS

Collectively, our findings suggest that activated CD18 holds promise as a novel target protein for cancer therapy, and TB511 shows potential as a therapeutic agent for tumor treatment.

Integrated Transcriptomic and Proteomic Analyses of Antler Growth and Ossification Mechanisms

Antlers are the sole mammalian organs capable of continuous regeneration. This distinctive feature has evolved into various biomedical models. Research on mechanisms of antler growth, development, and ossification provides valuable insights for limb regeneration, cartilage-related diseases, and cancer mechanisms. Here, ribonucleic acid sequencing (RNA-seq) and four-dimensional data-independent acquisition (4D DIA) technologies were employed to examine gene and protein expression differences among four tissue layers of the Chinese milu deer antler: reserve mesenchyme (RM), precartilage (PC), transition zone (TZ), cartilage (CA). Overall, 4611 differentially expressed genes (DEGs) and 2388 differentially expressed proteins (DEPs) were identified in the transcriptome and proteome, respectively. Among the 828 DEGs common to both omics approaches, genes from the collagen, integrin, and solute carrier families, and signaling molecules were emphasized for their roles in the regulation of antler growth, development, and ossification. Bioinformatics analysis revealed that in addition to being regulated by vascular and nerve regeneration pathways, antler growth and development are significantly influenced by numerous cancer-related signaling pathways. This indicates that antler growth mechanisms may be similar to those of cancer cell proliferation and development. This study lays a foundation for future research on the mechanisms underlying the rapid growth and ossification of antlers.

Integrated immunogenomic analyses of high-grade serous ovarian cancer reveal vulnerability to combination immunotherapy

Background

The efficacy of immunotherapies in high-grade serous ovarian cancer (HGSOC) is limited, but clinical trials investigating the potential of combination immunotherapy including poly-ADP-ribose polymerase inhibitors (PARPis) are ongoing. Homologous recombination repair deficiency or BRCAness and the composition of the tumor microenvironment appear to play a critical role in determining the therapeutic response.

Methods

We conducted comprehensive immunogenomic analyses of HGSOC using data from several patient cohorts. Machine learning methods were used to develop a classification model for BRCAness from gene expression data. Integrated analysis of bulk and single-cell RNA sequencing data was used to delineate the tumor immune microenvironment and was validated by immunohistochemistry. The impact of PARPi and BRCA1 mutations on the activation of immune-related pathways was studied using ovarian cancer cell lines, RNA sequencing, and immunofluorescence analysis.

Results

We identified a 24-gene signature that predicts BRCAness. Comprehensive immunogenomic analyses across patient cohorts identified samples with BRCAness and high immune infiltration. Further characterization of these samples revealed increased infiltration of immunosuppressive cells, including tumor-associated macrophages expressing TREM2, C1QA, and LILRB4, as specified by single-cell RNA sequencing data and gene expression analysis of samples from patients receiving combination therapy with PARPi and anti-PD-1. Our findings show also that genomic instability and PARPi activated the cGAS-STING signaling pathway in vitro and the downstream innate immune response in a similar manner to HGSOC patients with BRCAness status. Finally, we have developed a web application (https://ovrseq.icbi.at) and an associated R package OvRSeq, which allow for comprehensive characterization of ovarian cancer patient samples and assessment of a vulnerability score that enables stratification of patients to predict response to the combination immunotherapy.

Conclusions

Genomic instability in HGSOC affects the tumor immune environment, and TAMs play a crucial role in modulating the immune response. Based on various datasets, we have developed a diagnostic application that uses RNA sequencing data not only to comprehensively characterize HGSOC but also to predict vulnerability and response to combination immunotherapy.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

CD2 glycoprotein and CD44 structure and prevention of diabetes nephropathy: Central characteristics of related genes based on WGCNA and PPI

Diabetic nephropathy (DN) is a prevalent complication of diabetes mellitus, characterized by complex pathogenesis that involves numerous molecules and signaling pathways. Among these, CD2 glycoprotein and CD44 play pivotal roles in cell adhesion, signal transduction, and inflammatory responses, potentially contributing significantly to the onset and progression of DN. This study aimed to investigate the central features of CD2 glycoprotein and CD44 in preventing diabetic nephropathy. To achieve this, kidney tissue sample data from DN patients were sourced from a public gene expression database. The roles of CD2 glycoprotein and CD44 within the PPI network were then analyzed, focusing on their interactions with other related genes. WGCNA analysis identified several significant gene modules associated with DN, including CD2 glycoprotein and CD44. PPI network analysis showed that these two proteins had a high degree of connectivity in the network, suggesting that they may be central regulatory molecules of DN. Further functional enrichment analysis revealed the potentially important role of CD2 glycoprotein and CD44 in diabetic nephropathy.

ITGB2 related to immune cell infiltration as a potential therapeutic target of inflammatory bowel disease using bioinformatics and functional research

Inflammatory bowel disease (IBD) is a chronic systemic inflammatory condition regarded as a major risk factor for colitis-associated cancer. However, the underlying mechanisms of IBD remain unclear. First, five GSE data sets available in GEO were used to perform 'batch correction' and Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). Candidate molecules were identified using CytoHubba, and their diagnostic effectiveness was predicted. The CIBERSORT algorithm evaluated the immune cell infiltration in the intestinal epithelial tissues of patients with IBD and controls. Immune cell infiltration in the IBD and control groups was determined using the least absolute shrinkage selection operator algorithm and Cox regression analysis. Finally, a total of 51 DEGs were screened, and nine hub genes were identified using CytoHubba and Cytoscape. GSE87466 and GSE193677 were used as extra data set to validate the expression of the nine hub genes. CD4-naïve T cells, gamma-delta T cells, M1 macrophages and resting dendritic cells (DCs) are the main immune cell infiltrates in patients with IBD. Signal transducer and activator of transcription 1, CCR5 and integrin subunit beta 2 (ITGB2) were significantly upregulated in the IBD mouse model, and suppression of ITGB2 expression alleviated IBD inflammation in mice. Additionally, the expression of ITGB2 was upregulated in IBD-associated colorectal cancer (CRC). The silence of ITGB2 suppressed cell proliferation and tumour growth in vitro and in vivo. ITGB2 resting DCs may provide a therapeutic strategy for IBD, and ITGB2 may be a potential diagnostic marker for IBD-associated CRC.

In Silico Approach to Molecular Profiling of the Transition from Ovarian Epithelial Cells to Low-Grade Serous Ovarian Tumors for Targeted Therapeutic Insights

This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

Prognostic value and potential molecular mechanism of ITGB superfamily members in hepatocellular carcinoma

We analyzed the prognostic value and potential molecular mechanisms of the members of integrin β (ITGB)superfamily in hepatocellular carcinoma (HCC) using data from The Cancer Genome Atlas (TCGA), cBioPortal, Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) HPA, Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER and Gene set enrichment analysis (GSEA) databases. ITGB4/5 mRNA was upregulated in HCC tissues in contrast to the normal liver tissues, whereas ITGB2/3/8 levels were lower in the former. ITGB4 was the most frequently mutated ITGB gene in HCC. Receiver operating characteristic curve (ROC) analysis showed that the expression levels of ITGB2/3/4/5/7/8 had significant diagnostic value in distinguishing HCC tissues from healthy liver tissues, ITGB8 had the highest diagnostic efficacy. The ITGB1/3/6/8 were also upregulated in the HCC tissues in contrast to healthy liver tissues. The expression of ITGB8 was verified by immunohistochemistry (IHC). Furthermore, ITGB6 and ITGB7 expression levels were strongly associated with the overall survival (OS) of HCC patients. The ITGB superfamily members exhibited homology and interactions in protein structure. In addition, ITGB6 together with ITGB7 were negatively related to the infiltration of multiple immune cell populations. GSEA results showed that ITGB6 was enriched in HCC migration and recurrence, whereas ITGB7 was significantly enriched in HIPPO, TOLL and JAK-STAT signaling pathways. In conclusion, ITGB6 and ITGB7 genes are possible to be prognostic biomarkers for HCC.