Exploiting ECM remodelling to promote immune-mediated tumour destruction

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs

BACKGROUND

Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management.

METHODS

Transcriptomic and clinical datasets for patients with BLCA were obtained from The Cancer Genome Atlas. Using multivariate Cox regression and least absolute shrinkage and selection operator techniques, a risk prognostic signature defined by DRLs was developed. The model's accuracy and prognostic relevance were assessed through Kaplan-Meier survival plots, receiver operating characteristic curves, concordance index, and principal component analysis. Functional and pathway enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, were conducted to elucidate the underlying biological processes. Immune cell infiltration was quantified using the CIBERSORT algorithm. Differences and functions of immune cells in different risk groups were evaluated through single-sample Gene Set Enrichment Analysis. The Tumor Immune Dysfunction and Exclusion predictor and tumor mutational burden (TMB) assessments were utilized to gauge the likelihood of response to immunotherapy. Drug sensitivity predictions were made using the Genomics of Drug Sensitivity in Cancer database.

RESULTS

A robust 8-DRL risk prognostic model, comprising LINC00513, SMARCA5-AS1, MIR4435-2HG, MIR4713HG, AL122035.1, AL359762.3, AC006160.1, and AL590428.1, was identified as an independent prognostic indicator. This model demonstrated strong predictive power for overall survival in patients with BLCA, revealing significant disparities between high- and low-risk groups regarding tumor microenvironment, immune infiltration, immune functions, TMB, Tumor Immune Dysfunction and Exclusion scores, and drug susceptibility.

CONCLUSION

This study introduces an innovative prognostic signature of 8 DRLs, offering a valuable prognostic tool and potential therapeutic targets for bladder carcinoma. The findings have significant implications for TMB, the immune landscape, and patient responsiveness to immunotherapy and targeted treatments.

Identification of extracellular matrix-related biomarkers in colon adenocarcinoma by bioinformatics and experimental validation

Backgrounds

Extracellular matrix (ECM) is an important component of tumor microenvironment, and its abnormal expression promotes tumor formation, progression and metastasis.

Methods

Weighted gene co-expression network analysis (WGCNA) was used to identify ECM-related hub genes based on The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) data. COAD clinical samples were used to verify the expression of potential biomarkers in tumor tissues, and siRNA was used to explore the role of potential biomarkers in cell proliferation and epithelial-mesenchymal transition (EMT).

Results

Three potential biomarkers (LEP, NGF and PCOLCE2) related to prognosis of COAD patients were identified and used to construct ERGPI. Immunohistochemical analysis of clinical samples showed that the three potential biomarkers were highly expressed in tumor tissues of COAD patients. Knockdown of LEP, NGF or PCOLCE2 inhibited COAD cell proliferation and EMT. Dictamnine inhibited tumor cell growth by binding to these three potential biomarkers based on molecular docking and transplanted tumor model.

Conclusion

The three biomarkers can provide new ideas for the diagnosis and targeted therapy of COAD patients.

Cancer-associated fibroblast expression of glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a prognostic marker in gastric cancer

Glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a rate-limiting enzyme in hexosamine biosynthesis involved in the occurrence and progress of many cancers. What role it plays in gastric cancer (GC) is still unclear. In this study, transcriptome sequencing data from the Harbin Medical University (HMU)-GC cohort and The Cancer Genome Atlas (TCGA) dataset were combined with the HMU-TCGA training cohort to analyze the biological function and clinical significance of GFPT2. The correlation of GFPT2 with immune cells and stromal cells was analyzed in the GC immune microenvironment through transcriptome sequencing data and a public single-cell sequencing database. In cell lines, GC tissues, and the tissue microarray, GFPT2 protein expression was confirmed by western blotting and immunohistochemistry. The mRNA of GFPT2 was highly expressed in the tumor (p < 0.001), and GC cells and tumors expressed high levels of GFPT2 protein. Compared to low expression, high GFPT2 mRNA expression was associated with higher levels of tumor invasion, higher pathological stages, and poor prognosis (p = 0.02) in GC patients. In a drug susceptibility analysis, GFPT2 mRNA expression was associated with multiple chemotherapeutic drug sensitivity, including docetaxel, paclitaxel, and cisplatin. Gene enrichment analysis found that GFPT2 was mainly primarily involved in the extracellular matrix receptor interaction pathway. The ESTIMATE, CIBERSORT, and ssGSEA algorithms showed that GFPT2 was associated with immune cell infiltration. In addition, GFPT2 was more likely to be expressed within cancer-associated fibroblasts (CAFs), and high levels of GFPT2 expression were highly correlated with four CAFs scores (all p < 0.05). Finally, a prognostic model to assess the risk of death in GC patients was constructed based on GFPT2 protein expression and lymph node metastasis rate. In conclusion, GFPT2 plays an essential role in the function of CAFs in GC. It can be used as a biomarker to assess GC prognosis and immune infiltration.

Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq

Background

PANoptosis, a cell death pathway involving pyroptosis, apoptosis, and necroptosis, is pivotal in the development of malignancy. However, in the field of breast cancer, the interaction between PANoptosis and tumor cells has not been thoroughly explored.

Methods

We downloaded breast cancer data and GSE176078 single-cell sequencing dataset from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain PANoptosis-associated genes. To construct prognostic models, COX and LASSO regression was used to identify PANoptosis-associated genes with prognostic value. Finally, immune infiltration analysis and differential analysis of biological functions were performed.

Results

Risk grouping was performed according to the prognostic model constructed by COX regression and LASSO regression. The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes. In addition, the lower the risk score, the higher the degree of microsatellite instability (MSI). Meanwhile, radixin (RDX), the gene with the highest hazard ratio (HR) value among PANoptosis prognosis-related genes, was explicitly expressed in artery Iendothelial cells (ECs) and was widely involved in signaling pathways such as immune response and cell proliferation, possessing rich biological functions.

Conclusion

We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of breast cancer patients. Furthermore, this study has led to a deeper understanding of the role of PANoptosis in breast cancer and has the potential to provide new directions for immunotherapy of breast cancer.

Light-responsive nanomedicine for cancer immunotherapy

Immunotherapy emerged as a paradigm shift in cancer treatments, which can effectively inhibit cancer progression by activating the immune system. Remarkable clinical outcomes have been achieved through recent advances in cancer immunotherapy, including checkpoint blockades, adoptive cellular therapy, cancer vaccine, and tumor microenvironment modulation. However, extending the application of immunotherapy in cancer patients has been limited by the low response rate and side effects such as autoimmune toxicities. With great progress being made in nanotechnology, nanomedicine has been exploited to overcome biological barriers for drug delivery. Given the spatiotemporal control, light-responsive nanomedicine is of great interest in designing precise modality for cancer immunotherapy. Herein, we summarized current research utilizing light-responsive nanoplatforms to enhance checkpoint blockade immunotherapy, facilitate targeted delivery of cancer vaccines, activate immune cell functions, and modulate tumor microenvironment. The clinical translation potential of those designs is highlighted and challenges for the next breakthrough in cancer immunotherapy are discussed.

High expression of COL6A1 predicts poor prognosis and response to immunotherapy in bladder cancer

Extracellular matrix (ECM), as an important framework for tumor microenvironment, plays important roles in many critical processes, including tumor growth, invasion, immune suppression, and drug resistance. However, few biomarkers of ECM-related genes (ERGs) have been developed for prognosis prediction and clinical treatment of bladder cancer (BC) patients. Bioinformatics analysis and LC-MS/MS analysis were used to screen differentially expressed ERGs in BC. Multivariate Cox regression analysis and Lasso regression analysis were used to construct and validate an ERGs-based prognostic prediction model for BC. Immunohistochemistry was used to detect the protein expression of hub gene-COL6A1 in BC patients. Using bioinformatics analysis from The Cancer Genome Atlas (TCGA) database and proteomic analysis from our BC cohort, we constructed and validated an effective prognostic prediction model for BC patients based on four differentially expressed ERGs (MAP1B, FBN1, COL6A1, and MFAP5). Moreover, we identified human collagen VI-COL6A1 was a hub gene in this prognostic prediction model and found that COL6A1 was closely related to malignancy progression, prognosis, and response to PD-1 inhibitor immunotherapy in BC. Our findings highlight the satisfactory predictive value of ECM-related prognostic models in BC and suggested that COL6A1 may be a potential biomarker in predicting malignant progression, prognosis, and efficacy of immunotherapy in BC.

Epigenetically programmed resistance to chemo- and immuno-therapies

Resistance to cancer treatments remains a major barrier in developing cancer cures. While promising combination chemotherapy treatments and novel immunotherapies have improved patient outcomes, resistance to these treatments remains poorly understood. New insights into the dysregulation of the epigenome show how it promotes tumor growth and resistance to therapy. By altering control of gene expression, tumor cells can evade immune cell recognition, ignore apoptotic cues, and reverse DNA damage induced by chemotherapies. In this chapter, we summarize the data on epigenetic remodeling during cancer progression and treatment that enable cancer cell survival and describe how these epigenetic changes are being targeted clinically to overcome resistance.

Reversing the PAI-1-induced fibrotic immune exclusion of solid tumor by multivalent CXCR4 antagonistic nano-permeator

Fibrosis is one of the key factors that lead to the immune exclusion of solid tumors. Although degradation of fiber is a promising strategy, its application was still bottlenecked by the side effects of causing metastasis, resulting in the failure of immunotherapy. Here, we developed an antimetastatic polymer (HPA) for the delivery of chemo-drug and antifibrotic siPAI-1 to form the nano-permeator. Nano-permeator shrank after protonation and deeply penetrated into the tumor core to down-regulate the expression of PAI-1 for antifibrosis, and further promoted the sustained infiltration and activation of T cells for killing tumor cells. Moreover, metastasis after fiber elimination was prevented by multivalent CXCR4 antagonistic HPA to reduce the attraction of CXCL12 secreted by distant organs. The administration of stroma-alleviated immunotherapy increased the infiltration of CD8⁺ T cells to 52.5% in tumor tissues, inhibiting nearly 90% metastasis by HPA in distant organs. The nano-permeator reveals the mechanism and correlation between antifibrosis and antimetastasis and was believed to be the optimizing immunotherapy for solid fibrotic tumors.

Glycoconjugate Nanoparticle-Based Systems in Cancer Immunotherapy: Novel Designs and Recent Updates

For many years, cell-surface glycans (in particular, Tumor-Associated Carbohydrate Antigens, TACAs) have been the target of both passive and active anticancer immunotherapeutic design. Recent advances in immunotherapy as a treatment for a variety of malignancies has revolutionized anti-tumor treatment regimens. Checkpoint inhibitors, Chimeric Antigen Receptor T-cells, Oncolytic virus therapy, monoclonal antibodies and vaccines have been developed and many approvals have led to remarkable outcomes in a subset of patients. However, many of these therapies are very selective for specific patient populations and hence the search for improved therapeutics and refinement of techniques for delivery are ongoing and fervent research areas. Most of these agents are directed at protein/peptide epitopes, but glycans-based targets are gaining in popularity, and a handful of approved immunotherapies owe their activity to oligosaccharide targets. In addition, nanotechnology and nanoparticle-derived systems can help improve the delivery of these agents to specific organs and cell types based on tumor-selective approaches. This review will first outline some of the historical beginnings of this research area and subsequently concentrate on the last 5 years of work. Based on the progress in therapeutic design, predictions can be made as to what the future holds for increasing the percentage of positive patient outcomes for optimized systems.