CD93 orchestrates the tumor microenvironment and predicts the molecular subtype and therapy response of bladder cancer

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

CONTENT

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

SUMMARY AND OUTLOOK

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Current look at the most promising proteomic and glycomic biomarkers of bladder cancer

BACKGROUND

Bladder cancer (BC) belongs to the most frequent cancer types. The diagnostic process is still long and costly, with a high percentage of false-positive or -negative results. Due to the cost and lack of effectiveness, older methods need to be supplemented or replaced by a newer more reliable method. In this regard, proteins and glycoproteins pose high potential.

METHODS

We performed an online search in PubMed/Medline, Scopus, and Web of Science databases to find relevant studies published in English up until May 2023. If applicable, we set the AUC threshold to 0.90 and sensitivity/specificity (SN/SP) to 90%.

FINDINGS

Protein and glycoprotein biomarkers are a demonstrably viable option in BC diagnostics. Cholinesterase shows promise in progression-free survival. BLCA-4, ORM-1 along with HTRA1 in the detection of BC. Matrix metallopeptidase 9 exhibits potential for stratification of muscle-invasive subtypes with high negative predictive value for aggressive phenotypes. Distinguishing non-muscle invasive subtypes benefits from Keratin 17. Neu5Gc-modified UMOD glycoproteins pose potential in BC diagnosis, while fibronectin, laminin-5, collagen type IV, and lamprey immunity protein in early detection of BC.

Disulfidptosis characterizes the tumor microenvironment and predicts immunotherapy sensitivity and prognosis in bladder cancer

Background

Bladder cancer (BLCA) is prone to metastasis and has poor prognosis with unsatisfactory treatment responsiveness. Disulfidptosis is a recently discovered, novel mode of cell death that is closely associated with human cancers. However, a comprehensive analysis of the relationship between disulfidptosis and BLCA is lacking. Therefore, this study aimed to explore the potential effect of disulfidptosis on BLCA and identify a biomarker for evaluating the prognosis and immunotherapy of patients with BLCA.

Material and methods

We acquired BLCA RNA sequencing data from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) cohort (containing 19 normal samples and 409 tumor samples) and the GES39281 cohort (containing 94 tumor samples) which were used for external validation of the signature. Initially, we performed unsupervised consensus clustering to explore disulfidptosis-related subgroups. We then conducted functional enrichment analysis on these subgroups to gain insights into their biological significance and evaluate their immunotherapy response and chemotherapy sensitivity. Next, we conducted Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression to construct a prognostic signature in the TCGA training set for prognosis-related differentially expressed genes (DEGs) in the disulfidptosis-related subgroups. Subsequently, we used a receiver operating characteristic (ROC) curve and independent prognostic analysis to validate the predictive performance of the signature in the TCGA testing and the GES39281 cohorts. Finally, we explored the therapeutic value of this signature in patients with BLCA, in terms of immunotherapy and chemotherapy.

Result

In this study, we obtained two subgroups: DRG-high (238 samples) and DRG-low (160 samples). The DRG-high group exhibited a poor survival rate compared to the DRG-low group and had a significant association with tumor grade, stage, and metastasis. Additionally, several pathways related to cancer and the immune system were enriched in the high-DRG group. Moreover, the DRG-high group exhibited higher expression of PD1 and CTLA4 and had a better response to immunotherapy in patients with both PD1 and CTLA4 positivity. Conversely, the DRG-high group was more sensitive to common chemotherapeutic agents. A prognostic signature was created, consisting of COL5A1, DIRAS3, NKG7, and POLR3G and validated as having a robust predictive capability. Patients in the low-risk-score group had more immune cells associated with tumor suppression and better immunotherapy outcomes.

Conclusion

This study contributes to our understanding of the characteristics of disulfidptosis-related subgroups in BLCA. Disulfidptosis-related signatures can be used to assess the prognosis and immunotherapy of patients with BLCA.

The analysis of multiple omics and examination of pathological images revealed the prognostic and therapeutic significances of CD93 in lung squamous cell carcinoma

As a potent pro-angiogenic factor, the role of CD93 in the prognosis and therapeutic outcomes of lung squamous cell carcinoma (LUSC) merits exploration. In this study, we systematically collected transcriptomic, genomic, and clinical data from various public databases, as well as pathological images from hospital-operated patients. Employing statistical analysis software like R (Version 4.2.2) and GraphPad (Version 8.0), we conducted comprehensive analyses of multi-omics data. The results revealed elevated CD93 expression in LUSC tissues, closely associated with various cancer-related pathways. High CD93 expression indicated advanced clinical stage and poorer prognosis. Furthermore, CD93 contributed to resistance against chemotherapy and immunotherapy by enhancing tumor cell stemness, reducing immune cell infiltration, and inducing T cell exhaustion. Patients with low CD93 expression exhibited higher response rates to both chemotherapy and immunotherapy. Immunohistochemistry validated the significance of CD93 in LUSC. CD93 emerges as a biomarker signaling unfavorable prognosis and influencing therapeutic outcomes, suggesting a potential LUSC treatment avenue.

Integrating novel immunotherapeutic approaches in organ-preserving therapies for bladder cancer

Bladder cancer (BC) is a prevalent malignancy with significant morbidity and mortality. Over the years, the landscape of bladder cancer treatment has witnessed notable advancements, particularly in the realm of immunotherapy. Immunotherapy has emerged as a promising adjunct to organ-preserving approaches, harnessing the immune system's potential to target and eliminate cancer cells. Organ preservation strategies offer viable alternatives to radical cystectomy to avoid the morbidities associated with radical surgery, as well as to respond to the needs of patients unfit for or who have refused surgery. However, the challenge lies in achieving durable disease control while minimizing treatment-related toxicities. This review highlights the significance of immune checkpoint inhibitors, such as anti-programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) antibodies, in the treatment of localized bladder cancer. The clinical efficacy of immune checkpoint inhibitors, as both neoadjuvant and adjuvant therapies in combination with radiation or chemotherapy, is discussed. Moreover, the potential of immunotherapies beyond immune checkpoint inhibition, including combinations with bacillus Calmette-Guérin (BCG) instillations and/or investigational gene therapies, is explored. Furthermore, the predictive value of the tumour immune microenvironment for the success of these strategies is examined. Understanding the complex interplay between tumour immunity and therapeutic interventions can aid in identifying predictive biomarkers and tailoring personalized treatment strategies. Further research and clinical trials are warranted to optimize the use of immunotherapy in conjunction with organ-preserving therapies, potentially leading to enhanced patient outcomes and quality of life.

Prognostic analysis and validation of lncRNAs in bladder cancer on the basis of neutrophil extracellular traps

BACKGROUND

Complex interactions in the tumor microenvironment (TME) between bladder cancer (BLCA) and immune cells are critical for cancer progression. However, studies of neutrophil extracellular trap-associated long non-coding RNAs (NET-lncRNAs) in the TME of BLCA have not been reported. This study aims to screen for NET-lncRNAs in BLCA and to preliminarily explore their effects on BLCA development.

METHODS

The correlation of NET-related gene sets, which were identified from the cancer genome atlas (TCGA) BLCA datasets, with lncRNAs was analyzed and the prognosis-related genes were identified through random forest analysis. The least absolute shrinkage and selection operator (LASSO) model was utilized to obtain prognostic risk scores for NET-lncRNAs (NET-Score). We collected clinical BLCA samples, as well as SV-HUC-1 and BLCA cells, to validate the expression of NET-lncRNAs. Survival and independent prognostic analysis were performed. In J82 and UM-UC-3 cells, after NKILA expression was inhibited, cell proliferation and apoptosis levels were detected.

RESULTS

NET-related gene sets mainly included CREB5, MMP9, PADI4, CRISPLD2, CD93, DYSF, MAPK3, TECPR2, MAPK1 and PIK3CA. Then, four NET-lncRNAs, MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were identified. NET-Score had the highest hazard ratio for BLCA. An elevated NET-Score was linked to a significant increase in immune cell infiltration and copy number variation, as well as a notable decrease in survival rate and drug sensitivity. NET-lncRNA-related genes were mainly enriched in the pathways of angiogenesis, immune response, cell cycle and T cell activation. MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1 expressions were significantly increased in BLCA tissues. Compared with SV-HUC-1 cells, NKILA expression was elevated in J82 and UM-UC-3 cells. Inhibition of NKILA expression inhibited the proliferation and promoted apoptosis of J82 and UM-UC-3 cells.

CONCLUSIONS

Several NET-lncRNAs, including MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were successfully screened in the BLCA. The NET-Score was an independent prognostic factor for BLCA. In addition, inhibition of NKILA expression suppressed BLCA cell development. The above NET-lncRNAs could serve as potential prognostic markers and targets in BLCA.

Molecular classification of urothelial bladder carcinoma

Urothelial bladder carcinoma (UC) ranks among the top ten most commonly diagnosed cancers worldwide on an annual basis. The standardized classification system for urothelial bladder tumors is the Tumor, Node, Metastasis classification, which reflects differences between non-muscle-invasive bladder carcinoma (NMIBC) and muscle-invasive bladder carcinoma (MIBC) and it depends on the extent to which tumor has infiltrated the bladder wall and other tissues and organs. NMIBC and MIBC exhibit great intrinsic heterogeneity regarding different prognoses, survival, progression, and treatment outcomes. In recent years, studies based on mRNA expression profiling revealed the existence of biologically relevant molecular subtypes of UC, which show variant molecular features that can provide more precise stratification of UC patients. Here, we present a complex classification of UC based on mRNA expression studies and molecular subtypes of NMIBC and MIBC in detail with regard to different mRNA expression profiles, mutational signatures, and infiltration by non-tumor cells. The possible impact of molecular subtyping on treatment decisions and patients' outcomes is outlined, too.

Integrated transcriptomics and proteomics data analysis identifies CDH17 as a key cell surface target in colorectal cancer

Immunotherapy development against colorectal cancer (CRC) is hindered by the lack of cell surface target highly expressed in cancer cells but with restricted presence in normal tissues to minimize off-tumor toxicities. In this in silico analysis, a longlist of genes (n = 13,488) expressed in CRCs according to the Human Protein Atlas (HPA) database were evaluated to shortlist for potential surface targets based on the following prerequisites: (i) Absent from the brain and lung tissues to minimize the likelihood of neurologic and pulmonary toxicities; (ii) Restricted expression profile in other normal human tissues; (iii) Genes that potentially encode cell surface proteins and; (iv) At least moderately expressed in CRC cases. Fifteen potential targets were shortlisted and subsequently ranked according to the combination of their transcript and protein expression levels in CRCs derived from multiple datasets (i.e. DepMap, TCGA, CPTAC-2, and HPA CRCs). The top-ranked target with the highest and homogenous expression in CRCs was cadherin 17 (CDH17). Downstream analysis of CRC transcriptomics and proteomics datasets showed that CDH17 was significantly correlated with carcinoembryonic antigen expression. Moreover, CDH17 expression was significantly lower in CRC cases with high microsatellite instability, as well as negatively associated with immune response gene sets and the expression of MHC class I and II molecules. CDH17 represents an optimal target for therapeutic development against CRCs, and this study provides a novel framework to identify key cell surface targets for therapeutic development against other malignancies.

On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks

In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested-modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.

An immune-related gene signature for the prognosis of human bladder cancer based on WGCNA

The innovation of immunotherapy was a milestone in the treatment of bladder cancer (BLCA). However, the treatment benefits varied by individual thus promoting the investigation of the biomarker of the patients. Unfortunately, there were not many effective predictive models, which were desired by clinicians, for BLCA that can predict the prognosis and benefit of immunotherapy. We constructed a three genes prognosis prediction model termed RiskScore based on the result of weighted correlation network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) cohort (n = 406). We then validated the prediction accuracy with three validation cohort(GSE13507 (n = 165), GSE48075(n = 73), GSE32894(n = 224)). We compared the differences in gene expression, immune relate function, and immune infiltration between two groups divided by RiskScore. We further discovered the potential drug target and suitable compounds for high-risk groups. Our results suggested that the low-risk group may be more potential for immunotherapy for they have higher B cell infiltration, higher expression of immune checkpoints(PDCD1, CTLA4), and much more active immune-related pathways(B cell and T cell receptor signaling pathway). The RiskScore showed a well predictive accuracy for the prognosis of BLCA. After Spearman analysis, we found the suitable drug target and compounds for the patients in the high-risk group. The model we constructed is able to predict the prognosis of BLCA patients with ease and accuracy. PLK1 and gefitinib may be utilized for further treatment of BLCA patients.