Combining single-cell sequencing data to construct a prognostic signature to predict survival, immune microenvironment, and immunotherapy response in gastric cancer patients

Exploring novel biomarkers and immunotherapeutic targets for biofeedback therapies to reveal the tumor-associated immune microenvironment through a multimetric analysis of kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) constitutes the primary subtype of renal cell carcinoma, representing 75% to 80% of cases and carrying a substantial cancer-specific mortality rate of up to 24%. Despite advancements in treatment options, KIRC displays notable resistance to conventional therapies, emphasizing the need for innovative targeted immunotherapeutic strategies. Chromatin regulators (CRs), pivotal proteins controlling gene expression and critical biological processes, play a crucial role in the initiation and progression of KIRC. This study employed a multi-omics approach to evaluate the impact of CR-associated genes on KIRC prognosis.

METHODS

The study utilized the TCGA-KIRC dataset and employed LASSO Cox regression to construct and validate a prognostic model that focuses on genes influencing KIRC prognosis. The research investigated interactions among gene characteristics, clinical parameters, the tumor microenvironment, targeted immunotherapy, and drug responsiveness. Experimental validation, encompassing various techniques such as cell culture, transient transfection, qPCR, Transwell assays, confirmed the robust predictive capability of the BRD9 gene.

RESULTS

The analysis identified the risk score of CRs as an independent factor determining KIRC prognosis. Furthermore, the study introduced a predictive Nomogram model that integrates clinical attributes and risk assessment. Significantly, BRD9 exhibited substantially elevated expression within KIRC cells, underscoring its role in driving cancer cell proliferation, invasion, and migration. These findings suggest the potential for tailored immunotherapy targeting BRD9 in the treatment of KIRC.

CONCLUSION

This study presents an innovative prognostic framework for KIRC based on multi-omics approaches, seamlessly incorporating CRs. This model holds promise for improving the accuracy of prognosis prediction for KIRC patients, laying a robust foundation for the development of targeted immunotherapies.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

Background

Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body's anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors.

Methods

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments.

Results

GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells.

Conclusion

This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.