Identification and Validation of a m5C RNA Modification-Related Gene Signature for Predicting Prognosis and Immunotherapeutic Efficiency of Gastric Cancer

Significant roles of RNA 5-methylcytosine methylation in cancer

Cancer stands as a leading cause of mortality and poses an escalating threat to global health. Epigenetic dysregulation is pivotal in the onset and advancement of cancer. Recent research on RNA 5-methylcytosine (m5C) methylation has underscored its significant role in cancer. RNA m5C methylation is a key component in gene expression regulation and is intricately linked to cancer development, offering valuable insights for cancer diagnosis, treatment, and prognosis. This review provides an in-depth examination of the three types of regulators associated with RNA m5C methylation and their biological functions. It further investigates the expression and impact of RNA m5C methylation and its regulators in cancer, focusing on their mechanisms in cancer progression and clinical relevance. The current research on inhibitors targeting RNA m5C methylation-related regulators remains underdeveloped, necessitating further exploration and discovery.

Molecular subtype identification and prognosis stratification based on golgi apparatus-related genes in head and neck squamous cell carcinoma

BACKGROUND

Abnormal dynamics of the Golgi apparatus reshape the tumor microenvironment and immune landscape, playing a crucial role in the prognosis and treatment response of cancer. This study aims to investigate the potential role of Golgi apparatus-related genes (GARGs) in the heterogeneity and prognosis of head and neck squamous cell carcinoma (HNSCC).

METHODS

Transcriptional data and corresponding clinical information of HNSCC were obtained from public databases for differential expression analysis, consensus clustering, survival analysis, immune infiltration analysis, immune therapy response assessment, gene set enrichment analysis, and drug sensitivity analysis. Multiple machine learning algorithms were employed to construct a prognostic model based on GARGs. A nomogram was used to integrate and visualize the multi-gene model with clinical pathological features.

RESULTS

A total of 321 GARGs that were differentially expressed were identified, out of which 69 were associated with the prognosis of HNSCC. Based on these prognostic genes, two molecular subtypes of HNSCC were identified, which showed significant differences in prognosis. Additionally, a risk signature consisting of 28 GARGs was constructed and demonstrated good performance for assessing the prognosis of HNSCC. This signature divided HNSCC into the high-risk and low-risk groups with significant differences in multiple clinicopathological characteristics, including survival outcome, grade, T stage, chemotherapy. Immune response-related pathways were significantly activated in the high-risk group with better prognosis. There were significant differences in chemotherapy drug sensitivity and immune therapy response between the high-risk and low-risk groups, with the low-risk group being more suitable for receiving immunotherapy. Riskscore, age, grade, and radiotherapy were independent prognostic factors for HNSCC and were used to construct a nomogram, which had good clinical applicability.

CONCLUSIONS

We successfully identified molecular subtypes and prognostic signature of HNSCC that are derived from GARGs, which can be used for the assessment of HNSCC prognosis and treatment responses.

Gastric Cancer Signaling Pathways and Therapeutic Applications

Gastric cancer (GC) is a prevalent malignant tumor and ranks as the second leading cause of death among cancer patients worldwide. Due to its hidden nature and difficulty in detection, GC has a high incidence and poor prognosis. Traditional treatment methods such as systemic chemotherapy, radiotherapy, and surgical resection are commonly used, but they often fail to achieve satisfactory curative effects, resulting in a very low 5-year survival rate for GC patients. Currently, targeted therapy and immunotherapy are prominent areas of research both domestically and internationally. These methods hold promise for the treatment of GC. This article focuses on the signaling pathways associated with the development of GC, as well as the recent advancements and applications of targeted therapy and immunotherapy. The aim is to provide fresh insights for the clinical treatment of GC.

Comprehensive analysis of disulfidptosis related genes and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is a common malignant tumor of the digestive system. Disulfidptosis is a new programmed cell death mechanism, although its specific mechanism in GC is incompletely understood.

AIM

In this study, we used bioinformatics analysis to explore a disulfidptosis-based predictive model related to GC prognosis and to identify potential therapeutic targets and sensitive drugs for GC.

METHODS

We extracted GC-related data from The Cancer Genome Atlas and Gene Expression Omnibus databases. R software (version 4.2.1) was used for correlation analysis.

RESULTS

Through the above analysis, we found that the disulfidptosis related gene may be related to the prognosis of GC. Six genes, namely, PLS3, GRP, APOD, SGCE, COL8A1, and VAMP7, were found to constitute a predictive model for GC prognosis. APOD is a potential therapeutic target for treating GC. Bosutinib and other drugs are sensitive for the treatment of GC.

CONCLUSION

The results of this study indicate that disulfidptosis is related to the prognosis and treatment of GC, while APOD represents a potential therapeutic target for GC.