Construction and validation of an m6A RNA methylation regulator prognostic model for early-stage clear cell renal cell carcinoma

A novel m6A/m5C/m1A/m7G-related classification and risk signature predicts prognosis and reveals immunotherapy inclination in gastric cancer

Background

Gastric cancer (GC) is characterized by high morbidity and mortality rates, and the prognosis is not optimistic. Therefore, the search for new biomarkers is crucial. Methylation modifications in RNA modifications play a crucial role in tumors. However, the role of methylation modification of integrated m6A/m5C/m1A/m7G, in GC and its related analysis have not been reported. It still needs to be studied in depth. Our study aims to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provide new strategies for GC treatment.

Methods

We used TCGA-STAD (The Cancer Genome Atlas-Stomach Adenocarcinoma) as a training set and GSE84433 as a validation set to analyze and determine potential associations between m6A/m5C/m1A/m7G-related genes and clinical risk of GC. In addition, we explored the prognostic value and potential biological mechanisms of m6A/m5C/m1A/m7G-related genes in GC through consistent clustering, differential expression gene identification, enrichment analysis, and immune infiltration analysis. Finally, we constructed m6A/m5C/m1A/m7G-related risk signature (MRRS) to evaluate the correlation between risk grade and survival prognosis, drug sensitivity, and immune infiltration, and validated the validity by immunohistochemical staining.

Results

We identified subgroups of C1, C2, and C3 patients by consensus clustering using data from 45 m6A/m5C/m1A/m7G-related genes. The three groups showed significant differences in survival, immune scores, and immune cell infiltration. We then constructed MRRS using least absolute shrinkage and selection operator (LASSO) regression analysis, including SLC5A6, FKBP10, GPC3, and GGH, which could accurately differentiate between high-/low-risk populations. Its accuracy was further validated in the validation set and immunohistochemical staining. These results suggest that m6A/m5C/m1A/m7G are closely related to the GC tumor immune microenvironment, and MRRS has good performance in predicting the survival of GC patients.

Conclusions

In this study, we highlighted the association of m6A/m5C/m1A/m7G subtypes with changes in the GC immunotumor microenvironment. We constructed and validated MRRS, which is valuable in predicting survival, immune infiltration and drug sensitivity in GC patients. This helps to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provides new strategies for GC treatment.

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

Unveiling the multi-target compounds of Rhazya stricta: Discovery and inhibition of novel target genes for the treatment of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a prevalent kidney malignancy with a pressing need for innovative therapeutic strategies. In this context, emerging research has focused on exploring the medicinal potential of plants such as Rhazya stricta. Nevertheless, the complex molecular mechanisms underlying its potential therapeutic efficacy remain largely elusive. Our study employed an integrative approach comprising data mining,network pharmacology,tissue cell type analysis, and molecular modelling approaches to identify potent phytochemicals from R. stricta, with potential relevance for ccRCC treatments. Initially, we collected data on R. stricta's phytochemical from public databases. Subsequently, we integrated this information with differentially expressed genes (DEGs) in ccRCC, which were derived from microarray datasets(GSE16441,GSE66270, and GSE76351). We identified potential intersections between R. stricta and ccRCC targets, which enabled us to construct a compound-genes-pathway network using Cytoscape software. This helped illuminate R. stricta's multi-target pharmacological effects on ccRCC. Moreover, tissue cell type analysis added another layer of insight into the cellular specificity of potential therapeutic targets in the kidney. Through further Kaplan-Meier survival analysis, we pinpointed MMP9,ACE,ERBB2, and HSP90AA1 as prospective diagnostic and prognostic biomarkers for ccRCC. Notably, our study underscores the potential of R. stricta derived compounds-namely quebrachamine,corynan-17-ol, stemmadenine,strictanol,rhazinilam, and rhazimolare-to impede ccRCC progression by modulating the activity of MMP9,ACE,ERBB2, and HSP90AA1 genes. Further, molecular docking and dynamic simulations confirmed the plausible binding affinities of these compounds. Despite these promising findings, we recognize the need for comprehensive in vivo and in vitro studies to further investigate the pharmacokinetics and biosafety profiles of these compounds.

Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients

BACKGROUND

Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators (CRs) can regulate the occurrence and development of cancer. This article investigates the functional and prognostic value of CRs in renal carcinoma patients.

METHODS

mRNA expression and clinical information were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between risk groups.

RESULTS

We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients. We not only successfully validated its feasibility, but also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signalling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints. At the same time, the genomics of drug sensitivity in cancer database was used to analyze the commonly used drugs of renal clear cell carcinoma patients. It was found that patients in the low-risk group were sensitive to medicines such as axitinib, pazopanib, sorafenib, and gemcitabine. In contrast, those in the high-risk group may be sensitive to sunitinib.

CONCLUSION

The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.