m7G regulator-mediated molecular subtypes and tumor microenvironment in kidney renal clear cell carcinoma

Nucleic acid and protein methylation modification in renal diseases

Although great efforts have been made to elucidate the pathological mechanisms of renal diseases and potential prevention and treatment targets that would allow us to retard kidney disease progression, we still lack specific and effective management methods. Epigenetic mechanisms are able to alter gene expression without requiring DNA mutations. Accumulating evidence suggests the critical roles of epigenetic events and processes in a variety of renal diseases, involving functionally relevant alterations in DNA methylation, histone methylation, RNA methylation, and expression of various non-coding RNAs. In this review, we highlight recent advances in the impact of methylation events (especially RNA m6A methylation, DNA methylation, and histone methylation) on renal disease progression, and their impact on treatments of renal diseases. We believe that a better understanding of methylation modification changes in kidneys may contribute to the development of novel strategies for the prevention and management of renal diseases.

Comprehensive analysis reveals dual biological function roles of EpCAM in kidney renal clear cell carcinoma

Background

Epithelial cell adhesion molecule (EpCAM), a well-established marker for circulating tumor cells, plays a crucial role in the complex process of cancer metastasis. The primary objective of this investigation is to study EpCAM expression in pan-cancer and elucidate its significance in the context of kidney renal clear cell carcinoma (KIRC).

Methods

Data obtained from the public database was harnessed for the comprehensive assessment of the EpCAM expression levels and prognostic and clinicopathological correlations in thirty-three types of cancer. EpCAM was validated in our own KIRC sequencing and immunohistochemical cohorts. Subsequently, an in-depth exploration was conducted to scrutinize the interrelationship between EpCAM and various facets, including immune cells, immune checkpoints, and chemotherapy drugs. We employed Cox regression analysis to identify prognostic immunomodulators associated with EpCAM, which were subsequently utilized in the development of a prognostic model. The model was validated in our own clinical cohort and public datasets, and compared with 137 published models. The role of EpCAM in KIRC was explored by biological function experiments in vitro.

Results

While EpCAM exhibited pronounced overexpression across a wide spectrum of cancer types, a notable reduction was observed in KIRC tissues. As grade increased, EpCAM expression decreased. EpCAM expression decreased in patients without metastasis. EpCAM mRNA and protein levels were used as independent, favorable prognostic factors in patients with KIRC in our own cohort. The expression of EpCAM exhibited strong associations with immune-related pathways, demonstrating an inverse correlation with the majority of immune cell types. Immune checkpoint inhibitors exert better therapeutic effects on patients with low EpCAM expression. In addition, EpCAM can be used as a drug resistance indicator and guide the clinical medication of patients with KIRC. A robust model, which had good predictive accuracy and applicability, showed significant superiority over other models. Importantly, EpCAM played the dual roles of promoting proliferation and resisting metastasis in KIRC.

Conclusion

In the context of KIRC, EpCAM assumes a surprising dual role, where it not only facilitates cell proliferation but also exerts resistance against the metastatic process. EpCAM serves as a standalone prognostic marker for patients with KIRC, and related models can also effectively predict prognosis. These discoveries offer novel perspectives on the functional significance of EpCAM in the context of KIRC.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine

Background

The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM).

Methods

The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment.

Results

The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/.

Conclusion

The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00305-1.