m7G-Associated subtypes, tumor microenvironment, and validation of prognostic signature in lung adenocarcinoma

Tumor-associated characteristics and immune dysregulation in nasopharyngeal carcinoma under the regulation of m7G-related tumor microenvironment cells

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a type of malignant tumor with high morbidity. Aberrant levels of N7-methylguanosine (m7G) are closely associated with tumor progression. However, the characteristics of the tumor microenvironment (TME) in NPC associated with m7G modification remain unclear.

METHODS

A total of 68,795 single cells from single-cell RNA sequencing data derived from 11 NPC tumor samples and 3 nasopharyngeal lymphatic hyperplasia (NLH) samples were clustered using a nonnegative matrix factorization algorithm according to 61 m7G RNA modification regulators.

RESULTS

The m7G regulators were found differential expression in the TME cells of NPC, and most m7G-related immune cell clusters in NPC tissues had a higher abundance compared to non-NPC tissues. Specifically, m7G scores in the CD4+ and CD8+ T cell clusters were significantly lower in NPC than in NLH. T cell clusters differentially expressed immune co-stimulators and co-inhibitors. Macrophage clusters differentially expressed EIF4A1, and high EIF4A1 expression was associated with poor survival in patients with head and neck squamous carcinoma. EIF4A1 was upregulated in NPC tissues compared to the non-NPC tissues and mainly expressed in CD86+ macrophages. Moreover, B cell clusters exhibited tumor biological characteristics under the regulation of m7G-related genes in NPC. The fibroblast clusters interacted with the above immune cell clusters and enriched tumor biological pathways, such as FGER2 signaling pathway. Importantly, there were correlations and interactions through various ligand-receptor links among epithelial cells and m7G-related TME cell clusters.

CONCLUSION

Our study revealed tumor-associated characteristics and immune dysregulation in the NPC microenvironment under the regulation of m7G-related TME cells. These results demonstrated the underlying regulatory roles of m7G in NPC.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Integrative analyses reveal biological function and prognostic role of m7G methylation regulators in high-grade glioma

Based on 29 m7G regulators, glioma patients were categorized into three groups using data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. Distinct characteristics were observed in immune cell infiltration, functional enrichment, and clinical prognosis for every glioma subtype. Analyzing the differentially expressed genes (DEGs) confirmed the distinction among the three m7G clusters. A predictive tool for overall survival (OS) in high-grade glioma patients was developed and confirmed, consisting of 13 m7G regulators forming a prognostic signature. Elevated m7G levels were found to be associated with increased tumor mutation burden and immune activation, indicating a tumor microenvironment characterized by inflammation and a lower overall survival rate. In contrast, reduced m7G scores were linked to a deficiency in immune infiltration, a low burden of mutations, and a non-inflamed phenotype, suggesting a more positive clinical outlook. Additionally, the m7G risk scores were found to impact chemotherapy sensitivity. The m7G predictive pattern shows potential as a marker for the overall survival of patients with high-grade glioma. By significantly improving our comprehension of the functional role of m7G regulators in the advancement of glioma and their impact on clinical results, this study offers valuable perspectives for precision therapy in the management of high-grade glioma.

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.

Bioinformatics Analysis of the Prognostic Significance of CAND1 in ERα-Positive Breast Cancer

The identification of novel prognostic biomarkers for breast cancer is an unmet clinical need. Cullin-associated and neddylation-dissociated 1 (CAND1) has been implicated in mediating carcinogenesis in prostate and lung cancers. In addition, CAND1 is an established prognostic biomarker for worse prognosis in liver cancer. However, the prognostic significance of CAND1 in breast cancer has not yet been explored. In this study, Breast Cancer Gene-Expression Miner (Bc-GenExMiner) and TIMER2.0 were utilized to explore the mRNA expression of CAND1 in ERα-positive breast cancer patients. The Kaplan–Meier plotter was used to explore the relationship between CAND1 expression and several prognostic indicators. The Gene Set Cancer Analysis (GSCA) web server was then used to explore the pathways of the genes that correlate with CAND1 in ERα-positive breast cancer. Immune infiltration was investigated using Bc-GenExMiner. Our bioinformatics analysis illustrates that breast cancer patients have higher CAND1 compared to normal breast tissue and that ERα-positive breast cancer patients with a high expression of CAND1 have poor overall survival (OS), distant metastasis-free survival (DMFS), and relapse-free survival (RFS) outcomes. Higher CAND1 expression was observed in histologic grade 3 compared to grades 2 and 1. Our results revealed that CAND1 positively correlates with lymph nodes and negatively correlates with the infiltration of immune cells, which is in agreement with published reports. Our findings suggest that CAND1 might mediate invasion and metastasis in ERα-positive breast cancer, possibly through the activation of estrogen and androgen signaling pathways; however, experiments should be carried out to further explore the role of CAND1 in activating the androgen and estrogen signaling pathways. In conclusion, the results suggest that CAND1 could be used as a potential novel biomarker for worse prognosis in ERα-positive breast cancer.