Construction of a Novel Prognostic Model in Lung Adenocarcinoma Based on 7-Methylguanosine-Related Gene Signatures

RNA m7G methylation regulators and targets significantly contribute to chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is one of the most common lung injury diseases, closely associated with aging, air pollution and smoking exposure. The novel epigenetic modification 7-methylguanosine (m7G) RNA methylation affects the pathogenesis and progression of COPD. In this study, the combined roles of m7G methylation regulators were explored in COPD for the first time by integrated bioinformatic methods. The machine algorithms screened 7 disease signature genes relevant to clinical indicators, including CYFIP2, EIF3D, EIF4G3, GEMIN5, METTL1, SNUPN and NCBP2, and METTL1 was related to the progression in COPD. COPD patients could be well divided into two m7G subtypes by consensus clustering, and the two groups had differential immune profiles, visualized by single-cell sequencing and immune infiltration landscapes. More importantly, CAT was found to be a meaningful key target gene in METTL1-CAT axis for m7G methylation in COPD. We also used the cell premature senescence model for the preliminary validation of the above biosignature analysis results. The qRT-PCR and GSEA results revealed the important regulatory roles of the seven disease signature genes in COPD and aging-related diseases. Taken together, METTL1 and its target CAT have played an important role in COPD, as excellent candidates for its prevention and intervention.

Expression Profile and Prognostic Significance of Pivotal Regulators for N7-Methylguanosine Methylation in Diffuse Large B-Cell Lymphoma

N7-methylguanosine (m7G) occurs by adding a methyl group to the N7 atom of the RNA guanine. Emerging evidence suggests that m7G modification has emerged as a crucial regulator of tumorigenesis, progression, invasion, and metastasis in multiple cancers. Nevertheless, the utility of m7G modification in diffuse large B-cell lymphoma (DLBCL) remains undefined, notably the interaction with the tumor microenvironment (TME). Here, we aimed to identify the expression profile of m7G regulators in DLBCL, construct a novel risk model, and explore their connection with TME. We initially investigated the difference and correlation in m7G regulators' expression in normal and tumor groups, classified patients by consistent clustering analysis, investigated the functional and prognostic significance of the resulting subtypes, and identified prognosis-associated genes by one-way Cox and least absolute shrinkage and selection operator (LASSO) regression calculations, and constructed a risk model. Further analysis showed that correlation among immune cell infiltration with m7G risk score and determined that it impacts the prognosis of DLBCL patients. Our research demonstrated the relevance of m7G regulators to DLBCL prognosis, providing theoretical support for precise prognostic stratification and immunotherapeutic assessment in DLBCL.

M7G-related tumor immunity: novel insights of RNA modification and potential therapeutic targets

RNA modifications play a pivotal role in regulating cellular biology by exerting influence over distribution features and molecular functions at the post-transcriptional level. Among these modifications, N7-methylguanosine (m7G) stands out as one of the most prevalent. Over recent years, significant attention has been directed towards understanding the implications of m7G modification. This modification is present in diverse RNA molecules, including transfer RNAs, messenger RNAs, ribosomal RNAs, and other noncoding RNAs. Its regulation occurs through a series of specific methyltransferases and m7G-binding proteins. Notably, m7G modification has been implicated in various diseases, prominently across multiple cancer types. Earlier studies have elucidated the significance of m7G modification in the context of immune biology regulation within the tumor microenvironment. This comprehensive review culminates in a synthesis of findings related to the modulation of immune cells infiltration, encompassing T cells, B cells, and various innate immune cells, all orchestrated by m7G modification. Furthermore, the interplay between m7G modification and its regulatory proteins can profoundly affect the efficacy of diverse adjuvant therapeutics, thereby potentially serving as a pivotal biomarker and therapeutic target for combinatory interventions in diverse cancer types.

A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

Genes whose expressions in the primary lung squamous cell carcinoma are able to accurately predict the progression of metastasis through lymphatic system, inferred from a bioinformatics analyses

Lymph node metastasis is the most important prognostic factor in patients with lung squamous cell carcinoma. The current findings show that lymph node metastatic tumor cells can arise by programming metastasis in primary tumor cells. Thereby, the genetic alterations responsible for the metastasis could be detected in the primary tumors. This bioinformatic study aimed to determine novel potential prognostic biomarkers shared between primary lung squamous cell tumors (without lymph node metastasis) and lymphatic metastasis, using the Cancer Genome Atlas database. Differentially expressed genes were screened by limma statistical package in R environment. Gene ontology and biological pathways analyses were performed using Enrichr for up-regulated and down-regulated genes. Also, we selected lymph node metastasis related genes among DEGs using correlation analysis between DEGs and suitable references genes for metastasis. Receiver operating characteristic curves was applied using pROC and R package ggplot2 to evaluate diagnostic value of differentially expressed genes. In addition, survival and drug resistance analyses were performed for differentially expressed genes. The miRNA-mRNA interaction networks were predicted by miRwalk and TargetScan databases and expression levels analysis of the miRNAs which were mainly targeting mRNAs was performed using UALCAN database. Protein-protein interaction network analysis and hub genes identification were performed using FunRich and Cytoscape plugin cytoHubba. In this study, a total of 397 genes were differentially expressed not only with a significant difference between N + vs. normal and N0 vs. normal but also with significant difference between N + vs. N0. Identified GO terms and biological pathways were consistent with DEGs role in the lung squamous cell carcinoma and lymph node metastasis. A significant correlation between 56 genes out of 397 differentially expressed genes with reference genes prompted them being considered for identifying lymph node metastasis of lung squamous cell carcinoma. In addition, SLC46A2, ZNF367, AC107214.1 and NCBP1 genes were identified as survival-related genes of patients with lung squamous cell carcinoma. Moreover, NEDD9, MRPL21, SNRPF, and SCLT1 genes were identified to be involved in lung squamous cell carcinoma drug sensitivity/resistance. We have identified several numbers of miRNAs and their related target genes which could emerge as potential diagnostic biomarkers. Finally, CDK1, PLK1, PCNA, ZWINT and NDC80 identified as hub genes for underlying molecular mechanisms of lung squamous cell carcinoma and lymphatic metastasis. Our study highlights new target genes according to their relation to lymph node metastasis, whose expressions in the primary lung squamous cell carcinoma are able to accurately assess the presence of lymphatic metastasis.

A cuproptosis-related lncRNA signature to predict prognosis and immune microenvironment of colon adenocarcinoma

Cuproptosis is a novel cell death modality but its regulatory role in the colon cancer remains obscure. This study is committed to establishing a cuproptosis-related lncRNA (CRL) signature to forecast the prognosis for colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) samples were randomly divided into training and validation cohorts. LASSO-COX analysis was performed to construct a prognostic signature consisting of five CRLs (AC015712.2, ZEB1-AS1, SNHG26, AP001619.1, and ZKSCAN2-DT). We found the patients with high-risk scores suffered from poor prognosis in training cohort (p < 0.001) and validation cohort (p = 0.004). Nomogram was created based on the 5-CRL signature. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) demonstrated the nomogram performed well in 1‑, 3‑, and 5‑year overall survival (OS). Subsequently, we observed increased infiltration of multiple immune cells and upregulated expression of immune checkpoints and RNA methylation modification genes in high-risk patients. Additionally, gene set enrichment analysis (GSEA) revealed two tumor-related pathways, including MAPK and Wnt signaling pathways. Finally, we found AKT inhibitors, all-trans retinoic acid (ATRA), camptothecin, and thapsigargin had more sensitivity to antitumor therapy in high-risk patients. Collectively, this CRL signature is promising for the prognostic prediction and precise therapy of COAD.