A novel defined m7G regulator signature to investigate the association between molecular characterization and clinical significance in lung adenocarcinoma

Abstract

BackgroundAbout170 chemical modifications to RNAs have been identified, which significantly affect gene expression. Dysregulation of RNA modifications induced by abnormal expression or mutations in RNA modifiers might result in cancer. The most frequent RNA modifications are N6-methyladenosine (m6A), 5-methylcytosine (m5C), and N7-methylguanosine (m7G). Lung cancer is the leading cause of cancer-related deaths globally. The present study aimed to investigate whether the expression of the m7G-related genes is linked to lung cancer cases with lung adenocarcinoma (LUAD), which accounts for about 40% of lung cancer cases.MethodsA total of 12 m7G-related differentially expressed genes (DEGs) were identified in LUAD patients by The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression method was used to build a four-gene risk model. Then, LUAD patients in the TCGA cohort were divided into low- and high-risk groups based on their risk scores for subsequent molecular and clinical research.ResultsCompared to the low-risk group, the high-risk group had a decreased overall survival (OS) (P=0.047). The risk score and stage were independent factors for predicting the OS of LUAD (P=0.0004 and P<0.0001, respectively). Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses based on the two groups showed that the DEGs were metabolically and hormonally related. The high-risk group showed a higher mutation rate and lesser immune cell infiltration, especially in TP53, KRAS, and MET. The expression level of PD-L1 and CTLA4 was high in the high-risk group (P<0.05). The high-risk group is more sensitive to anti-cancer therapy with lower IC50 and higher immunophenoscore (IPS).ConclusionsIn this study, we developed a novel LUAD stratification model based on m7G-related genes that successfully predicts the prognosis of LUAD patients and serves as a guide for clinically personalized treatment.

M1A and m7G modification-related genes are potential biomarkers for survival prognosis and for deciphering the tumor immune microenvironment in esophageal squamous cell carcinoma

“…Selection of m 1 A and m 7 G modi cation-related genes and acquisition of data from public repositories Figure S1 shows the study work ow. As reported in previously published researches, 7 m 1 A (ALKBH1, ALKBH3, FTO, TRMT10C, TRMT6, TRMT61A, YTHDF1, YTHDF2) and 16 m 7 G (AGO2, CYFIP1, EIF3D, EIF4E2, EIF4E3, EIF4G3, GEMIN5, IFIT5, LARP1, METTL1, NCBP1, NCBP2, NCBP3, NUDT11, SNUPN) regulator-mediated modi cation patterns (Supplementary Table 1) were identi ed for further analyses from published literature (22,23,24,25,26).…”

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

“…Similar mechanisms were demonstrated in breast cancers and bladder cancers 127 , 132 . However, it was reported in LUAD and cutaneous melanoma that neutrophils were less infiltrated in group of high m7G levels 56 , 133 . Except for neutrophils, further studies are needed in eosinophils and basophils, which are members of granulocytes as well.…”

“…Meanwhile, these patients showed increased infiltration of DCs as well as decreased activated T cells 115 . However, CRC, LUAD and cutaneous melanoma showed opposite results that m7G modification in high-risk patients could suppress DCs infiltrating 56 , 128 , 133 . These findings align with the double-edged functions of DCs in TIME.…”

An m1A/m6A/m7G/m5C regulator-mediated methylation modification pattern and Landscape of immune microenvironment infiltration characterization in Lower-Grade Glioma cohorts from three continents based on machine learning

“…In addition, the scRNA-seq data for GSE138794 were downloaded from the GEO database, keeping the LGG samples for GSM4119535, GSM4119536, and GSM4119538. All MRRs related to M1A, M6A, M7G, and M5C were identi ed from previous researches [26,27]. Finally, 64 genes were annotated in the meta-cohort (Supplementary File 1).…”