Genomic And Tumor Microenvironment Differences Between Cell Cycle Progression Pathway Altered/Non-Altered Patients With Lung Adenocarcinoma

Identification of the ferroptosis-related gene signature and the associated regulation axis in lung cancer and rheumatoid arthritis

Patients with Rheumatoid arthritis (RA) have an elevated risk of lung cancer compared to the healthy population. However, there are few studies on the relationship between RA and lung adenocarcinoma (LUAD), especially the mechanisms at the genetic level. In this study, we investigated the link between RA and LUAD regarding Ferroptosis-Related Genes. The RNA-seq data of RA (GSE77298 and GSE 82107) and LUAD(GSE75037) in the Gene Expression Omnibus (GEO) database were obtained. 259 ferroptosis-related genes were obtained from the website ( http://www.zhounan.org/ferrdb/ ).The differential genes obtained from the RA and LUAD datasets were intersected with ferroptosis-related genes to obtain the ferroptosis-related differentially expressed genes (FRDEGs). Next, the mRNA-miRNA network was constructed, then Gene Set Enrichment Analysis (GSEA) for target genes were performed. The CIBERSORT algorithm was used to analyze the immune infiltration. Finally, the results were validated using external datasets (GSE89408 and GSE48780) and The Cancer Genome Atlas (TCGA) dataset. We obtained FRDEGs common to LUAD and RA: FANCD2, HELLS, RRM2, G6PD, VLDLR. These five genes play important roles in the progression of RA and LUAD. They also hold great diagnostic value for both diseases. Also, we found that LUAD and RA share common signaling pathways and similar immune mechanisms.

New link between RNH1 and E2F1: regulates the development of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a non-small cell carcinoma. Ribonuclease/angiogenin inhibitor 1 (RNH1) exerts multiple roles in virous cancers. E2F1 is a critical transcription factor involved in the LUAD development. Here, we analyze the expression of RNH1 in LUAD patients, investigate the biological function of RNH1 in LUAD, and demonstrate its potential mechanisms through E2F1 in LUAD.

METHODS

In the present study, we presented the expression of RNH1 in LUAD based on the database and confirmed it by western blot detection of RNH1 in human LUAD tissues. Lentiviral infection was constructed to silence or overexpress RNH1 in NCI-H1395 and NCI-H1437 cells. We assess the role of RNH1 on proliferation in LUAD cells by MTT assay, colony formation assays, and cell cycle detection. Hoechst staining and flow cytometry were used to evaluate the effects of RNH1 on apoptosis of LUAD cells. The function of RNH1 in invasion and migration was investigated by Transwell assay. Dual luciferase assay, ChIP detection, and pull-down assay were conducted to explore the association of E2F1 in the maintenance of RNH1 expression and function. The regulation of E2F1 on the functions of RNH1 in LUAD cells was explored. Mouse experiments were performed to confirm the in-vivo role of RNH1 in LUAD. mRNA sequencing indicated that RNH1 overexpression altered the expression profile of LUAD cells.

RESULTS

RNH1 expression in LUAD tissues of patients was presented in this work. Importantly, RNH1 knockdown improved the proliferation, migration and invasion abilities of cells and RNH1 overexpression produced the opposite effects. Dual luciferase assay proved that E2F1 bound to the RNH1 promoter (-1064 ∼ -1054, -1514 ∼ -1504) to reduce the transcriptional activity of RNH1. ChIP assay indicated that E2F1 DNA was enriched at the RNH1 promoter (-1148 ∼ -943, -1628 ∼ -1423). Pull-down assays also showed the association between E2F1 and RNH1 promoter (-1148 ∼ -943). E2F1 overexpression contributed to the malignant behavior of LUAD cells, while RNH1 overexpression reversed it. High-throughput sequencing showed that RNH1 overexpression induced multiple genes expression changes, thereby modulating LUAD-related processes.

CONCLUSION

Our study demonstrates that binding of E2F1 to the RNH1 promoter may lead to inhibition of RNH1 expression and thus promoting the development of LUAD.

Development and validation of a MUC16 mutation-associated immune prognostic model for lung adenocarcinoma

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.