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Lung squamous cell carcinoma (LSCC) is one of the most common types of lung cancer in adults worldwide. With the development of modern medicine, cancer treatment that harnesses the power of the immune system might be particularly effective for treating LSCC. In this research, LSCC expression data, which quantify the cellular composition of immune cells, were analyzed by weighted gene coexpression network analysis (WGCNA) and a deconvolution algorithm based on the Gene Expression Omnibus (GEO) database, and the results indicated a close relationship between LSCC and CD8+ T cells. Six hub genes (SYT3, METTL8, HSPB3, GFM1, ERLIN2, and CLCN2) were verified by gene–gene network and protein–protein interaction (PPI) network analyses. We found that the six hub genes were increased in cancer tissues and were closely correlated with cancer development and progression. After immune correlation analysis, METTL8 was selected as a prognostic biomarker. Finally, we found that the METTL8 levels were increased in multiple lung cancer cell lines and LSCC tissues. METTL8 inhibition could clearly induce G1 cell cycle arrest and suppress proliferation. Therefore, METTL8, which is related to CD8+ T cell infiltration, might be identified as a potential biomarker and gene therapy target in LSCC.

In order to study the mechanism of abnormal deoxyribonucleic acid methylation in the process of lung cancer in rats, Wistar rats are used as the research object. The lung cancer model of rats is established by bronchial perfusion of carcinogens. Hematoxylin-eosin staining is used to observe the pathological changes of lung tissue in rats. The overall methylation level of genome is analyzed by immunohistochemistry, and the methylation status is detected by methylation specific polymerase chain reaction. The results show that the nuclei of normal bronchial epithelial cells are brown and yellow uder optical microscope. With the development of carcinogenesis, the staining gradually fades to light yellow in the infiltrating nuclei of cancer cells. Compared with normal bronchial epithelial cells, the optical density and integrated optical density values of 5-mC immunohistochemistry decrease. The methylation frequency of p16 gene in atypical hyperplasia, carcinoma in situ and invasive cancer tissues is significantly higher than that in normal and proliferative tissues. The methylation frequency of p57 gene is significantly higher in carcinoma in situ and invasive tissues than in normal and proliferative tissues. Abnormal methylation-mediated down-regulation of p16 and p57 gene transcription may be an important molecular biological event in carcinogenesis of lung cancer rats induced by carcinogens. There are still some shortcomings in the research process, but the results of the study still provide some reference and guidance for exploring more precise mechanism in the future. Therefore, this study is a significant research topic.

Background: Origin recognition complex (ORC) 1, ORC2, ORC3, ORC4, ORC5 and ORC6, form a replication-initiator complex to mediate DNA replication, which play a key role in carcinogenesis, while their role in lung adenocarcinomas (LUAD) remains poorly understood.Methods: We confirmed the transcriptional and post-transcriptional levels, DNA alteration, DNA methylation, miRNA network, protein structure, PPI network, functional enrichment, immune infiltration and prognostic value of ORCs in LUAD based on Oncomine, GEPIA, HPA, cBioportal, TCGA, GeneMANIA, Metascape, KM-plot, GENT2, and TIMER database. Results: ORC mRNA and protein were both enhanced obviously based on Oncomine, Ualcan, GEPIA, TCGA and HPA database. Furthermore, ORC1 and ORC6 have significant prognostic values for LUAD patients based on GEPIA database. Protein structure, PPI network, functional enrichment and immune infiltration analysis indicated that ORC complex cooperatively accelerate the LUAD development by promoting DNA replication, cellular senescence and metabolic process. Conclusion: the ORC complex has an important prognostic and expression significance for LUAD patients.

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