Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m 6 A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m 6 A in LUAD patients is critical. In this study, m 6 A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m 6 Arelated lncRNA model. Kaplan-Meier analysis, principalcomponent analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m 6 A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m 6 Abased lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.

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Background As one of the most common malignancy, lung adenocarcinoma (LUAD) is characterized by low 5-year survival rate. This research aimed to investigate the effects of ribonucleotide reductase regulatory subunit M2 (RRM2) on malignant biological behaviors and activation of cGAS/STING pathway. We also explored the synergistic sensitization mechanisms of RRM2 and radiotherapy. Methods Bioinformatic tools were used to evaluate the clinical significance of RRM2 in LUAD patients. The roles of RRM2 in malignant phenotype and DNA damage in LUAD cells were investigated with cell proliferation, colony formation, immunofluorescence, modified Boyden chamber and comet assays. The mouse models were used to evaluate the biological significance of RRM2 in vivo. Cytotoxic T cell infiltration was evaluated via flow cytometric analysis and immunohistochemistry staining in C57BL/6 mice. We also explored the synergistic effects of RRM2 silencing and radiation on LUAD cells with apoptosis assay and immunoblotting in vitro. Results Bioinformatic analysis revealed that RRM2 had diagnostic values for LUAD patients. Higher levels of RRM2 predicted worse prognosis. RRM2 silencing inhibited LUAD cell proliferation, invasion and migration. RRM2 knockdown induced S phase arrest and DNA damage. RRM2 silencing induced cyclic GMP-AMP synthase (cGAS)/stimulator of interferon genes (STING) pathway, and the downstream targets were regulated in a STING-dependent manner. Knockdown of RRM2 suppressed tumor growth in the xenograft tumor models. RRM2 deficiency increased CD8 + T cells in the tumor tissues and spleens. Furthermore, RRM2 silencing had synergistic effects with radiation on inhibiting cell proliferation and promoting apoptosis. Meanwhile, this combination promoted the activation of cGAS/STING signaling pathway synergistically, and simultaneously increased expression of IFNβ, CCL5 and CXCL10. Conclusion Our results demonstrated that RRM2 silencing had anti-tumor values and activated the cGAS/STING signaling pathway. RRM2 silencing increased CD8 + T cells infiltration. RRM2 silencing cooperated with radiation to inhibit LUAD cell proliferation, promote apoptosis and enhance the activation of cGAS/STING signaling pathway. RRM2 could be a promising target for tumor regression through cancer immunotherapy in LUAD.

Background: The incidence of lung adenocarcinoma (LUAD) increased substantially in recent years. A systematic investigation of the metabolic genomics pattern is critical to improve the treatment and prognosis of LUAD. This study aimed to analyze the relationship between tumor microenvironment (TME) and metabolism-related genes of LUAD. Methods: The data was extracted from TCGA and GEO datasets. The metabolism-related gene expression profile and the corresponding clinical data of LUAD patients were then integrated. The survival-related genes were screened out using univariate COX regression and lasso regression analysis. The latent properties and molecular mechanisms of these LUAD-specific metabolism-related genes were investigated by computational biology. Results: A novel prognostic model was established based on 8 metabolism-related genes, including TYMS, ALDH2, PKM, GNPNAT1, LDHA, ENTPD2, NT5E, and MAOB. The immune infiltration of LUAD was also analyzed using CIBERSORT algorithms and TIMER database. In addition, the high-and low-risk groups exhibited distinct layout modes in the principal component analysis. Conclusions: In summary, our studies identified clinically significant metabolism-related genes, which were potential signature for LUAD diagnosis, monitoring, and prognosis.