Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

Background: The long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) serves as a powerful predictor of tumor progression and overall survival in patients. Our previous studies showed that HOTAIR modulated HOXA1 DNA methylation by reducing DNMT1 and DNMT3b expression in drugresistant small cell lung cancer (SCLC). Moreover, H3 lysine 27 trimethylation (H3K27me3) is catalyzed by enhancer of zeste homolog 2 (EZH2) and plays a critical role in SCLC chemoresistance. However, it is not completely clear whether H3K27me3 affects HOXA1 DNA methylation or whether this effect is mediated by HOTAIR. Methods: The levels of EZH2 and H3K27me3 were identified in SCLC tissues by immunohistochemical (IHC) staining and in SCLC multidrug-resistant cells by Western blotting. Cell counting kit-8 (CCK-8) and flow cytometry were used to detect and analyze the biological function of H3K27me3. Then, we assessed the role of HOTAIR in the regulation of EZH2 and H3K27me3 by using lentivirus and small interfering RNA. Further, bisulfite sequencing PCR was conducted to detect the methylation levels of HOXA1 DNA. Finally, Western blotting was performed to examine the regulatory role of H3K27me3 in controlling HOTAIR expression in SCLC. Results: In this study, we found that EZH2 and H3K27me3 levels were markedly higher in SCLC tissues and multidrug-resistant SCLC cells. The results indicated that H3K27me3 was related to multidrug resistance. HOTAIR overexpression and knockdown showed that EZH2 and H3K27me3 were regulated by HOTAIR. Moreover, H3K27me3 affected HOXA1 DNA methylation levels. Strikingly, we found that H3K27me3 acted as a negative feedback regulator of HOTAIR. Conclusions: Our study showed that H3K27me3 affects HOXA1 DNA methylation via HOTAIR regulation, indicating that H3K27me3 may be a potential therapy target for SCLC chemoresistance.

Cuproptosis, a newly identified form of programmed cell death, plays vital roles in tumorigenesis. However, the interconnectivity of cuproptosis and ferroptosis is poorly understood. In our study, we explored genomic alterations in 1162 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) cohort to comprehensively evaluate the cuproptosis regulators. We systematically performed a pancancer genomic analysis by depicting the molecular correlations between the cuproptosis and ferroptosis regulators in 33 cancer types, indicating cross-talk between cuproptosis and ferroptosis regulators at the multiomic level. We successfully identified three distinct clusters based on cuproptosis and ferroptosis regulators, termed CuFeclusters, as well as the three distinct cuproptosis/ferroptosis gene subsets. The tumor microenvironment cell-infiltrating characteristics of three CuFeclusters were highly consistent with the three immune phenotypes of tumors. Furthermore, a CuFescore was constructed and validated to predict the cuproptosis/ferroptosis pathways in individuals and the response to chemotherapeutic drugs and immunotherapy. The CuFescore was significantly associated with the expression of miRNA and the regulation of post-transcription. Thus, our research established an applied scoring scheme, based on the regulators of cuproptosis/ferroptosis to identify LUAD patients who are candidates for immunotherapy and to predict patient sensitivity to chemotherapeutic drugs.