Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma.

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.

Prognostic value and immune landscapes of immunogenic cell death-related lncRNAs in hepatocellular carcinoma.

BACKGROUND: Both immunogenic cell death (ICD) and long noncoding RNAs (lncRNAs) are strongly associated with tumor development, but the mechanism of action of ICD-associated lncRNAs in hepatocellular carcinoma (HCC) remains unclear. METHODS: We collected data from 365 HCC patients from The Cancer Genome Atlas (TCGA) database. We formulated a prognostic signature of ICD-associated lncRNAs and a nomogram to predict prognosis. To explore the potential mechanisms and provide clinical guidance, survival analysis, enrichment analysis, tumor microenvironment analysis, tumor mutation burden (TMB), and drug sensitivity prediction were conducted based on the subgroups obtained from the risk score. RESULTS: A prognostic signature of seven ICD-associated lncRNAs was constructed. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients. The nomogram had a higher predictive value than the nomogram constructed without the risk model. Enrichment analysis confirmed that risk lncRNAs were closely associated with cell proliferation and mitosis. Most of the immune checkpoints currently used in therapy (e.g., PDCD1 and CTLA4) appeared to be elevated in high-risk patients. Tumor microenvironment analysis showed differential expression of lymphocytes (including natural killer cells, regulatory T cells, etc.) in the high-risk group. TMB had a higher incidence of mutations in the high-risk group (P=0.004). Chemotherapy drug sensitivity prediction provides effective guidelines for individual therapy. RT-qPCR of human HCC tissues verified the accuracy of the model. CONCLUSION: We constructed an effective prognostic signature for patients with HCC using seven ICD-lncRNAs, which provides guidance for the prognostic assessment and personalized treatment of patients.

Membrane metalloendopeptidase (MME) is positively correlated with systemic lupus erythematosus and may inhibit the occurrence of breast cancer.

BACKGROUND: Patients with systemic lupus erythematosus (SLE) have a lower risk of breast cancer (BRCA) than the general population. In this study, we explored the underlying molecular mechanism that is dysregulated in both diseases. METHODS: Weighted gene coexpression network analysis (WGCNA) was executed with the SLE and BRCA datasets from the Gene Expression Omnibus (GEO) website and identified the potential role of membrane metalloendopeptidase (MME) in both diseases. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of related proteins and miRNAs were performed to investigate the potential molecular pathways. RESULTS: WGCNA revealed that MME was positively related to SLE but negatively related to BRCA. In BRCA, MME expression was significantly decreased in tumor tissues, especially in luminal B and infiltrating ductal carcinoma subtypes. Receiver operating characteristic (ROC) analysis identified MME as a valuable diagnostic biomarker of BRCA, with an area under the curve (AUC) value equal to 0.984 (95% confidence interval = 0.976-0.992). KEGG enrichment analysis suggested that MME-related proteins and targeted miRNAs may reduce the incidence of BRCA in SLE patients via the PI3K/AKT/FOXO signaling pathway. Low MME expression was associated with favorable relapse-free survival (RFS) but no other clinical outcomes and may contribute to resistance to chemotherapy in BRCA, with an AUC equal to 0.527 (P value < 0.05). CONCLUSIONS: In summary, MME expression was significantly decreased in BRCA but positively correlated with SLE, and it might reduce the incidence of BRCA in SLE patients via the PI3K/AKT/FOXO signaling pathway.

Construction and validation of a model based on immunogenic cell death-associated lncRNAs to predict prognosis and direct therapy for kidney renal clear cell carcinoma.

BACKGROUND: Immunogenic cell death (ICD) is an important part of the antitumor effect, yet the role played by long noncoding RNAs (lncRNAs) remains unclear. We explored the value of ICD-related lncRNAs in tumor prognosis assessment in kidney renal clear cell carcinoma (KIRC) patients to provide a basis for answering the above questions. METHODS: Data on KIRC patients were obtained from The Cancer Genome Atlas (TCGA) database, prognostic markers were identified, and their accuracy was verified. An application-validated nomogram was developed based on this information. Furthermore, we performed enrichment analysis, tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis, and drug sensitivity prediction to explore the mechanism of action and clinical application value of the model. RT-qPCR was performed to detect the expression of lncRNAs. RESULTS: The risk assessment model constructed using eight ICD-related lncRNAs provided insight into patient prognoses. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients (p<0.001). The model had good predictive value for different clinical subgroups, and the nomogram constructed based on this model worked well (risk score AUC=0.765). Enrichment analysis revealed that mitochondrial function-related pathways were enriched in the low-risk group. The adverse prognosis of the higher-risk cohort might correspond to a higher TMB. The TME analysis revealed a higher resistance to immunotherapy in the increased-risk subgroup. Drug sensitivity analysis can guide the selection and application of antitumor drugs in different risk groups. CONCLUSIONS: This prognostic signature based on eight ICD-associated lncRNAs has significant implications for prognostic assessment and treatment selection in KIRC.

Effects of residue removal and tillage on greenhouse gas emissions in continuous corn systems as simulated with RZWQM2.

Agricultural production is a major source of carbon dioxide (CO2) and nitrous oxide (N2O) globally. The effects of conservation practices on soil CO2 and N2O emissions remain a high degree of uncertainty. In this study, soil CO2 and N2O emissions under different residue and tillage practices in an irrigated, continuous corn system, were investigated using the Root Zone Water Quality Model (RZWQM2). Combinations of no/high stover removal (NR and HR, respectively) and no-till/conventional tillage (NT and CT, respectively) field experiments were tested over the four crop-years (Apr. 2011-Apr. 2015). The model was calibrated using the NRCT, and validated with other treatments. The simulation results showed that soil volumetric water content (VWC) in the NR treatments (i.e., NRCT and NRNT) was 1.3%-1.9% higher than that in the HR treatments (i.e., HRCT and HRNT) averaged across the four years. A higher amount of CO2 and N2O emissions were simulated in the NRCT across the four years (annual average: 7034 kg C/ha/yr for CO2 and 3.8 kg N/ha/yr for N2O), and lower emissions were in the HRNT (annual average: 6329 kg C/ha/yr and 3.7 kg N/ha/yr for N2O). A long-term simulation (2001-2015) suggested that the CO2 and N2O emissions were closely correlated with the stover removal degree (SRD), tillage, VWC, soil temperature (ST), years in management (Y), and fertilizer application. Stover and tillage practices had cumulative effects on CO2 emissions. The simulated annual CO2 emissions in 1st year from NRCT, NRNT, and HRCT were 7.8%, 0.0%, and 7.7% higher than that from HRNT, respectively; then the emissions in 15th year were 63.6%, 47.7%, and 29.1% higher, respectively. Meanwhile, there were no cumulative effects on N2O emissions. The results also demonstrated that the RZWQM2 is a promising tool for evaluating the long-term effects of CO2 and N2O emissions on different conservation practices.