Computational identification of long non-coding RNAs associated with graphene therapy in glioblastoma multiforme

Combination of transcriptome and Mendelian inheritance reveals novel prognostic biomarker of CTLA-4-related lncRNAs and protective role of nitrogen metabolism pathway in lung adenocarcinoma development

OBJECTIVE

Since in the cancer setting, tumor cells may use cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to evade the immune system. This study aimed to identify CTLA-4-related long non-coding RNAs (lncRNAs) and assess their roles in lung adenocarcinoma (LUAD) development.

METHODS

Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA), MSigDB and Gene Weaver. CTLA-4-related lncRNA-based gene signatures (CTLA4LncSigs) were identified using Cox regression, establishing a risk score model and an independent prognostic model. Enrichment analysis (GO/KEGG) was performed. Mendelian randomization (MR) analysis investigated the nitrogen metabolism and lung cancer relationship, with Bayesian weighted MR (BWMR) addressing uncertainties. Correlations with tumor microenvironment and drug sensitivity were explored.

RESULTS

Nineteen CTLA4LncSigs significantly influenced LUAD prognosis. The risk score demonstrated independence as a prognostic factor. Functional analysis revealed lncRNAs' impact on nitrogen metabolism. MR and BWMR confirmed the protective role of the nitrogen metabolism pathway in lung cancer.

CONCLUSION

Our study identifies CTLA-4-related lncRNAs associated with LUAD prognosis and uncovers a previously undiscovered protective role of the nitrogen metabolism pathway in combating LUAD development, providing new insights into potential therapeutic targets and prognostic biomarkers for this aggressive cancer subtype.

Graphene therapy-related lncRNAs as prognostic and immune microenvironmental biomarkers in hepatocellular carcinoma

BACKGROUND

Graphene materials have the capacity to influence the tumor microenvironment and intracellular signaling responsiveness. However, the process of graphene-assisted liver cancer treatment still lacks specific biomarkers for assessing its efficacy.

METHODS

We identified graphene therapy-related lncRNAs (GTLncRNAs) through gene analysis and correlation tests. Multivariate COX and LASSO regression analyses yielded significant lncRNAs for a risk score model. We evaluated clinicopathological factors and tumor microenvironment using ssGSEA. We scrutinized the pathways of immune function, the evasion of tumor immunity, and the potential for immunotherapy. GTLncRNAs with differential expression were subjected to GO/KEGG analysis, and prospective chemotherapy drugs were discerned utilizing the pRRophetic algorithm. The prognostic model was authenticated through the examination of the Imvigor210 cohort, and an analysis of mRNA stemness was executed.

RESULTS

The researchers constructed a prognostic model based on 22 graphene therapy-related lncRNAs. Protective lncRNAs (AC010280.2, AL365361.1, and LINC01549) and negative lncRNAs (AC026412.3, AL031985.3, ELFN1-AS1, SNHG4, and EB2-AS1) were identified. Higher risk scores correlated with shorter survival. Low-risk immune pathways included Type_II_IFN_Reponse and cytolytic_activity. Subgroups differed significantly in TMB, TIDE, MDSC, exclusion, and dysfunction. Low TMB values correlated with longer survival. The high-risk subgroup showed increased sensitivity to screened compounds, and mRNAsi was higher in cancer tissue.

CONCLUSIONS

Our GTLncRNAs-based model accurately predicted survival of HCC patients and underscored the influence of graphene therapy-related genes on the tumor microenvironment. Potential treatment compounds were identified, and the mRNAsi index demonstrated prognostic value.