Comprehensive Analysis to Reveal Amino Acid Metabolism-Associated Genes as a Prognostic Index in Gastric Cancer.

Background: Amino acid metabolism (AAM) is related to tumor growth, prognosis, and therapeutic response. Tumor cells use more amino acids with less synthetic energy than normal cells for rapid proliferation. However, the possible significance of AAM-related genes in the tumor microenvironment (TME) is poorly understood. Methods: Gastric cancer (GC) patients were classified into molecular subtypes by consensus clustering analysis using AAMs genes. AAM pattern, transcriptional patterns, prognosis, and TME in distinct molecular subtypes were systematically investigated. AAM gene score was built by least absolute shrinkage and selection operator (Lasso) regression. Results: The study revealed that copy number variation (CNV) changes were prevalent in selected AAM-related genes, and most of these genes exhibited a high frequency of CNV deletion. Three molecular subtypes (clusters A, B, and C) were developed based on 99 AAM genes, which cluster B had better prognosis outcome. We developed a scoring system (AAM score) based on 4 AAM gene expressions to measure the AAM patterns of each patient. Importantly, we constructed a survival probability prediction nomogram. The AAM score was substantially associated with the index of cancer stem cells and sensitivity to chemotherapy intervention. Conclusion: Overall, we detected prognostic AAM features in GC patients, which may help define TME characteristics and explore more effective treatment approaches.

A novel algorithm for lung adenocarcinoma based on N6 methyladenosine-related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti-tumor therapies.

BACKGROUND: Lung cancer is a highly heterogeneous malignant tumor with high incidence and mortality. Recently, increasing evidence has demonstrated that N6-methyladenosine (m6A) methylation and the tumor microenvironment (TME) play important roles in the occurrence and development of lung adenocarcinoma (LUAD). METHODS: In this study, we constructed a novel and reliable algorithm based on m6A-related immune lncRNAs (mrilncRNAs), consisting of molecular subtypes and a prognostic signature. RESULTS: According to the analyses of molecular subtypes, patients in cluster 1 were in a more advanced stage, showed poor prognosis, were sensitive to immunotherapy (anti-programmed cell death 1 Ligand 1 (PD-L1) and anti-lymphocyte activating 3 (LAG-3)), and had a highest tumor mutational burden (TMB), while anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy seemed to be a good choice for patients in cluster 3. Subsequently, the results of the risk assessment model indicated that the low-risk patients exhibited a survival advantage, had an earlier stage, and showed a higher response to common anti-cancer drugs, including chemotherapy (Docetaxel, Paclitaxel), molecular targeted therapy (Erlotinib), and immunotherapy (anti-CTLA-4 therapy), while Gefitinib could be a good choice for patients with high-risk scores. CONCLUSION: In conclusion, the constructed algorithm exhibits promising practical prospects, and allows the selection of suitable and sensitive anti-cancer drugs, which could provide theoretical support to predict the survival outcomes of patients with LUAD.