Identification of an Autophagy-Related lncRNA Prognostic Signature and Related Tumor Immunity Research in Lung Adenocarcinoma

Abstract

Background: Autophagy is closely associated with the tumor immune microenvironment (TIME) and prognosis of patients with lung adenocarcinoma (LUAD). In the present study, we established a signature based on long noncoding RNAs (lncRNAs) related to autophagy (ARlncRNAs) to investigate the TIME and survival of LUAD patients.Methods: We selected ARlncRNAs associated with prognosis to construct a model, and divided each sample into different groups based on risk score. Subsequently, Kaplan-Meier survival analysis was performed to investigate the survival outcomes of LUAD patients in different group. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis explored the enriched pathways and functions. Several bioinformatics analyses were conducted to explore the TIME of LUAD patients in different group.Results: The ARlncRNAs signature could be recognized as an independent prognostic factor for LUAD patients, and patients in the low-risk group had a greater survival advantage. GO and KEGG enrichment analysis suggested that several immune functions and pathways were enriched in different groups. A high-risk score correlated significantly negatively with high abundance of immune cells and stromal cells around the tumor and high tumor mutational burden (TMB). Low-risk patients had a higher PD-1, CTLA-4 and HAVCR2 expression, and had a better efficacy of immune checkpoint inhibitor (ICIs), including PD-1/CTLA-4 inhibitor.Conclusions: A reliable signature based on ARlncRNAs was constructed to explore the TIME and prognosis of LUAD patients, which could provide valuable information for individualized LUAD treatment.

Silencing oncogene cell division cycle associated 5 induces apoptosis and G1 phase arrest of non‐small cell lung cancer cells via p53‐p21 signaling pathway

“…In recent years, the emergence of bioinformatic analyses has functioned as an effective and reliable pattern to study the complex molecular pathogenesis of NSCLC 29 . Increasing scholars have screened effective and reliable targets through bioinformatics analyses to predict the prognosis, explore the tumor immunity, and even select suitable anti‐tumor drugs of patients with tumors, which benefited greatly the patients with malignant tumors 30,31 …”

“…29 Increasing scholars have screened effective and reliable targets through bioinformatics analyses to predict the prognosis, explore the tumor immunity, and even select suitable anti-tumor drugs of patients with tumors, which benefited greatly the patients with malignant tumors. 30,31 CDCA5, acts as a cell cycle regulator, is combined with nuclear chromatin from S phase to metaphase and is subsequently released into the cytoplasm upon nuclear envelope breakdown. 9 Through bioinformatics analyses, we concluded that overexpression of CDCA5, an oncogene overexpressed in 18 common malignant tumors, was 19 It is worth mentioning that cyclin E is located downstream of the p53-p21 signaling pathway and regulates downstream cell cycle arrest (Figure 4).…”

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

“…In recent years, with the development of bioinformatic analyses, research has established reliable prognostic signatures based on mRNAs or lncRNAs to predict the prognosis of patients with various malignancies. 26,27 We were eager to build a new algorithm to predict the survival outcomes of patients with LUAD and provide valuable recommendations for the selection of appropriate anti-tumor drugs.…”

Bioinformatics algorithm for lung adenocarcinoma based on macropinocytosis-related long noncoding RNAs as a reliable indicator for predicting survival outcomes and selecting suitable anti-tumor drugs

“…The constructed prognostic signature was subjected to internal verification by dividing the patients with LUAD randomly into a training group and a testing group, which proved the reliability of the signature. Moreover, compared with the traditional prognostic signature that divided patients into 2 risk groups, [39] we divided the patients into 4 groups based on molecular subtypes and the prognostic signature, which could offer patients with LUAD more precise treatment. We included chemotherapy (Paclitaxel), targeted therapy (Erlotinib, Gefitinib), and immunotherapy (PD-1, PD-L1, CTLA-4, and HAVCR-2 therapy), which are currently recommended for patients with advanced lung cancer.…”