Construction of an immune-related lncRNA signature as a novel prognosis biomarker for LUAD

Abstract

The tumor immune microenvironment of lung cancer is associated with prognosis and immunotherapy efficacy. Long noncoding RNAs are identified as prognostic biomarkers associated with immune functions. We constructed a signature comprising differentially expressed immune-related lncRNAs to predict the prognosis of patients with lung adenocarcinoma. We established the immune-related lncRNA signature by pairing immune-related lncRNAs regardless of expression level and lung adenocarcinoma patients were divided into high- and low-risk groups. The prognosis of patients in the two groups was significantly different; The immune-related lncRNA signature could serve as an independent lung adenocarcinoma prognostic indicator. The signature correlated negatively with B cell, CD4+ T cell, M2 macrophage, neutrophil, and monocyte immune infiltration. Patients with low risk scores had a higher abundance of immune cells and stromal cells around the tumor. Gene set enrichment analysis showed that samples from low-risk group were more active in the IgA production in intestinal immune network and the T and B cell receptor signaling pathway. High-risk groups had significant involvement of the cell cycle, DNA replication, adherens junction, actin cytoskeleton regulation, pathways in cancer, and TGF-β signaling pathways. High risk scores correlated significantly negatively with high CTLA-4 and HAVCR2 expression and higher median inhibitory concentration of common anti-tumor chemotherapeutics (e.g., cisplatin, paclitaxel, gemcitabine) and targeted therapy (e.g., erlotinib and gefitinib). We identified a reliable immune-related lncRNA lung adenocarcinoma prognosis model, and the immune-related lncRNA signature showed promising clinical prediction value.

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

“… 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).…”

Construction of an algorithm based on oncosis‐related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

“…With the development of bioinformatics, utilizing mRNAs and lncRNAs to construct prognostic signatures for predicting the OS of cancer patients has become a hot topic of research. 23 We can precisely predict a 5‐year survival rate, the efficacy of immunotherapy, and the sensitivity to chemotherapy drugs of patients with malignant tumors by detecting the expression of a few genes or lncRNA, which provides great convenience for individualized clinical medication. In the present study, we divided the LUAD patients into different risk groups and different molecular subtypes based on the expression of orlncRNAs, which is a more accurate prediction than the traditional risk assessment model and is more in line with the philosophy of precision medicine.…”

“…Furthermore, the interaction between malignant tumors and oncosis has been investigated comprehensively in recent years. With the development of bioinformatics, utilizing mRNAs and lncRNAs to construct prognostic signatures for predicting the OS of cancer patients has become a hot topic of research 23 …”

Construction of an Algorithm Based on Oncosis-Related LncRNAs Comprising the Molecular Subtypes and a Risk Assessment Model in Lung Adenocarcinoma

“…Furthermore, the interaction between malignant tumors and oncosis has been investigated comprehensively in recent years. With the development of bioinformatics, utilizing mRNAs and lncRNAs to construct prognostic signatures for predicting the OS of cancer patients has become a hot topic of research 23 . We can precisely predict a 5year survival rate, the e cacy of immunotherapy, and the sensitivity to chemotherapy drugs of patients with malignant tumors by detecting the expression of a few genes or lncRNA, which provides great convenience for individualized clinical medication.…”