Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer

Abstract

BackgroundIncreasing evidence has demonstrated the functional relevance of long non-coding RNAs (lncRNAs) to immunity regulation and the tumor microenvironment in non-small cell lung cancer (NSCLC). However, tumor immune infiltration-associated lncRNAs and their value in improving clinical outcomes and immunotherapy remain largely unexplored.MethodsWe developed a computational approach to identify an lncRNA signature (TILSig) as an indicator of immune cell infiltration in patients with NSCLC through integrative analysis for lncRNA, immune and clinical profiles of 115 immune cell lines, 187 NSCLC cell lines and 1533 patients with NSCLC. Then the influence of the TILSig on the prognosis and immunotherapy in NSCLC was comprehensively investigated.ResultsComputational immune and lncRNA profiling analysis identified an lncRNA signature (TILSig) consisting of seven lncRNAs associated with tumor immune infiltration. The TILSig significantly stratified patients into the immune-cold group and immune-hot group in both training and validation cohorts. These immune-hot patients exhibit significantly improved survival outcome and greater immune cell infiltration compared with immune-cold patients. Multivariate analysis revealed that the TILSig is an independent predictive factor after adjusting for other clinical factors. Further analysis accounting for TILSig and immune checkpoint gene revealed that the TILSig has a discriminatory power in patients with similar expression levels of immune checkpoint genes and significantly prolonged survival was observed for patients with low TILSig and low immune checkpoint gene expression implying a better response to immune checkpoint inhibitor (ICI) immunotherapy.ConclusionsOur finding demonstrated the importance and value of lncRNAs in evaluating the immune infiltrate of the tumor and highlighted the potential of lncRNA coupled with specific immune checkpoint factors as predictive biomarkers of ICI response to enable a more precise selection of patients.

Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma

“…Traditional diagnosis methods, such as the biopsy, endoscopy, and chest X-ray and CT, were inadequate for early diagnosis, and there is the lack of specific and sensitive biomarkers for earlier detection of OSCC. Alteration in molecular profiles have been observed in various human cancers compared to corresponding normal tissues, and have been used as novel markers for cancer diagnosis and prognosis (Mehta et al, 2010;Jurgensmeier et al, 2014;Chen et al, 2018Chen et al, , 2019Sun et al, 2020). lncRNAs are recently emerging as a novel promising biomarker for cancer diagnosis and prognosis (Shi et al, 2016;Bolha et al, 2017).…”

Integrative modeling of multi-omics data for predicting tumor mutation burden in lung cancer patients

“…High TMB indicates the presence of more neoantigens in the tumor microenvironment, which promote the in ammatory response and result in the alteration of transcriptomic and epigenetic signature [47]. It has been proved that gene expression signatures in the tumor microenvironment were associated with the prognosis in NSCLC [48][49][50][51]. In agreement with previous studies, the differentially expressed genes between TMB-H and TMB-L patients identi ed in this study were found to enrich in the immune-related damaged DNA binding, nuclear division, nuclear chromosome segregation, organelle ssion, single-stranded DNA binding, ribonucleoprotein complex binding and pyrimidine metabolism (Additional le 2: Figure S2) [52][53][54][55][56].…”

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

“…Machine learning method is a good choice to uncover underlying patterns (Stephenson et al, 2019). It has been widely employed in bioinformatics (Cao et al, 2017;Bao et al, 2019;Conover et al, 2019;Moritz et al, 2019;Stephenson et al, 2019;Zou and Ma, 2019;Sun et al, 2020). The current work aims to develop a machine learning based method to diagnose HCC within-sample REOs.…”