Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients

ABSTRACT

Lung adenocarcinoma (LUAD) represents the main lung cancer (LC) subtype that possesses a disappointing clinical outcome over the decades. Tumor hypoxia is closely bound up with dismal survival for malignant tumor cases. We identified hypoxia-associated long non-coding RNA (lncRNA) signature to be an explicit indicator for predicting prognosis. The present work acquired RNA-seq and associated clinical data from The Cancer Genome Atlas (TCGA) database. Consensus cluster analysis characterized the hypoxia status of LUAD patients. Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) method determined significantly prognosis-related lncRNAs which were used to create a prognostic model. Diverse statistical approaches like the Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and nomogram were adopted to verify the accuracy of the risk score. The potential immune environment landscape was unearthed by the CIBERSORT algorithm. Three hypoxia-related clusters were determined and 221 differentially expressed hypoxia-related lncRNAs were screened out. We developed a new predictive model based on seven lncRNAs (LINC00941, AC022784.1, AC079949.2, LINC00707, AL161431.1, AC010980.2 and AC090001.1). Kaplan-Meier curves and ROC plots uncovered the reliable predictive power of the risk score model. In addition, the immunosuppressive landscape was presented in the high-risk group by immune cell infiltration analysis. The seven hypoxia lncRNAs survival signature in our article are robust, accurate tools for predicting overall survival in LUAD patients.

KEYWORDS:

• Hypoxia
• lncRNA
• prognosis
• lung adenocarcinoma
• TCGA
• signature

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Publicly available datasets were analyzed in this study. These data can be found here: TCGA (https://portal.gdc.cancer.gov/).

Authors contributions

Jun Shao and Boqing Zhang contributed equally to this work. Qingguo Li and Jun Shao conceived and designed the original study. Jun Shao and Boqing Zhang collected and analyzed the data. Jun Shao, Boqing Zhang, and Lin Kuai contributed to the interpretation of the data. Jun Shao and Boqing Zhang drafted the manuscript. Qingguo Li revised the manuscript. All authors saw and approved the final version of the manuscript.

Ethics approval and consent to participate

All data collection analyzed in studies involving human participants was per the ethical standards of the Cancer Genome Atlas Human Subjects Protection and Data Access Policies. This study was approved by the Institutional Review Board and the Ethics Committee of the Second Affiliated Hospital of NJMU, and complied with the principles of the Declaration of Helsinki (Approval Number: No. [2018] KY 118). In addition, written informed consent forms were signed by all the patients who participated in this research.

Supplementary material

Supplemental data for this article can be accessed here.